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Cardiopulmonary Exercise Testing

Pulmonary Medicine

Guest Editors: Luke Howard, Michael P. W. Grocott, Robert Naeije, Ron Oudiz, and Roland Wensel

Pulmonary Medicine


Guest Editors: Luke Howard, Michael P. W. Grocott,Robert Naeije, Ron Oudiz, and Roland Wensel

Copyright © 2012 Hindawi Publishing Corporation. All rights reserved.

This is a special issue published in “Pulmonary Medicine.” All articles are open access articles distributed under the Creative CommonsAttribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is prop-erly cited.

Editorial Board

N. Ambrosino, ItalyMichel Aubier, FranceA. Azuma, JapanM. Safwan Badr, USALeif Bjermer, SwedenDemosthenes Bouros, GreeceDina Brooks, CanadaAndrew Bush, UKDenis Caillaud, FranceStefano Centanni, ItalyPascal O. Chanez, FranceEdwin Chilvers, UKKazuo Chin, JapanBruno Crestani, FranceRoberto Walter Dal Negro, ItalyJean-Charles Dalphin, FranceP. Dekhuijzen, The Netherlands

Burton F. Dickey, USAE. Duiverman, The NetherlandsJim Egan, IrelandArmin Ernst, USAR. Farre, SpainDimitris Georgopoulos, GreeceJorrit Gerritsen, The NetherlandsNicole S. L. Goh, AustraliaHartmut Grasemann, CanadaAndrew Greening, UKAndrew J. Halayko, CanadaFelix Herth, GermanyAldo T. Iacono, USAS. L. Johnston, UKMarc A. Judson, USARomain Kessler, FranceKazuyoshi Kuwano, Japan

Joseph P. Lynch, USAJudith C. W. Mak, Hong KongHisako Matsumoto, JapanLuisetti Maurizio, ItalyM. S. Niederman, USAAkio Niimi, JapanT. Penzel, GermanyMilos Pesek, Czech RepublicIrwin Reiss, GermanyLuca Richeldi, ItalyAndrew Sandford, CanadaCharlie Strange, USAE. R. Swenson, USAJun Tamaoki, JapanJeremy P. T. Ward, UKEmiel F. M. Wouters, The Netherlands

Contents

Cardiopulmonary Exercise Testing, Luke Howard, Michael P. W. Grocott, Robert Naeije, Ron Oudiz,and Roland WenselVolume 2012, Article ID 564134, 3 pages

The Impact of Pulmonary Arterial Pressure on Exercise Capacity in Mild-to-Moderate Cystic Fibrosis: ACase Control Study, Katerina Manika, Georgia G. Pitsiou, Afroditi K. Boutou, Vassilis Tsaoussis,Nikolaos Chavouzis, Marina Antoniou, Maria Fotoulaki, Ioannis Stanopoulos, and Ioannis KioumisVolume 2012, Article ID 252345, 6 pages

Predicted Aerobic Capacity of Asthmatic Children: A Research Study from Clinical Origin, Lene LochteVolume 2012, Article ID 854652, 9 pages

Test-Retest Reliability and Physiological Responses Associated with the Steep Ramp Anaerobic Test inPatients with COPD, Robyn L. Chura, Darcy D. Marciniuk, Ron Clemens, and Scotty J. ButcherVolume 2012, Article ID 653831, 6 pages

Cardiopulmonary Exercise Testing in Lung Transplantation: A Review,Katherine A. Dudley and Souheil El-ChemalyVolume 2012, Article ID 237852, 7 pages

Validity of Reporting Oxygen Uptake Efficiency Slope from Submaximal Exercise Using RespiratoryExchange Ratio as Secondary Criterion, Wilby Williamson, Jonathan Fuld, Kate Westgate, Karl Sylvester,Ulf Ekelund, and Soren BrageVolume 2012, Article ID 874020, 8 pages

Abnormalities of the Ventilatory Equivalent for Carbon Dioxide in Patients with Chronic Heart Failure,Lee Ingle, Rebecca Sloan, Sean Carroll, Kevin Goode, John G. Cleland, and Andrew L. ClarkVolume 2012, Article ID 589164, 6 pages

Hindawi Publishing CorporationPulmonary MedicineVolume 2012, Article ID 564134, 3 pagesdoi:10.1155/2012/564134

Editorial


Luke Howard,1 Michael P. W. Grocott,2 Robert Naeije,3 Ron Oudiz,4 and Roland Wensel1

1 Imperial College School of Medicine, Imperial College London, London W12 0NN, UK2 Centre for Human Integrative Physiology, University of Southampton, Southampton, UK3 Erasme Academic Hospital, Free University of Brussels, Brussels, Belgium4 Los Angeles Biomedical Research Institute at Harbor, UCLA Medical Center, Torrance, CA, USA

Correspondence should be addressed to Luke Howard, [email protected]

Received 16 December 2012; Accepted 16 December 2012

Copyright © 2012 Luke Howard et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Cardiopulmonary exercise testing (CPET) is important forthe differential diagnosis of dyspnoea-fatigue syndromes.The test more typically includes measurements of ventilation(VE), carbon dioxide output (VCO2), and oxygen uptake(VO2) at a progressively increased workload (W) until amaximum VO2, called VO2max or VO2peak to define aerobicexercise capacity, but steady state evaluations have utility insome contexts. The difference in VO2max and VO2peak cansometimes be made by the identification or not of a VO2

plateau while workload may be still increasing. However, theinformation content of VO2max and VO2peak is essentially thesame, provided other criteria of maximum exercise are met,with, importantly, a respiratory exchange ratio (RER)≥1.1.Ventilatory reserve may be calculated at VO2peak. This is thedifference between maximum voluntary ventilation (MVV)and VEpeak, with MVV either directly measured, or predictedfrom the forced expiratory volume in 1 sec (FEV1) times 35or 40. The ventilatory reserve normally ranges from 20 to60 L/min, with an extreme lower limit of normal of 11 L/min,although caution should be applied to this measure giventhe unreliability of calculated MVV. Other relevant CPETmeasurements are VO2 at the anaerobic threshold (VO2AT)(RER= 1), VE/VCO2 either as a slope over the entireCPET or, preferably, at the AT, maximum heart rate andrecovery, and the VO2-work rate relationship (ΔVO2/ΔW).Indeed VE/VCO2 slope has been shown to be a powerfulprognostic indicator in heart failure independent of peakVO2.

Cardiac limitation of aerobic exercise capacity is char-acterized by decreased VO2AT, ΔVO2/ΔW and increasedVE/VCO2-slope, when heart failure is present. In thesepatients, resting heart rate is increased, but maximum heartrate and heart rate recovery are decreased (the latter to

<12 beats per min). A ventilatory limitation to exercisecapacity is characterized by a low ventilatory reserve, whichis often, but not always, attained with RER< 1. The CPETprofile of severe deconditioning resembles that of heartfailure, but with VO2max usually >20 mL/kg except in theelderly and an unremarkable VE/VCO2. The identificationof “peripheral factors” is more difficult. Mitochondrialdisorders are uncommon causes of early lactic acidosis withvery low VO2max and VO2AT—along with a hyperdynamiccardiovascular state. Muscle mass in patients with chronicdisease-associated cachexia may become insufficient for amaximal challenge of the cardiovascular system, which isnormally achieved with approximately half of the skele-tal muscle mass (legs on the bicycle). Peripheral oxygenextraction in heart failure has been shown to be preserved.Therefore, in these patients VO2max is essentially maximalcardiac output (Q-) dependent, as indicated by the Fickequation:

VO2 max = Qmax(CaO2 − CvO2), (1)

where CvO2 becomes a constant, and thus VO2max deter-mined by maximum O2 delivery or Q × CaO2.

Many studies have reported on marked histological andbiological changes in skeletal muscle of patients with chronicdiseases, including heart failure, but measurements of max-imum O2 extraction are few. Skeletal muscle deteriorationin cardiac or lung disease patients may be most relevantto muscle strength or power, which is more appropriatelymeasured by anaerobic exercise capacity tests.

A simple, inexpensive, and safe surrogate of VO2max is thedistance walked in 6 minutes. The 6-min walk test (6MWT)derives from the linear relationships between VO2, Q orworkload,—or speed. Like VO2max, the 6-min walk distance

2 Pulmonary Medicine

is also decreased by decreases in CaO2 as a cause of decreasedoxygen delivery and breathing reserve. The test is limitedin the less severely impaired patients by a ceiling effect,corresponding to the maximum possible speed of walking inpatients still be able to further increase VO2—but for thatpurpose would have to switch to walk on a slope or running.Demographic factors such as age, height and weight shouldalso be taken in to consideration of the distance walked. The6MWT is very useful to evaluate the functional impairmentof patients, less so for the differential diagnosis of dyspnoea-fatigue.

In the present issue of Pulmonary Medicine, these basicexercise physiology principles are further explored by niceseries of clinical studies on patients with a variety of cardiacand pulmonary diseases.

In the study entitled “Abnormalities of the ventilatoryequivalent for carbon dioxide in patients with chronic heartfailure” L. Ingle et al. investigated the kinetics of theVE/VCO2 ratio during an incremental exercise testing follow-ing a modified Bruce protocol in a large series of 423 patientswith chronic heart failure compared to 78 healthy controls.They showed that the nadir of the VE/VCO2 in patients withheart failure occurred earlier, and that this was of prognosticrelevance. These results add to previously known prognosticvalue of increased VE/VCO2-slope in heart failure. Theauthors’ interpretation of the findings is that a shorter time toVE/VCO2nadir reflects earlier onset of the non-CO2 stimulusto ventilation in patients with chronic heart failure. This ispossible. However, the time to VE/VCO2nadir was decreasedin proportion to the decreased exercise duration, VO2ATor VO2peak, all at around 60% of measured in the controls,indicating an important intrinsic contribution of aerobicexercise capacity, known as another determinant of survivalin heart failure.

The study by R. L. Chura et al. “Test-retest reliabilityand physiological responses associated with the steep rampanaerobic test in patients with COPD” provides robust dataon the reproducibility of a very steep incremental exerciseprotocol (25 W per 10 s) in 11 COPD patients. The SteepRamp Anaerobic test (SRAT) aimes at a measurement ofanaerobic exercise capacity, and, as such, was comparedby the authors to the more classically known 30s-WingateAnaerobic Test (WAT). The patients also performed a CPET.Both SRAT and WAT showed a good reproducibility at 1-2 days intervals. Maximum power output was 157 W onthe SRAT, compared to 231 W on the WAT and 66 W onthe CPET, but metabolic and ventilatory responses weresimilar. The FEV1 of the patients was on average of 1 L,allowing to estimate a MVV of 40 L/min. The VEmax was onaverage 40 L/min during the three exercise tests, indicatingthat the three tests exhaused the ventilatory reserve of thepatients. Because the peak work output was the highest atthe maximum achieved VO2 with the SRAT, the authorsconcluded about the superiority of this test over the WATto determine anaerobic exercise capacity. This makes a lot ofsense. Furthermore, the higher power output with the SRATcompared to the WAT at similar VO2 probably reveals morespecific measurement of purely alactic anaerobic exercisecapacity. This important study will hopefully draw attention

to the importance of anaerobic exercise testing in theevaluation of patients with cardiac and pulmonary diseases.

In the article “Cardiopulmonary exercise testing in lungtransplantation: a review” K. A. Dudley and S. El-Chemalyreview published data on the role of CPET in the assessmentof appropriateness of listing for lung transplantation as wellof posttransplant outcome in patients with various forms oflung disease. The lung allocation score does not currentlyinclude CPET variables, but exercise capacity solely deter-mined by a 6MWT. The review confirmed the previouslyknown prognostic relevance of the 6MWT for patients await-ing transplantation. In these patients, the 6MWT was supe-rior to spirometry in the prediction of 6-month mortality.However, how pretransplant 6MWT relates to posttransplantfunctional state and prognosis remains unclear. The reasonwhy CPET variables are currently not components of thelung allocation score is in the insufficient evidence derivedfrom limited size studies evaluating only specific respiratorypathologies and type of transplant, thus with findings thatare difficult to extrtapolate to other patients waiting fortransplantation. Data from posttransplant patients revealsome functional improvement with, however, still significantreduction in exercise tolerance. This does not appear tobe limited by the pulmonary function, therefore pointingtowards peripheral mechanisms of reduced functional capac-ity. Despite the need for more systematic studies in this field,the authors conclude that, based on the existing data, CPETcarries prognostic information beyond that of pulmonaryfunction tests and 6-minute-walk distance suggesting a roleof CPET in pretransplant and possibly also posttransplantassessment.

The study “Predicted aerobic exercise capacity of asthmaticchildren: a research study from clinical origin” by L. Lochtecompared longitudinally the predicted aerobic exercisecapacity based on submaximal exercise testing in 28 asth-matic children compared to 28 controls during 10 months.There were also physical activity and asthma questionnaires.The predicted aerobic exercise capacity test consisted in5 min continuous running on a tredmill at a running speedadjusted to maintain a stable submaximal heart rate of170–180 bpm. The estimation of VO2max was based onpreviously reported weight-adjusted VO2, running speedand heart rate, and VO2 extrapolated from the differencebetween maximum and submaximum heart rates. This is anadaptation of the Astrand test, based on the same rationalethat any further increase in VO2 (or Q) at a submaximumlevel of exercise is exclusively heart rate determined. Physicalactivity as evaluated by questionnaires was well correlatedto estimated VO2max—and age. The results showed lowerpredicted aerobic exercise capacity in asthmatic children,without this being related to asthma severity or exerciseinduced asthma, but there was improvement over time. Thereasons for decreased fitness in young asthmatics are unclear,but it might be deconditioning rather than insufficientasthma control. The study underscores the importance ofmonitoring physical activity in addition to lung function inchildren with asthma.

The study “The impact of pulmonary arterial pressureon exercise capacity in mild-to-moderate cystic fibrosis: a case

Pulmonary Medicine 3

control study” by K. Manika et al. explored the impactof systolic pulmonary artery pressure (PASP) measuredby echocardiography before and immediately after testingon exercise capacity in 17 patients with mild-to-moderatecystic fibrosis without pulmonary hypertension and in 10controls. The patients hads higher postexercise PASP andlower VO2peak, VO2AT, and peak O2 pulse than the controls.The change in PASP was strongly correlated to parametersof exercise capacity in the patients, not in the controls. Theauthors conclude that pulmonary vascular disease mightcontribute to decreased exercise capacity in patients withcystic fibrosis. It may be noted that the postexercise PASPwere not higher than normal, and the difference betweenpatients and controls, though significant, was of a fewmmHg, thus in the range of the error of measurement.However, the patients had an average ventilatory reserve ofaround 40 L/min at maximum exercise, the maximum RERwas of 1.19, not different from the controls and the patientswith the highest PASP also had a higher VE/VCO2. It is thuspossible that pulmonary vascular disease contributes to thelimitation of exercise capacity in patients with cystic fibrosis.Further studies on a larger number of patients with morecomprehensive measurements of the pulmonary circulationwill be needed to prove it.

The study “Validity of reporting oxygen uptake efficiencyslope from submaximal exercise using respiratory exchangeratio as secondary criterion” by W. Williamson et al. inves-tigated the VO2 efficiency slope, or rate of change in VO2

per rate of change in VE, in 100 healthy volunteers during aramped treadmill protocol from data truncated to RER levelsfrom 0.85 to 1.2. The slope increased significantly from low-to-moderate intensity exercise and was highest at a RER of 1,decreasing at higher values. The O2 efficiency slope has beenpreviously reported to be a valid and reproducible marker offunction and prognosis as VO2peak. The present results showthat VO2 slope is not constant during an incremental CPETand probably optimally measured at the RER of 1. How thistighter definition of the VO2 slope compares with VO2AT,VE/VCO2, ΔVO2/W , and chronotropic incompetence in theevaluation of patients with cardiac or pulmonary diseaseswill have to be investigated in further studies.

In summary, for those of us interested in a better under-standing of the dyspnoea-fatigue symptoms of our patients,CPET is a remarkable tool. It generates lots of variablesthat need integration into pathophysiological reasoning andpretest clinical probability assessments. More work is neededfor the integration of the CPET variables into validateddecision trees and recommendations. This is an exciting area,still open for a lot more investigation and progress. The seriespapers published in this issue of Pulmonary Medicine arealtogether an important step in the good direction.

Luke HowardMichael P. W. Grocott

Robert NaeijeRon Oudiz

Roland Wensel


Clinical Study

The Impact of Pulmonary Arterial Pressure onExercise Capacity in Mild-to-Moderate Cystic Fibrosis:A Case Control Study

Katerina Manika,1 Georgia G. Pitsiou,2 Afroditi K. Boutou,2 Vassilis Tsaoussis,1

Nikolaos Chavouzis,2 Marina Antoniou,1 Maria Fotoulaki,3

Ioannis Stanopoulos,2 and Ioannis Kioumis1

1 Pulmonary Department, Aristotle University of Thessaloniki, “G. Papanikolaou” General Hospital, Exohi, 57010 Thessaloniki, Greece2 Respiratory Failure Unit, Aristotle University of Thessaloniki, “G. Papanikolaou” General Hospital, Exohi, 57010 Thessaloniki, Greece3 4th Department of Pediatrics, Aristotle University of Thessaloniki, “Papageorgiou” General Hospital, 56429 Thessaloniki, Greece

Correspondence should be addressed to Georgia G. Pitsiou, [email protected]

Received 30 November 2011; Accepted 10 June 2012

Academic Editor: Robert Naeije

Copyright © 2012 Katerina Manika et al. This is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properlycited.

Background. Pulmonary hypertension (PH) is an often complication of severe cystic fibrosis (CF); however, data on the presenceand impact of pulmonary vasculopathy in adult CF patients with milder disease, is very limited. Aim. To investigate, for the firsttime, the impact of systolic pulmonary arterial pressure (PASP) on maximal exercise capacity in adults with mild-to-moderatecystic fibrosis, without PH at rest. Methods. This is a Case Control study. Seventeen adults with mild-to-moderate CF, withoutPH at rest (cases) and 10 healthy, nonsmoking, age, and height matched controls were studied. All subjects underwent maximalcardiopulmonary exercise testing and echocardiography before and within 1 minute after stopping exercise. Results. Exerciseventilation parameters were similar in the two groups; however, cases, compared to controls, had higher postexercise PASP anddecreased exercise capacity, established with lower peak work rate, peak O2 uptake, anaerobic threshold, and peak O2 pulse.Furthermore, the change in PASP values before and after exercise was strongly correlated to the parameters of exercise capacityamong cases but not among controls. Conclusions. CF adults with mild-to-moderate disease should be screened for the presenceof pulmonary vasculopathy, since the elevation of PASP during exercise might contribute to impaired exercise capacity.

1. Introduction

Exercise impairment in cystic fibrosis (CF) is well establishedand a variety of determinants, such as pulmonary andnutritional factors, muscle dysfunction and deconditioning,have been studied in this direction [1–4]. It seems that thefactors which are limiting exercise tend to vary across diseasestages; ventilatory impairment is probably the major factorlimiting exercise in severe disease, while nonpulmonaryfactors seem to be related to reduced exercise capacity in mildand moderate disease [4].

Pulmonary hypertension (PH), which is a commondeterminant of exercise capacity in patients with respiratory

disorders [5], is an often complication of CF. PH is observedin 20–65% of adult CF patients with severe disease [6–10], and it has been associated with increased mortality[6, 11]. However, data on its frequency and impact amongpatients with milder disease are limited. Although adult CFpatients with mild-to-moderate disease achieve maximumexercise without generally reaching ventilatory limitation [4],the potential effect of pulmonary vasculopathy on exercisecapacity, in this patient population, has not yet been clarified.

In this study we hypothesized that pulmonary vasculardisease contributes to the exercise intolerance of adult CFpatients with mild-to-moderate disease, without PH at rest.Under this scope we conducted a case-control study, utilizing


maximal cardiopulmonary exercise testing (CPET), in orderto investigate, for the first time in literature, the impact ofpost exercise PASP on this patient population.

2. Methods

2.1. Subjects. The study followed a case-control design.Seventeen adult CF patients with mild-to-moderate diseaseconstituted the group of cases and 10 healthy nonsmokingvolunteers matched for age and height, constituted the groupof controls. All CF patients were regularly attending theoutpatient CF clinic of “G. Papanikolaou” Hospital, werein stable clinical condition and were life-long nonsmokers.Mild pulmonary disease was defined by the presence ofFEV1 >65% of predicted, and moderate disease by thepresence of ≤40% FEV1 ≤65% of predicted [6]. Patientswho presented with an exacerbation of the disease, that isincreased sputum production, purulence, or dyspnea withor without systemic symptoms, combined with a 10% ormore reduction in patient’s forced expiratory volume in 1second (FEV1) usual value, were excluded from the study.Patients who were hospitalized or required per os antibioticsduring the last 2 months prior to the study, receiveddomiciliary oxygen therapy, or presented with any othercondition affecting exercise capacity, were also excluded.Ethical approval for the study protocol was received fromthe “G. Papanikolaou” Hospital Scientific Committee andinformed consent was provided by all participants.

2.2. Study Protocol. During the baseline visit, all CF patientswere clinically assessed by the same physician, who docu-mented their Schwachmann score (SS). An arterial bloodsample was obtained for both cases and controls, while allparticipants underwent pulmonary function testing. Forcedvital capacity (FVC) and FEV1 were measured utilizing anelectronic spirometer (Wright ventilometer, Clement ClarkeInternational Ltd., England), according to the AmericanThoracic Society recommendations [12].

All participants visited again the CF clinic within oneweek and underwent a complete transthoracic echocardio-graphic study, including 2D, pulsed and continuous-waveDoppler and colour flow imaging using a HD7 cardiac ultra-sound system (Philips medical system, Andover, MA, USA).Standard two-dimensional (2D) and colour flow Dopplerimages were obtained using the parasternal long and shortaxis and apical views. PASP was estimated by calculatingthe maximal velocity of the tricuspid regurgitant jet and byfurther using the Bernoulli equation and then adding to thisvalue an estimated right atrial pressure based on both thesize of the inferior vena cava and the change in diameterof this vessel during respiration. PH was defined as a PASP>35 mmHg at rest [13]. Left ventricular dimensions, ejectionfraction, and cardiac index were obtained by previouslyrecommended techniques [14].

A maximal exercise capacity test was then performedon a cycle ergometer (medical graphics), under continuousmonitoring of heart rate (HR), oxygen saturation (SpO2),and a 12-lead electrocardiogram, while blood pressure (BP)

measurements were obtained every two minutes using astandard-cuff mercury sphygmomanometer. A ramp proto-col was used with an incremental rate of 20 Watts·min−1

for controls and of 10–20 Watts·min−1 for cases accordingto disease severity and estimated fitness, aiming for the testto last approximately 10–12 minutes [15]. Each test waspreceded by 3 min of resting to enable subjects to achievesteady state conditions for HR, SpO2, BP, and gas exchangevariables, and of 2 min of unloaded cycling. Patients andcontrols were asked to score their sense of dyspnea andmuscle fatigue using Borg scale, every 2 minutes during thetest.

Gas exchange values and exercise parameters were col-lected breath-by-breath and computer-averaged over 10-second intervals. Anaerobic threshold was calculated bythe V slope method, as previously described [15]. Thefollowing exercise parameters were recorded: maximal workrate (WR peak), peak oxygen uptake (VO2 peak), oxygenuptake at anaerobic threshold (AT), peak oxygen pulse (VO2

peak/HR), ventilatory equivalent for carbon dioxide at AT(VE/VCO2), maximal ventilation VEmax, peak heart rate(HR), maximum minute ventilation to maximum voluntaryventilation ratio (VEmax/MVV), and breathing reserve (BR).The MVV was calculated as 40 × FEV1 and the breathingreserve as MVV-VEmax. Ventilatory limitation was definedas (VEmax/MVV) × 100 > 85% or BR <11 lit [15, 16].

Within 1 minute after the completion of the exercisetesting, a second echocardiogram was performed by the sameinvestigator, following the same protocol and focusing onpostexercise tricuspid regurgitation velocity.

2.3. Data Analysis. Data analysis was conducted using theStatistical Package for Social Sciences (SPSS) for Windows2000XP, release 17.0. Normal predicted values for CPETparameters were calculated using standard equations [15,16]. The Shapiro-Wilk test of normality was used to assessthe normal or not distribution of data. Student’s t-test forindependent samples was used to compare CPET parametersbetween cases and controls, while the Mann-Whitney theU-test was applied to compare CPET parameters betweenCF patients with low (≤35 mmHg) and high (>35 mmHg)postexercise PASP. ΔSPAP was calculated as follows: PASPafter exercise − PASP at rest. Pearson correlation coefficient(r) was used to assess potential correlations between ΔPASPand CPET parameters in cases and controls. A P value <0.05was considered significant.

3. Results

Summary characteristics of cases and controls are shown inTables 1 and 2. During rest, none of the CF patients sufferedfrom PH and no difference was noted in PASP or any otherechocardiographic measurement between the two groups.However, PASP immediately after exercise was significantlyhigher in the group of cases compared to controls (31.5versus 25.8, P = 0.041).

Both cases and controls stopped exercise because offatigue. At the end of exercise mean Borg scale for fatigue


Table 1: Demographic characteristics, pulmonary function vari-ables and pulmonary artery pressure measurements in cases andcontrols.

Cases Controls P value

Number (M/F) 17 (11/6) 10 (7/3)

Age (years) 23.9± 3.5 26.8± 3.1 NS

BMI (kg/m2) 21.3± 3.0 24.9± 4.1 NS

SS 72.3± 13.9 NA

FEV1 (% predicted) 66.3± 24.3 99.1± 5.4 0.004

FVC (% predicted) 78.4± 18.6 98.5± 6.1 0.007

pO2 rest (mmHg) 75.7± 9.2 93.4± 9.2 0.008

SpO2 rest (%) 95.4± 1.9 97.6± 1.4 NS

PASP rest (mmHg) 27.5± 6.4 24.6± 2.2 NS

PASP post ex. (mmHg) 31.5± 7.3 25.8± 1.3 0.041

ΔPASP (mmHg) 4.0± 3.1 1.2± 1.3 0.048

Data are presented as mean± 1 standard deviation. NS: not significant, BMI:body mass index, SS: Schwachmann score, NA: not applicable, rest: at rest,post ex.: post exercise, FEV1: forced expiratory volume in 1 second, FVC:forced vital capacity, pO2: arterial oxygen partial pressure, SpO2: oxygensaturation, PASP: right ventricular systolic pressure, ΔPASP: PASP post-PASP rest.

was approximately 8 and mean Borg scale for dyspnea was 6for both groups. Respiratory exchange ratio (RER) at peakexercise was >1.1 for all subjects. During maximal CPET,CF patients presented limited exercise capacity, comparedto controls, as established by lower WR, VO2 peak, AT, andoxygen pulse (Table 2). However, no participant presentedwith respiratory limitation. Although the absolute value ofMVV-VEmax was higher among cases compared to controls,VEmax/MVV% predicted did not differ between the twogroups and even though BR was significantly lower amongcases (Table 2), it was higher than 11 liters in all participants.Oxygen saturation at peak exercise was also similar betweenthe two groups (Table 2).

After exercise, 5 cases presented with PASP >35 mmHgand the rest 12 cases with PASP ≤35 mmHg, while allcontrols had PASP ≤35 mmHg. No difference was notedbetween the two CF patient groups with and withoutexercise-induced PH, regarding FEV1% predicted and FVC%predicted values (data not shown). Those CF patients withpost exercise PASP >35 mmHg exhibited lower WR peak%predicted, VO2 peak% predicted, VO2/HR% predicted, andSpO2 peak, and a trend for higher VE/VCO2@AT, comparedto the rest of CF patients. However, none of the parametersindicative of ventilatory limitation during exercise, that isVEmax, VEmax/MVV and BR differed between CF patientswith and without postexercise PH (Table 3).

In cases, but not in controls, ΔSPAP established aninverse, strong correlation to several parameters of exercisecapacity, that is, WR peak (watts and % predicted), VO2 peak(mL/kg∗min and % predicted), oxygen pulse (mL/kg∗beatsand % predicted), and SpO2 peak (Table 4). ΔSAP was alsostrongly correlated to Schwachman score (Spearman rho =−0.698, P = 0.002) in the group of cases. On the contrary,neither pulmonary function testing parameters, nor anyvariable indicative of ventilatory limitation during exercise

Table 2: Exercise parameters in cases and controls.

Cases Controls P value

WR peak (% predicted) 66. ± 14.8 97.1± 5.3 0.002

VO2 peak (mL/kg∗min) 28.5± 7.5 35.9± 4.8 0.026

VO2 peak (% predicted) 73.7± 14.8 97.8± 5.9 0.002

AT (mL/kg∗min) 20.1± 5.4 27.1± 5.5 0.013

AT (% predicted) 48.8± 10.5 69.9± 12.3 0.008

VO2/HR (mL/kg∗beats) 10.8± 3.4 15.9± 4.9 0.032

VO2/HR (% predicted) 88 ± 17.2 98.1± 11.3 0.048

VE/VCO2@AT 31.3± 3.7 25.1± 3.0 0.008

VEmax (lit) 69.8 ± 21 81.1± 30.5 NS

VEmax/MVV (%) 66.7± 21.5 53.4± 18.1 NS

MVV-VEmax (lit) 40.01± 36.3 79.96± 34.9 0.047

BR (lit) 40 ± 36.3 79.7± 34.7 0.045

RER 1.19± 0.7 1.21± 0.6 NS

SpO2 peak (%) 93.9 ± 4 96 ± 1.9 NS

Borg scale fatigue (peak) 7.9± 1.4 8.2± 1.3 NS

Borg scale dyspnea (peak) 6.2± 2.9 5.9± 1.4 NS

Data are presented as mean ± 1 standard deviation. NS: not significant, WRpeak: maximal work rate, VO2 peak: maximal oxygen uptake, AT: oxygenuptake at anaerobic threshold, VO2/HR: peak oxygen pulse, VE/VCO2@AT:ventilatory equivalent for carbon dioxide at anaerobic threshold, VE max:minute ventilation at peak exercise, VEmax/MVV: minute ventilation atpeak exercise to maximum voluntary ventilation ratio, BR: breathingreserve, RER: respiratory exchange ratio, SpO2 peak: oxygen saturation atpeak exercise.

was correlated to ΔSPAP, in any of the groups (data notshown).

4. Discussion

The main findings of our study are (a) patients withmild-to-moderate CF without PH at rest, exhibit higherpostexercise PASP and lower exercise capacity compared tocontrols, without reaching ventilatory limitation, (b) exerciseimpairment and dyspnea are probably more pronouncedamong CF patients with higher (≥35 mmHg) postexercisePASP, and (c) ΔPASP is inversely correlated with maximalwork rate and oxygen uptake in cases, but not in controls.

The association of PH and exercise tolerance in mild-to-moderate CF is far from clear. Montgomery et al. havereported the case of a CF adult patient with severe lungdisease and pulmonary hypertension that increased signifi-cantly after exercise and improved with sildenafil treatment[17]. Although recently published data demonstrate that CFpatients suffer from endothelial dysfunction and defectivedilatation of pulmonary vessels during exercise [18], inanother study the estimated rest PASP was not correlatedto submaximal exercise capacity among CF patients withboth severe and moderate disease [19]. However, there is nodata regarding the exercise-induced increase of pulmonaryartery pressure and its possible impact on maximum exercisetolerance, in patients with less severe disease and no evidentpulmonary vasculopathy at rest.

In this study, a group of mild-to-moderate CF patientswithout PH at rest exhibited a higher postexercise PASP,


Table 3: Exercise parameters in cystic fibrosis patients withpostexercise PASP ≤35 mmHg and postexercise PASP >35 mmHg.

PASP ≤35 PASP >35 P value

WR peak (watts) 135.7± 46.6 102.4± 30.1 NS

WR peak (% predicted) 73.6± 11.6 51.4± 7.5 0.008

VO2 peak (mL/kg∗min) 30.1± 8.0 25.1± 5.5 NS

VO2 peak (% predicted) 78.4± 15.4 63.4± 6.2 0.042

AT (mL/kg∗min) 20.3± 5.6 16.3± 1.6 NS

AT (% predicted) 51.9± 11.2 42.1± 4.6 NS

VO2/HR (mL/kg∗beats) 11.4± 3.8 9.2 ± 2 NS

VO2/HR (% predicted) 93.5± 17.6 75.9± 7.8 0.040

VE/VCO2@AT 28.1± 4.1 32.8± 2.3 0.061

VEmax (lit) 63.3± 21.6 57.5± 18.7 NS

VEmax/MVV (%) 64.3± 23.8 70.5± 14.9 NS

MVV-VEmax (lit) 39.6± 35.1 42.1± 38.9 NS

BR (lit) 42.8± 40.7 31.7± 21.2 NS

RER 1.19± 0.7 1.19± 0.4 NS

SpO2 peak (%) 95.4± 2.7 90.6± 4.7 0.016

Borg scale fatigue (peak) 7.9± 1.6 7.4± 0.6 NS

Borg scale dyspnea (peak) 5.9± 3.3 7.0± 2.2 0.046

Data are presented as mean ± 1 standard deviation. NS: not significant, WRpeak: maximal work rate, VO2 peak: maximal oxygen uptake, AT: oxygenuptake at anaerobic threshold, VO2/HR: peak oxygen pulse, VE/VCO2@AT:ventilatory equivalent for carbon dioxide at anaerobic threshold, VEmax:minute ventilation at peak exercise, VEmax/MVV: minute ventilation atpeak exercise to maximum voluntary ventilation ratio, BR: breathingreserve, RER: respiratory exchange ratio, SpO2 peak: oxygen saturation atpeak exercise.

Table 4: Pearson correlations between ΔPASP and exercise variablesin cases and controls.

ΔPASP

Cases Controls

WR peak (watts) −0.539∗ NS

WR peak (% predicted) −0.764∗∗ NS

VO2 peak (mL/kg∗min) −0.540∗ NS

VO2 peak (% predicted) −0.714∗ NS

AT (mL/kg∗min) NS NS

AT (% predicted) NS NS

VO2/HR (mL/kg∗beats) −0.530∗ NS

VO2/HR (% predicted) −0.663∗∗ NS

VE/VCO2@AT NS NS

SpO2 peak (%) −0.701∗ NS

ΔPASP: PASP (pulmonary artery systolic pressure) after exercise − PASPat rest, WR peak: maximal work rate, VO2 peak: maximal oxygen uptake,AT: anaerobic threshold, VO2/HR: peak oxygen pulse, VE/VCO2@AT:ventilatory equivalent for carbon dioxide at anaerobic threshold, SpO2 peak:oxygen saturation at peak exercise; ∗P < 0.05; ∗∗P < 0.001; NS: notsignificant.

a lower exercise capacity and a higher VE/VCO2 ratio atanaerobic threshold compared to controls, although bothgroups terminated exercise due to fatigue, without present-ing respiratory limitation. VE/VCO2 is considered to be anoninvasive marker of pulmonary vascular resistance [20]and previous studies have reported a significant increase in

the VE/VCO2 slope, during exercise, both in CF patients[1] and in patients with severe PH [21]. During maximalCPET, both dyspnea and exercise limitation were evenmore pronounced among patients with higher (>35 mmHg)postexercise PASP values, compared to the rest of thepatients. Furthermore, ΔPASP values correlated to peakwork rate, peak O2 uptake, O2 pulse and SpO2 at peakexercise only in the group of cases, while no correlation wasnoted to any measurement of ventilation during exercise.These data indicate that in CF patients with less severedisease, pulmonary circulation could be defective, resultingto impaired exercise capacity, regardless of the patients’respiratory reserve.

As in several chronic respiratory diseases, exercisecapacity in adult CF patients could also be influenced bysuboptimal nutritional status and muscle dysfunction [22,23]. Malnutrition results, through a loss of muscle mass,to a reduction in every day activities and to peripheralmuscle deconditioning [23]. The current study was notdesigned to control for these confounders, so their specificimpact on exercise limitation could not be assessed. However,patients and controls weighted the same, since they wereheight-matched and had the same BMI, which indicates thattheir nutritional status was similar. Moreover, there was nodifference in peak Borg fatigue score neither between patientsand controls, nor between patients with and without exerciseinduced PH, indicating a similar peripheral muscle effort.These results come to an agreement with a previous studywhere differences in Borg scores of muscle effort and lacticacid were noted only in the group of CF patients with severerespiratory limitation and not among those with mild andmoderate disease [4]. Future studies are needed to assess theexact impact of pulmonary vasculopathy on exercise capacityin these patient group, independently of nutritional statusand muscle dysfunction.

There are certain limitations in this study. The num-ber of participants who were included was quite small.However, our findings regarding exercise performance arevery similar to the ones from larger cohorts [2]. Moreover,although the “gold standard” for measurement of pulmonaryartery pressure remains right heart catheterization, Dopplerechocardiography has proved to be an easily accessible,noninvasive alternative in previous studies [24]. Anotherlimitation is that PASP is very much affected by cardiacoutput, so a higher postexercise PASP might reflect not apulmonary vascular disease but just a persistently highercardiac index; however there is no sufficient explanation asto why this could be established in cases but not in controls.Furthermore, cardiac output and PASP rapidly recover afterexercise in a variable and non-proportional rate [25] andArgiento et al. have previously reported that this could be areason why post-exercise measurements may be problematic[26]. However, in the latter study, PASP was estimated 5to 20 minutes after exercise, while in the current study allmeasurements were conducted within 60 seconds. Althoughthe method adopted in our study may still estimate PASPvalues less accurately than echocardiography during exercise[25], it has been previously used in order to assess pulmonaryhypertension among scleroderma patients and was found to


correlate well with several parameters of exercise capacity[27, 28].

In conclusion, pulmonary vascular disease, as establishedby high post exercise PASP, might be added to the list ofdeterminants of both exercise impairment and increased dys-pnea among CF patients with mild-to-moderate disease. Toour knowledge, this study is the first to directly investigate thepotential association between estimated PASP and maximalexercise capacity in these patients. The limitation in physicalfunctioning and the increased dyspnea are the two primaryparameters which affect quality of life in CF patients [29].Under this scope, further studies, including a larger numberof patients with different stages of disease severity are needed,in order for the contribution of pulmonary vascular diseasein the physical impairment of this population to be fullyevaluated.

Conflict of Interests

The authors declare that they have no conflict of interests.

References

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[2] A. R. Shah, D. Gozal, and T. G. Keens, “Determinants ofaerobic and anaerobic exercise performance in cystic fibrosis,”American Journal of Respiratory and Critical Care Medicine,vol. 157, no. 4, pp. 1145–1150, 1998.

[3] E. Pouliou, S. Nanas, A. Papamichalopoulos et al., “Prolongedoxygen kinetics during early recovery from maximal exercisein adult patients with cystic fibrosis,” Chest, vol. 119, no. 4, pp.1073–1078, 2001.

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[10] A. A. Ionescu, N. Payne, I. Obieta-Fresnedo, A. G. Fraser,and D. J. Shale, “Subclinical right ventricular dysfunction incystic fibrosis: a study using tissue Doppler echocardiography,”

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[14] N. B. Schiller, “Two-dimensional echocardiographic determi-nation of left ventricular volume, systolic function, and mass.Summary and discussion of the 1989 recommendations of theAmerican Society of Echocardiography,” Circulation, vol. 84,no. 3, pp. I280–I287, 1991.

[15] K. Wasserman, J. E. Hansen, D. Y. Sue, W. W. Stringer, and B.J. Whipp, “Measurements during integrative cardiopulmonaryexercise testing,” in Principles of Exercise-Testing and Interpre-tation, K. Wasserman, J. E. Hansen, D. Y. Sue, W. W. Stringer,and B. J. Whipp, Eds., pp. 76–110, Lippincott Williams andWilkins, Philadelphia, Pa, USA, 4th edition, 2005.

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[17] G. S. Montgomery, S. D. Sagel, A. L. Taylor, and S. H.Abman, “Effects of sildenafil on pulmonary hypertension andexercise tolerance in severe cystic fibrosis-related lung disease,”Pediatric Pulmonology, vol. 41, no. 4, pp. 383–385, 2006.

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Research Article

Predicted Aerobic Capacity of Asthmatic Children:A Research Study from Clinical Origin

Lene Lochte

Centre for Child and Adolescent Health, Section of Aetiological Epidemiology, School of Social and Community Medicine,Faculty of Medicine and Dentistry, University of Bristol, Bristol BS8 2BN, UK

Correspondence should be addressed to Lene Lochte, [email protected]

Received 22 December 2011; Revised 20 March 2012; Accepted 18 April 2012

Academic Editor: Luke Howard

Copyright © 2012 Lene Lochte. This is an open access article distributed under the Creative Commons Attribution License, whichpermits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Objective. To compare longitudinally PAC of asthmatic children against that of healthy controls during ten months. Methods.Twenty-eight asthmatic children aged 7–15 years and 27 matched controls each performed six submaximal exercise tests ontreadmill, which included a test of EIA (exercise-induced asthma). Predicted aerobic capacity (mLO2/min/kg) was calculated.Spirometry and development were measured. Physical activity, medication, and “ever asthma/current asthma” were reported byquestionnaire. Results. Predicted aerobic capacity of asthmatics was lower than that of controls (P = 0.0015) across observationtimes and for both groups an important increase in predicted aerobic capacity according to time was observed (P < 0.001).FEV1 of the asthmatic children was within normal range. The majority (86%) of the asthmatics reported pulmonary symptomsto accompany their physical activity. Physical activity (hours per week) showed important effects for the variation in predictedaerobic capacity at baseline (F = 2.28, P = 0.061) and at the T4 observation (F = 3.03, P = 0.027) and the analyses showedimportant asthma/control group effects at baseline, month four, and month ten. Physical activity of the asthmatics correlatedpositively with predicted aerobic capacity. Conclusion. The asthmatic children had consistently low PAC when observed acrosstime. Physical activity was positively associated with PAC in the asthmatics.

1. Introduction

Children with asthma often experienced breathlessness dur-ing physical activity and therefore tended to avoid vigorousphysical activity with disadvantageous consequences to theirphysical conditioning [1, 2].

There are few paediatric pulmonary conditions in whichphysical activity has had such potentially harmful effect onpatients, not only by limiting exercise capability, but also byacting as a direct stimulus to the underlying pathophysiology[3]. Exercise-induced asthma (EIA) has been recognizedas one major manifestation of untreated asthma [4] withphysical activity acknowledged as a powerful trigger ofasthmatic disease [3, 5].

Physical activity in paediatric asthma has been influencedby physical as well as psychosocial variables. The compre-hensive psychosocial variables included attitudes towards

exercise. Asthmatic children have demonstrated negativeattitudes towards physical activity [6] to be influenced by thelimitations that they experienced in safely and unrestrictedlyto join physical activities [5].

The cardiopulmonary fitness of asthmatic children wasoften suboptimal. Some studies revealed lower predictedaerobic capacity (PAC) among asthmatic children than withhealthy controls [7–9]; yet, most such observations werecross-sectional. Evidence of PAC in healthy children was wellestablished whereas PAC of asthmatic youths was scarcelydocumented, in particular from longitudinal observation.

The author hypothesized that the asthmatic childrenwould demonstrate a different PAC than that of their healthypeers when observed over time. Therefore, the aim ofthe present study was to longitudinally compare PAC ofasthmatic children against that of healthy controls during tenmonths.


2. Materials and Methods

2.1. Subjects. Twenty-eight asthmatic children and 27 con-trols without asthma volunteered. Data were collected atpretest, baseline, EIA-test, and following one, four, and tenmonths (Table 1). Eligible controls at month four were n =24. Data were collected from October 1998 through April1999.

The asthmatic group was selected according to thediagnose bronchial asthma as defined by the British ThoracicSociety [10] and the control group was matched on age (±1year), sex, weight (±5 kg), and height (±5 cm). The bodyweight, mean (SD) of asthmatics and controls was 40.0 (17.9)and 38.4 (13.8), respectively. Standing height, mean (SD) ofasthmatics and controls was 143.7 (16.1) and 146.7 (17.8),respectively.

The asthmatic children were outpatients at the PaediatricAsthma and Allergy Clinic of Copenhagen University Hos-pital, Gentofte, Denmark, and they were included consec-utively (informed consent obtained from parents) at theirfirst visit. The control children were recruited from schoolclasses of the Capital Region of Denmark and informedconsent obtained from parents of the controls. All subjectswere Caucasians.

Both groups were free from pulmonary tract infections,that is, forced expiratory volume in one sec (FEV1) was>60% of the expected normal value estimated from height.Descriptive information on the study population is shown inTable 2.

2.2. Questionnaire. Baseline data on asthma, allergy, andphysical activity were obtained by a standardised ques-tionnaire, published elsewhere [7]. The questionnaire wasdispatched to all children, was identical for asthma andcontrols, and was completed by the children assisted by theirparents.

2.2.1. Asthma and Allergy. The questions on “ever asthma”and “current asthma” have previously been applied in sur-veys of asthmatic child populations [11]. The questionnaireincluded questions on pulmonary and allergy symptomsfrom skin, eyes, nose, lungs and symptoms frequency (daily,weekly, monthly, and/or every half-year).

2.2.2. Physical Activity. Scores of physical activity as reportedby hours per week were adopted from validated standards[12] of the Health Behaviour in School-Aged Children(HBSC) study [13]. For hours (h) per week, six responsecategories were applied: (i) none, (ii) 0.5 h, (iii) 1 h, (iv)2-3 hs, (v) 4–6 hs, (vi) ≥7 hs. Physical activity referred tovigorous leisure-time physical activity, exercise, or sportsoutside school hours equivalent to at least slow jogging thatmade the children sweat or become out of breath.

Eighty-six per cent (n = 24) of the asthmatic childrenreported pulmonary symptoms such as wheezing, dyspnoea,chest tightness, and cough related to exercise. Of these, 46%(n = 11) reported allergic reactions such as eczema, 38%(n = 9) rhinoconjunctivitis, and 25% (n = 6) both eczema

and rhinoconjunctivitis. Hospital journals confirmed thatone-fifth of the asthmatic children were atopic and 7% (n =2) had undergone allergy test; 14% (n = 4) had familypredisposition.

Classification of asthma severity made reference tomodified national guidelines, published elsewhere [7], basedon present symptoms, lung function (PEFR), and medicaltreatment. PEFR, peak expiratory flow rate, (L/min) was self-recorded “same day morning and evening measurements”over the course of two weeks using Wright’s peak flowmeter (Airmed, Harlow, United Kingdom). The best of threePEFR exhalations was used for calculation. From the PEFRmeasurements, the percentage predicted values of PEFR(PEFR pred) was estimated using standard normal valuesaccording to height [14].

One PEFR registration was not returned and one wasexcluded due to extreme value. Hence, the study populationcomprised 13 children with mild asthma (PEFR pred >90–100%) 11 children with moderate (PEFR pred = 80–90%),and 2 children suffered severe asthma (PEFR pred <80%).

2.3. Design. The study population was followed prospec-tively by treadmill exercise tests during ten months.

2.4. Procedures. The age of the children was calculated fromthe date of birth to the nearest 0.01 years. Each childperformed one pretest of PAC to establish running speed(RS) at an individual level according to sex and age, and topreclude training effect [4]. The pretest served to familiarisethe child with the clinical environment and procedures. Thebaseline PAC-test was performed one week after the pretest.

All tests were performed separately with each child inthe Clinic during the afternoon, and the asthmatic childrenperformed the EIA-test prior to the pollen season. Allinstructions given during tests used comparable standardsfor asthmatic and control children.

A basic exercise warm-up programme preceded eachexercise test. No EIA occurred during warmup. As a safetyprecaution, blood saturation was monitored during eachtest in all subjects by finger electrode method using NellcorSymphony N-3000-120 (Nellcor, Boulder, CO, USA).

Body weight (BW) was measured by a spring balance tothe nearest 0.1 kg (indoor clothing worn). Standing heightwas measured by a stadiometer to the nearest 0.1 cm andno shoes were worn during the measurements. Body massindex (BMI) was calculated as BW (kg)/height2 (m). Theanthropometric apparatus was calibrated according to thestandards of the manufacturers.

The endocrinological development of children aged ≥10years was assessed by three methods: (i) the presence ofmenarche in girls, (ii) sex characteristics (breast size ingirls and pubic hair in girls and boys) evaluated accordingto standards [15] and (iii) the testicular volume of boysestimated by comparison with an ellipsoid of known volumeusing Prader Orchidometer, Zachmann, 1974, [15]. Theresults were presented in Table S1 (see SupplementaryMaterial available online at doi:10.1155/2012/854652).


Table 1: Matrix of observations.

Pretest T0 T-EIA T1 T4 T10

Age (year) and sex X

BMI X X X

Development characteristics X

Ever asthma/current asthma X

FEV1 X X X X X

ΔFEV1 X

HRrest X

Medical treatment X X X X X

Menarche X X X

PAC X X X X

PEFR X

Physical activity (h/week) X X X

RS X X X X X X

Symptoms (allergic/pulmonary) X

T0: time point of baseline test, T-EIA: time point EIA-test, T1: time point 1st month test, T4: time point 4th month test, T10: time point 10th month test,BMI: body mass index (kg/m2), FEV1: forced expiratory volume in one second (mL), ΔFEV1: maximal percentage decrease in FEV1 from baseline, HRrest:heart rate at rest (beats/min), PAC: predicted aerobic capacity (mLO2/min/kg), PEFR: peak expiratory flow rate (L/min), and RS: running speed (km/h).

Majorities of all children measured were distributed atpubic hair stage 1-2 and breast stage 2 or beyond accordingto the standards [15]. The boys had testicular volumesdeveloped to stage 1-2 in 67% (n = 6) and 71% (n = 5)of the evaluated asthmatics and controls, respectively.

The PAC-tests were conducted without influencingongoing medical treatment (corticosteroids: budesonide; β2-agonist: terbutaline (short term); salmeterol and formoterol(long term).

Prior to the EIA-test the intake of β2-agonist wasdiscontinued for short term (12 hours) and long term (24hours). No controls received medication.

HR at rest (HRrest) was monitored following 30 min ofrest in a horizontal position. Heart rate (HR (beats/min))during the PAC and EIA-tests was continuously monitoredin all children by a thoracic Polar Sport Tester band (Polar,Kempele, Finland).

FEV1 (mL) was measured in a standing position by aVitalograf (Spiropharma, Vitalograf Gold Standard, Klamp-enborg, Denmark). FEV1 was measured immediately before,3, 5, and 10 minutes posttest. At the EIA-test FEV1 wasfurther measured at 15, 20, and 30 minutes posttest.

At least three forced expirations were performed andchildren were instructed to blow from maximal inspirationand exhale quickly, forcefully, and for as long as possible intothe instrument. Forced expiratory manoeuvres were repeateduntil two measurements of FEV1 within 100 mL of each otherwere obtained. The largest FEV1 value was used for analysisand from the FEV1 measurements the percentage predictedvalues of FEV1 (FEV1 pred) were estimated using standardnormal values according to height [14]. The accuracy ofthe vitalograf was verified weekly by a calibrated syringe(Spiropharma), corrected to body temperature, atmosphericpressure, and saturation with water vapour (BTPS). Thevitalograf did not need adjustment during the test period.

Table 2: Descriptive information, mean (±SD), of study popula-tion by group.

Baseline

Asthma P Control

♂ / ♀ ♂ / ♀N 17 / 11 16 / 11

Age 10.1 (2.5) ns 9.9 (2.7)

BMI 18.8 (3.8) = 0.046 17.1 (2.0)

FEV1 2.2 (0.8) ns 2.3 (1.0)

PAC 44.3 (7.3) = 0.009 52.1 (13.4)

RS 7.5 (1.6) ns 7.7 (1.3)

N : numberAge (years), BMI: body mass index (kg/m2), FEV1: forced expiratory volumein one second (L), PAC: predicted aerobic capacity (mLO2/min/kg), RS:running speed (km/h)P: probability, ns: nonsignificant.

2.4.1. PAC-Test. The PAC-test constituted a submaximal,5 min exercise test of continuous running on a treadmill(Spiropharma, Cardiogenics no. 2113, Klampenborg, Den-mark). The test constituted continuous increments of RSfor the first 2 min, and RS was adjusted to a submaximalHR of 170–180 beats per minute (bpm). When HR attainedsteadystate (HRsteadystate), the current speed was maintainedfor 3 min; HRsteadystate (bpm) and RS (km/h) were registeredfor the estimation of PAC (mLO2/min/kg).

2.4.2. EIA-Test. The EIA-test followed standardised guide-lines using continuous increments of RS and inclination ofthe treadmill (Spiropharma, Cardiogenics no. 2113, Klamp-enborg, Denmark) during 5 minutes. When a submaximalHR of 180–190 bpm was attained, RS was kept constant for


the duration of the test. The range of inclination was 5–15%and each subject wore a nose clip.

The pulmonary response from the EIA-test was expressedas the maximal percentage decrease in FEV1 (ΔFEV1%)from baseline (baseline value—lowest recorded postexercisevalue/baseline value × 100%) [4]. This value was used toassess the occurrence of EIA with the cutoff value set at 15%[4, 16]. No asthmatic children reached the set cut-off. Themean (95% CI) ΔFEV1% was 3.8 (1.5; 6.2) and 1.3 (−1.1;3.8) in asthma and controls, respectively.

2.4.3. Equation PAC. PAC was estimated by the followingvariables: maximal heart rate, HRmax (220 bpm—age inyears) (estimated), HRsteadystate ≈ 170–180 bpm (measured),and HRrest (measured). The estimation used a constant forchild resting metabolic rate, RMR, (4.2 mLO2/min/kg) [17],and VO2 (mLO2/min/kg). The estimation used laboratoryderived VO2 corresponding to RS at HRsteadystate publishedearlier [2].

The applied equation (below) was a modification ofthe original equation proposed by Klausen et al. [18]. Thecalculation resulted in the estimation of PAC adjusted for BWas follows:

PAC = RMR +HRmax −HRrest

HRsteadystate −HRrest× (VO2 − RMR). (1)

The ambient room temperature and relative humiditywere measured before each exercise test by a portablethermohygrograph (Model SL (Sound Level) 435007). Theaverage room temperature and relative humidity of the PACtests were 19.6± 1.9◦C and 62.9±10.6%, respectively; for theEIA-test the average room temperature and relative humiditywere 19.0 ± 1.0◦C and 58.1± 5.0%, respectively.

2.5. Statistics. General linear multiple regression analysiswas conducted to determine the adjusted effects on PAC(APL∗plus, Scientific Time Sharing Corporation, Rockville,MD, USA).

Two-way analysis of variance “repeated measures” wasapplied to evaluate the variations in the mean values of PACover time within and between the groups of asthma andcontrol children (SAS, v.8.2 PROC MIXED, SAS InstituteInc., Cary, NC, USA).

Multiple linear regression models were applied to esti-mate the groups (asthma and controls) specific regressionlines using STATA (v.11.2) (Stata Corp, College Station, TX,USA). Age and sex adjustments were performed.

The sample size was calculated to comply with the powerestimation, sensitivity (1 – β) = 80% and specificity (α) =0.95. The significance tests applied were twotailed and thesignificance level set at 5%.

2.6. Ethics. The study was approved by the local ScientificEthics Committee of Copenhagen County (no. KA 98029 m),the Danish Data Protection Agency (no. 1998-1200-320),and the Institutional Review Board at the Paediatric Asthmaand Allergy Clinic of Copenhagen University Hospital,

PAC

80

70

60

50

40

30

20

10

0

AsthmaControls

Baseline T1 T4 T10

T1: First monthT4: Fourth monthT10: Tenth month

Figure 1: Mean PAC (mLO2/min/kg) with SE in the asthmatic andcontrol groups of children according to four observation times.

Gentofte, Denmark. The study followed procedures inaccordance with the Declaration of Helsinki (1964).

3. Results

Across the four times of observation, important groupdifferences of PAC (P = 0.0015) between the asthmaticchildren and controls were seen with asthmatics depictingconsistently lower PAC than controls. PAC of both groupsof children increased gradually with time (P < 0.001)(Figure 1).

PAC increased with age in the two groups of children. Thebaseline age effect (F = 14.73, P < 0.001) was illustrated inFigure 2. At baseline the asthmatics showed lower PAC valuesthan controls (group effect: F = 10.35, P = 0.0020).

Figure 3 illustrated the associations between physicalactivity and PAC at baseline separate for asthmatics andcontrols. By analysing all children (n = 55) important effectsof group (full and reduced models) and marginal effect ofphysical activity (full model) was found as follows: [(reducedmodel, effect of group: F = 7.29, P = 0.0093) (R2 = 0.1209)],[(full model, effect of group: F = 5.52, P = 0.023; effect ofphysical activity (h/week): F = 2.28, P = 0.061); (R2 = 0.2898,P = 0.0090)].

Figure 4 illustrated the associations between physicalactivity and PAC at T4 separate for asthmatics and controls.Analysis of all children (n = 52) at T4 showed importanteffects of group and physical activity [(reduced model, effectof group: F = 6.86, P = 0.012; effect of physical activity(h/week): F = 3.03, P = 0.027); (R2 = 0.3040, P = 0.0042)].

Figure 5 illustrated the associations between physicalactivity and PAC at T10 separate for asthmatics and controls.By analysis of all children (n = 55) at T10 only the effect ofgroup demonstrated importance [(reduced model, effect ofgroup: F = 5.22, P = 0.026) (R2 = 0.0897)].


100

80

60

40

20

05 10 15

PAC

All casesn = 55y = 33 + 1.9xr = 0.46P < 0.001

AsthmaControls

Age

r = Spearman’s rho

Figure 2: Baseline associations of PAC (mLO2/min/kg) by age(years) in asthmatic and control children.

Table S2 showed that in the asthmatic group, approxi-mately 50% of children received medical treatment and thedistribution was almost similar at all four time points.

Table S3 illustrated the distributions of “ever asthma” and“current asthma” for asthma and control children. Physicalactivity (h/week) did not show important effects (ns) for“ever asthma” or “current asthma” in asthmatics or controls(not illustrated).

4. Discussion

The study showed that PAC of the asthmatic children waslower than that of controls and differed between the fourtimes of observation. During the ten months observationtime, I found relative consistency in the importance of phys-ical activity effects for PAC, and physical activity explainedup to 30% of the variation in PAC. There were consistentand important group differences between asthmatics andcontrols.

This study was designed to limit well-known bias risks.Laboratory assessment of cardiopulmonary fitness has beendocumented to require considerable resources whereas thesubmaximal exercise test overcomes most of these limita-tions. The PAC-test represented a noninvasive and validated[2] submaximal method and was therefore preferred for thecurrent study.

The requests for study participation followed consecutiveorder to ensure random selection of the asthmatic children.The procedure for selection of participants was closelymonitored to ascertain sufficient time for the individual tocontemplate participation. To avoid misclassification from,for example, disproportionate inclusion of highly motivatedchildren (and parents), reflection time prior to giving

consent was extended and further information provided asnecessary.

The results showed that physical activity correlatedpositively with PAC, more consistently so for the asthmaticsthan controls, but physical activity did not influence “everasthma” or “current asthma” and therefore these self-reports may not sustain the results. The reports of “everasthma” and “current asthma,” nonetheless, complied withdata from other Scandinavian child populations that usedthe same asthma and physical activity questions [19]. Itcannot be excluded, however, that parents of the asthmaticswho were well informed of benefits of physical activityfor cardiopulmonary fitness in optimal asthma care, couldhave been overrepresented. Likewise, these parents may haverecalled physical activity differently than other parents andcould have overreported the physical activity levels of theirchildren.

The study groups originated from slightly varyingsociodemographic residential areas of the Capital Region.The variation could have influenced the data collection, forexample, self-reports of physical activity in a nonrandommanner. Although unconfirmed, it was also possible thatasthmatics and younger children received more supportcompleting the questionnaire than controls.

The results on the endocrinological development of theincluded children were almost similar in asthmatics andcontrols. Although asthmatic PAC was consistently lowerthan that of controls, the stable increase of PAC by age inboth groups may illustrate the well-recognized systematicinfluence of development on VO2 [20].

The participants with moderate and severe asthma whoaccounted for almost half of the asthmatic group may infact have contributed disproportionately to the asthmaticreductions of PAC compared to the mild asthmatic subjects.Earlier findings from studies of severe childhood asthma [8]may lend support to this interpretation.

The asthmatic children demonstrated a similar increaseof PAC over time to that seen for the healthy controls. It isplausible that this finding reflected a diminished asthmaticdisease activity occurring during the observation time.However, this hypothesis needs to be prespecified and testedseparately in future studies.

Indeed poor fitness of asthmatic children has been thor-oughly investigated and physical risk factors documented[1, 3, 21, 22]. In healthy children, “previous” physical activityplayed an important role for the “present” activity level[23]. The reports of physical activity were collected at threedifferent time points during ten months, but the currentstudy was not designed to test for separate effects of physicalactivity in the asthmatics and controls investigated.

Over the course of the past decade, reviewers of asthmaticchildren’s fitness have demonstrated a shift in their conclu-sions from previous “asthmatic children being less fit” [3] tocurrent positions that “studies are inconclusive” [24]. Thisstudy complied with the former representing the times whendata were collected.

The submaximal exercise test has been validated indifferent populations, and the validity of a submaximalexercise test depended on a predictable relationship between



80

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0 2 4 6 0 2 4 6

PAC

Physical activity (h/week)

95% CI

Fitted regression line

Baseline PAC by physical activity

Controls Asthma

n = 27y = 40.3 + 2.9xr = 0.41P= 0.033

n = 28y = 31.3 + 3xr = 0.4P= 0.035

PAC (mLO2/kg/min)

Figure 3: Baseline associations between PAC (mLO2/min/kg) and physical activity (h/week) in asthmatics and controls.

95% CI

Fitted regression linePAC (mLO2/kg/min)

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T4 PAC by physical activity

n = 24y = 37.1 + 4.4xr = 0.41P= 0.033

n = 28y = 40.2 + 1.7xr = 0.31P= 0.11

PAC


Controls Asthma


Figure 4: T4 associations between PAC (mLO2/min/kg) and physical activity (h/week) in asthmatics and controls.



T10 PAC by physical activity

95% CI

Fitted regression linePAC (mLO2/kg/min)


80

60

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2 3 4 5 6 2 3 4 5 6

PAC

Controls Asthman = 27y = 56.7− 0.6xr = 0.0016P= 0.99

n = 28y = 40.8 + 2xr = 0.36P= 0.058

Figure 5: T10 associations between PAC (mLO2/min/kg) and physical activity (h/week) in asthmatics and controls.

oxygen consumption and HR [25]. The equation applied inthis study was validated under the assumption of a linearrelationship between VO2 and HR and the submaximalmethod tended to underestimate PAC compared with lab-oratory monitored aerobic capacity [2]. Given such minorunderestimation of the present PAC, there has not beenan indication that this would have affected the groups ina differential way. Conversely, for the current full studypopulation target heart rates were reached and hence theexpected physiological stress levels.

The present results of the EIA-test indicated that lungfunction could have played a role in the asthmatic reductionof PAC. The decrease of the asthmatic lung function duringthe EIA-test potentially reflected the untreated nature of theasthmatic disease for half of the asthmatic children. Previousresults of mild-to-moderate childhood asthma have shownthat only sedentary children had impaired cardiopulmonarycapacity [26]. The positive associations between physicalactivity and PAC that was seen for the asthmatics seeminglyconfirmed such results.

Earlier results published by my group found lowerHRsteadystate of the control group than of the asthma groupto suggest that physical activity could have influenced HRdifferently in asthmatic and nonasthmatic children [2].Even though regressions slopes for HR and VO2 havebeen documented to alter with pulmonary disease [27],applying explanations like those to the present asthmaticPAC reductions would involve laboratory replication.

Although the present results have been generated fromrelatively small sample sizes, initial precautions were taken topower the study adequately. The longitudinal design servedto demonstrate consistency of the results for PAC. Whilethese data cannot ascertain causal associations between phys-ical activity and PAC, they are nonetheless consistent withfindings of others for both physical activity [19] and PAC[8, 9]. The reduced aerobic capacity of the asthmatic studygroup, consistent across observation times, may further addto the biological credibility of the current study hypothesis.

The extent to which the present asthmatic study popu-lation reported limitations from pulmonary symptoms dis-agreed with other asthmatic child populations studied [26].Colleagues [26] investigated a large sample size and couldreport results of VO2max from subgroups of sedentary andactive (up to 2 hs/week) children that seemed comparablewith the present results. The current sample, however, wasnot sufficiently sized to accommodate subgroup divisions.The frequent and largely untreated pulmonary symptomsreported to accompany physical activity by the asthmaticgroup could be a further explanation for the low asthmaticPAC that I found. Ultimately, the results may have been influ-enced by other known or unknown residual confoundingfactors that were not accounted for in this work.

BMI was higher than expected for the asthmatic group,which possibly indicated that the weight matching wasnot complete for the study population. VO2 was derivedfrom established data on VO2 and running speed. Although


baseline running speed showed no group differences, itcannot be excluded that the asthmatic children weighingslightly more than controls may have produced slowerrunning speeds than controls at any one point during the tenmonths of observation time.

More recently, high BMI of asthmatic children and itsrelations with poor physical conditioning have been wellacknowledged. Hence, the results suggested that rather thanattempting to match participants on weight, future studies ofasthmatic children might indeed generate important infor-mation from weight associations. In the light of the globalweight burden with young people, the weight matchingdesign may not be warranted when asthmatic children areunder study.

In summary, the current results indicated that physicalactivity could be a risk factor for low PAC in the asthmaticchildren investigated. Although the results cannot necessarilybe extrapolated to the global scene of asthmatic child popu-lations, they underlined that joint monitoring of pulmonarysymptoms and cardiopulmonary fitness of asthmatics maysupply important information for prevention and treatmentof those young suffering pulmonary symptoms by physicalactivity.

At a time when objective and individualised criteria tostandardise exercise tests of asthmatic children are requested[3, 28], and where those that do exist have not been routinelyimplemented in paediatric asthma clinics, the public healthimplication of the current study is to target standardisedmonitoring of pulmonary function as well as cardiopul-monary fitness of asthmatic young; hence, to capture thevariations of this burdening pulmonary childhood disease;the recommendations apply equally to Scandinavian andcomparable populations of asthmatic children.

5. Conclusions

This study showed that the asthmatic children had consis-tently low PAC when observed across time. Physical activitywas positively associated with PAC in the asthmatics.

Conflicts of Interest

The author has stated explicitly that there are no conflicts ofinterest in connection with this paper.

Acknowledgments

The study was financially supported by Danish LungAssociation, Danish Health Insurance Foundation, DanishPhysical Therapy Organisation, and Danish Asthma andAllergy Association. The author wishes to thank Dr. AageVoelund, Principal Statistician Emeritus, Novo Nordisk A/SCopenhagen, Bagsvaerd, and Statistical Advisory Service,Department of Biostatistics, University of Copenhagen,Copenhagen, Denmark, for statistical guidance and dataanalysis. Dr. Vagn Braendholt, Associate Professor, Consul-tant, Zealand Region of Denmark, South Naestved Hospital,

Naestved, Denmark, is thanked for the screening and enrol-ment of the asthmatic children and for his clinical assistancewith the assessments by orchidometer. The current resultscontributed to the MPH thesis of the author: thesis no. 70,University of Copenhagen, Copenhagen, Denmark, 2001. Inturn, this baseline study has been followed up by laboratoryvalidation of the PAC-test. The follow-up study was pub-lished in the Clinical Respiratory Journal and made availableat: http://dx.doi.org/10.1111/j.1752-699X.2008.00107.x.Thebaseline data for PAC have been included in detailed presen-tations of the follow-up study at International conventions asfollows: World Congress of Asthma, Interasma, Guadalajara,Mexico, 15 October 2006; World Asthma Meeting, Istanbul,Turkey, 23 June 2007; European Respiratory Society, AnnualCongress, Stockholm, Sweden, 19 September 2007; Interna-tional Epidemiological Association, XVIII World Congressof Epidemiology, Porto Alegre, Brazil, 21 September 2008.On 10 December 2009, the comprehensive research project(1998–2009) was summarised at the International Meetingof Health and Environment, Rome, Italy.

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[2] L. Lochte, M. Angermann, and B. Larsson, “Cardiorespiratoryfitness of asthmatic children and validation of predictedaerobic capacity,” Clinical Respiratory Journal, vol. 3, no. 1, pp.42–50, 2009.

[3] C. J. Clark and L. M. Cochrane, “Physical activity and asthma,”Current Opinion in Pulmonary Medicine, vol. 5, no. 1, pp. 68–75, 1999.

[4] J. M. Henriksen, Exercise-induced asthma in children [DMScThesis], Allergy and Munksgaard International Publishers,Copenhagen, Denmark, 1990.

[5] K.-H. Carlsen, “Exercise induced asthma in children andadolescents and the relationship to sports,” Pediatric Allergyand Immunology, vol. 9, no. 4, pp. 173–180, 1998.

[6] R. C. Strunk, D. A. Mrazek, J. T. Fukuhara, J. Masterson, S.K. Ludwick, and J. F. LaBrecque, “Cardiovascular fitness inchildren with asthma correlates with psychologic functioningof the child,” Pediatrics, vol. 84, no. 3, pp. 460–464, 1989.

[7] L. Lochte, “Exercise capacity of asthmatic children validationin clinical Context,” Thesis 70, University of Copenhagen,Copenhagen, Denmark, 2001.

[8] G. Hedlin, V. Graff-Lonnevig, and U. Freyschuss, “Workingcapacity and pulmonary gas exchange in children withexercise-induced asthma,” Acta Paediatrica Scandinavica, vol.75, no. 6, pp. 947–954, 1986.

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[10] “The British guidelines on asthma management, 1995: reviewand position statement, British Thoracic Society,” Thorax, vol.52, supplement 1, pp. 1S–21S, 1997.

[11] M. I. Asher, U. Keil, H. R. Anderson et al., “International studyof asthma and allergies in childhood (ISAAC): rationale andmethods,” European Respiratory Journal, vol. 8, no. 3, pp. 483–491, 1995.


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[13] C. Currie, “Appendix 1: International standard version of thecore questions of the 1997-98 survey,” in Health Behaviour inSchool-aged Children. Research Protocol for the 1997-98 Survey,A World Health Organization Cross-National Study, vol. 20,University of Edinburgh, Edinburgh, UK, 1998.

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[15] D. M. Styne, “The physiology of puberty,” in Clinical PaediatricEndocrinology, C. G. D. Brook, Ed., pp. 234–252, BlackwellScience, Oxford, UK, 1995.

[16] M. M. Haby, J. K. Peat, C. M. Mellis, S. D. Anderson, and A. J.Woolcock, “An exercise challenge for epidemiological studiesof childhood asthma: validity and repeatability,” EuropeanRespiratory Journal, vol. 8, no. 5, pp. 729–736, 1995.

[17] D. C. Nieman, M. D. Austin, S. M. Chilcote, and L. Benezra,“Validation of a new handheld device for measuring restingmetabolic rate and oxygen consumption in children,” Interna-tional Journal of Sport Nutrition and Exercise Metabolism, vol.15, no. 2, pp. 186–194, 2005.

[18] K. Klausen, I. Hemmingsen, and B. Rasmussen, “Konditionog fysiologiske virkninger af konditionstraening,” in AlmenIdraetsteori, K. Klausen, I. Hemmingsen, and B. Rasmussen,Eds., pp. 81–96, Forum, Copenhagen, Denmark, 1985.

[19] W. Nystad, “The physical activity level in children with asthmabased on a survey among 7-16-year-old school children,”Scandinavian Journal of Medicine and Science in Sports, vol. 7,no. 6, pp. 331–335, 1997.

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[28] A. L. Varray, J. G. Mercier, C. M. Terral, and C. G. Prefaut,“Individualized aerobic and high intensity training for asth-matic children in an exercise readaptation program: is trainingalways helpful for better adaptation to exercise?” Chest, vol. 99,no. 3, pp. 579–586, 1991.


Research Article

Test-Retest Reliability and PhysiologicalResponses Associated with the Steep Ramp Anaerobic Test inPatients with COPD

Robyn L. Chura,1 Darcy D. Marciniuk,2 Ron Clemens,2 and Scotty J. Butcher1

1 School of Physical Therapy, University of Saskatchewan, 1121 College Dr, Saskatoon, SK, Canada S7N 0W32 Respirology, Critical Care and Sleep Medicine, University of Saskatchewan, Canada

Correspondence should be addressed to Scotty J. Butcher, [email protected]

Received 4 November 2011; Revised 7 April 2012; Accepted 11 April 2012

Academic Editor: Roland Wensel

Copyright © 2012 Robyn L. Chura et al. This is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properlycited.

The Steep Ramp Anaerobic Test (SRAT) was developed as a clinical test of anaerobic leg muscle function for use in determininganaerobic power and in prescribing high-intensity interval exercise in patients with chronic heart failure and Chronic ObstructivePulmonary Disease (COPD); however, neither the test-retest reliability nor the physiological qualities of this test have beenreported. We therefore, assessed test-retest reliability of the SRAT and the physiological characteristics associated with the testin patients with COPD. 11 COPD patients (mean FEV1 43% predicted) performed a cardiopulmonary exercise test (CPET) onDay 1, and an SRAT and a 30-second Wingate anaerobic test (WAT) on each of Days 2 and 3. The SRAT showed a high degreeof test-retest reliability (ICC = 0.99; CV = 3.8%, and bias 4.5 W, error −15.3–24.4 W). Power output on the SRAT was 157 Wcompared to 66 W on the CPET and 231 W on the WAT. Despite the differences in workload, patients exhibited similar metabolicand ventilatory responses between the three tests. Measures of ventilatory constraint correlated more strongly with the CPET thanthe WAT; however, physiological variables correlated more strongly with the WAT. The SRAT is a highly reliable test that betterreflects physiological performance on a WAT power test despite a similar level of ventilatory constraint compared to CPET.

1. Introduction

Individuals with Chronic Obstructive Pulmonary Disease(COPD) are often prescribed aerobic exercise to enhancefunction and reduce shortness of breath during activities ofdaily living. General guidelines for this exercise prescriptionsuggest patients should exercise continuously at moderateintensities [1–3]. There is evidence, however, to suggest thatexercise at higher intensities may be more beneficial for thispopulation [4].

Traditionally, results from cardiopulmonary exercise test-ing (CPET) involving an incremental, graded exercise test(GXT) of 8–12 minutes in duration, have been used toprescribe exercise for individuals with COPD and are widelyconsidered to be the gold standard for measurement ofcardiopulmonary function and aerobic performance [5].CPET, however, may underestimate the workload requiredfor optimal physiological benefit from exercise training due

to ventilatory limitations causing early test cessation and ablunted peak work rate [6, 7]. High-intensity interval exer-cise intensity may be prescribed for healthy individuals basedon tests of anaerobic power and capacity, such as a 30-secondWingate Anaerobic Test (WAT), which is considered to be thegold standard measure of anaerobic capacity [8]; however,these types of tests have not been widely used, nor wouldbe appropriate in typical clinical use for individuals withCOPD. However, the steep ramp anaerobic test (SRAT) hasbeen proposed as a clinical test that may more accuratelyreflect leg muscle capabilities and better set interval trainingintensities for individuals with chronic heart failure [7, 9, 10]and COPD [11].

The SRAT was developed by Meyer et al. [7] for use bypatients with heart failure to specifically challenge the mus-cles maximally before patients reached a cardiovascular limit.Unlike the WAT, in which subjects must pedal as fast aspossible against a fixed resistance for 30 seconds, the SRAT


is an incremental GXT where the workload increases by25 watts every 10 seconds until patient exhaustion [7, 10].Much higher work rates are typically achieved with the SRATcompared to the incremental CPET, and a percentage ofthe peak work rate (PWR) from the SRAT can be used toprescribe intervals for training in this population [10]. TheSRAT has also been used in COPD patients to prescribeintensity for high intensity interval exercise [11]. The SRATmay be better tolerated for use in populations that becomeshort of breath quickly during exercise because, and ratherthan being a timed test like the WAT, it is patient-limited.The test-retest reliability and the physiological responses ofthe SRAT in this population remain unknown.

The purposes of this study were to determine (a) thetest-retest reliability of the SRAT in patients with COPD and(b) the physiologic, ventilatory, and perceptual parametersobtained on the SRAT compared with performance on atraditional CPET or WAT in COPD patients.

2. Materials and Methods

2.1. Subjects. 11 patients (7 males and 4 females) with mod-erate and severe COPD (11) were recruited through theSaskatoon Pulmonary Rehabilitation Program and throughthe Division of Respirology, Critical Care and Sleep Medi-cine, University of Saskatchewan. Subjects had a respirologistconfirmed diagnosis of COPD [12], did not require theuse of supplemental oxygen at rest or during exercise, andhad not been in hospital with an acute exacerbation withinthe previous 6 weeks. Subjects were excluded if they hadcardiovascular or musculoskeletal disease that would preventthem from completing heavy exercise.

This research was approved by the University of Saskat-chewan Biomedical Ethics Committee. All subjects signed aconsent form and were advised that they could freely with-draw from the study at any time.

2.2. Research Design. A randomized cross-over design wasused to assess subjects’ physiological, ventilatory, and per-ceptual responses to the SRAT as compared to the CPET andWAT. The subjects attended 3 sessions for testing, within a 3week period, with at least 48 hours separating sessions. Aninitial baseline assessment session included screening, assess-ment of criteria for study admission, pulmonary functiontests, and an incremental CPET. The following 2 visits eachincluded a 30-second WAT and a SR test separated by onehour. The second of these 2 visits was included in order toestablish the test-retest reliability of these measures. Theorder of the tests was constant between visits but randomizedbetween subjects.

2.3. Pulmonary Function Testing and CPET. Resting pul-monary function testing (FEV1, FVC, RV, TLC, DLCO) wasperformed according to established standards [13] (V6200CAutobox and Vmax 229D gas analyzer, SensorMedics Corp.,Yorba Linda, California, USA). CPET was performed usingestablished protocols [5] with a workrate increment of5–15 W/min on a mechanically braked cycle ergometer

(800 S, SensorMedics). The test was terminated when thesubject indicated voluntary exhaustion, or the revolutionsper minute fell below 60 and could not be increased withencouragement. Peak work rate (CPETpeak), and all physi-ologic, ventilatory, and perceptual measures were collectedand used in the analysis.

2.4. 30-Second Wingate Anaerobic Test (WAT). The WAT wasperformed as per established protocol [8]. Subjects com-pleted a self-paced 5 minute warm-up on the cycle ergometer(Monark 894 E, Ergomedic). Subjects were given two prac-tice trials where they were asked to pedal as fast as possible,and one half the brake weight used for the actual WAT wasapplied to the flywheel for two seconds. This protocol wasrepeated for a second practice trial. After a two minuterest, the WAT was performed. Patients were instructed tomaintain the maximal velocity for 30 seconds against the fullbreak weight (females: 35 g/kg [14] and males: 45 g/kg [15]).Continual standardized encouragement was given to thepatient throughout the entire test. The average power output(WATavg) over the 30 seconds (which reflects anaerobiccapacity), and all physiologic, ventilatory, and perceptualmeasures were collected and used in the analysis.

2.5. The Steep Ramp Anaerobic Test (SRAT). The SRAT wasperformed as described by Meyer et al. [7]. Testing wasperformed using the same equipment, with monitoring ofthe same parameters as for the CPET and WAT. After a 2minute unloaded warm-up, the intensity increased by 25watts every 10 seconds. The test was terminated when thesubject indicated they could no longer continue or if therevolutions per minute fell below 60 rpm. Continual stan-dardized encouragement was given to the patient throughoutthe entire test. The peak work rate (SRATpeak), and all phys-iologic, ventilatory, and perceptual measures were collectedand used in the analysis.

2.6. Physiologic, Ventilatory, and Perceptual Measures. For allthree exercise tests, physiologic measurements (blood pres-sure, heart rate (HR) and rhythm (3-lead ECG), oxygen sat-uration (SpO2) (N-395, Nellcor)), and perceptual measures(ratings of perceived exertion (RPE) for dyspnea and fatigue(0–10 modified Borg scale)), were obtained at baseline, dur-ing exercise, and end-exercise. Measurements including oxy-gen consumption (VO2 ), carbon dioxide production (VCO2 ),tidal volume (VT), minute ventilation (VE), and respiratoryrate (RR) were recorded on a breath-by-breath basis and wereaveraged in 10 second increments. Inspiratory capacity (IC)maneuvers [16] were performed at baseline, during exercise,and end-exercise. From these maneuvers, operational lungvolumes (end-expiratory lung volume (EELV) and end-inspiratory lung volume (EILV)) were calculated at each timepoint. EELV was estimated as the difference between TLCand IC, whereas EILV was estimated as the EELV plus VT .The degree of ventilatory constraint at peak exercise wasevaluated by the inspiratory reserve volume (IRV; equalsTLC−EILV) and by the VT /IC ratio.


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Figure 1: (a) Bland-Altman plot of reliability of Wingate average power measurements (Wavg) between both sessions. Y-axis: The differencebetween Wavg from one day to the next. X-axis: The average of Wavg between both days. (b) Bland-Altman plot of reliability of the steepramp peak power measurements (SRpeak) between both sessions. Y-axis: The difference between SRpeak from one day to the next. X-axis: Theaverage of SRpeak between both days.

Table 1: Subject characteristics.

Subject characteristics (n = 11)

Male : Female, (n) 7 : 4

Age, years 71±3

Weight, kg 84.6± 21.0

BMI, kg/m2 29.3± 5.9

TLC, L (% predicted) 6.56± 1.21 (108 ± 10)

RV, L (% predicted) 3.42± 0.91 (151 ± 32)

FEV1, (L) (% predicted) 1.08± 0.26 (43 ± 15)

FVC (L), (% predicted) 2.73± 0.68 (83 ± 15)

FEV1/FVC, % 41 ± 10

Mean ± standard deviation. Abbreviations: TLC: total lung capacity, RV:residual volume, FEV1: forced expiratory volume in 1 second, FVC: forcedvital capacity, pred = predicted.

2.7. Statistical Analysis. Test-retest reliability of the SRAT andthe WAT was analyzed using Intraclass correlations (ICC),coefficient of variation (CV), and Bland-Altman plots. Theanalysis of the data comparing the CPETpeak, SRATpeak,and the Wavg, as well as the ventilatory, physiological, andperceptual measures for each of the three tests includedrepeated measures analysis of variance (ANOVA). Tukey’spost hoc analysis was performed where significant differenceswere found. Pearson r correlations for the work rate,ventilatory, physiological, and perceptual measures of each ofthe three tests were also performed to determine significantrelationships between measures. All statistical analyses wereperformed using a significance level of P < 0.05.

3. Results

Subject characteristics are presented in Table 1. Both theWAT and the SRAT demonstrated a high degree of test-retest

400

350

300

250

200

150

100

0

50

CPET WAT SRAT

Wor

k ra

te (

W)

∗

∗

Figure 2: Comparison of the cardiopulmonary exercise test peakpower (CPET), the steep ramp test (SRAT) peak power, and theaverage power in the 30-second Wingate anaerobic test (WAT) inwatts. Results are presented as mean ±0.95 confidence interval. ∗ =P < 0.05 versus SRAT.

reliability. ICC was 0.99 and 0.98, and the CV was 3.8%and 8.6% for the SRAT and WAT, respectively. Bland-Altmanplots demonstrated a small degree of bias and error betweenthe 2 sessions for the SRAT (4.5 W; −15.3–24.4 W, resp.) andthe WAT (12.0 W;−49.5–73.5 W, resp.) (See Figures 1(a) and1(b)).

Between-test physiological, ventilatory, and perceptualdata are presented in Table 2. In addition, Figure 2 showsthe mean work rates for the 3 tests. There were significantdifferences between CPETpeak, SRATpeak, and Wavg (65.9 ±35.6, 156.8 ± 67.9, and 231.2 ± 113.4 W, resp.). There wereno differences between VO2 , RR, SpO2, HR, VE, IC, IRV,EELV, and VT /IC measurements at peak exercise in each of


Table 2: End-exercise measures for cardiopulmonary exercise test (CPET), steep ramp test (SR), and Wingate anaerobic test (WAT)presented with means and standard deviations.

End-exercise measuresTests

CPET SRAT WAT

PWR (CPET & SR) Wavg (WAT) 65.9± 35.9 156.8± 67.9∗† 231.2± 113.4∗

VO2 (L/min) 1.11± 0.46 1.07± 0.41 0.99± 0.45

VCO2 (L/min) 1.13± 0.52 0.97± 0.40 0.90± 0.42∗

VE (L/min) 40.436± 13.33 38.94± 13.01 39.73± 14.73

RER 1.00± 0.13 0.90± 0.07∗ 0.89± 0.08∗

VT (L) 1.19± 0.31 1.12± 0.24 1.09± 0.33

VT /IC (%) 76.5± 13.0 70.1± 12.0∗ 70.4± 13.8

IC/TLC (%) 24.1± 4.7 25.1± 5.5 23.5± 4.0

EELV/TLC (%) 75.9± 4.7 74.9± 5.5 76.5± 4.0

EILV/TLC (%) 94.0± 4.7 92.0± 5.1 92.9± 4.2

IRV/TLC (%) 6.0± 4.7 8.0± 5.1 7.1± 4.2

RR (breaths per minute) 34 ± 6 35 ± 8 37 ± 8

SpO2 (%) 91.5± 3.0 92.3± 1.5 93.3± 3.9

HR (beats per minute) 111.9± 20.9 109.8± 19.7 116.9± 22.0

HR (%pred) 75.3± 14.7 73.7± 13.0 78.5± 14.5

Dyspnea 5.6± 1.8 5.5± 2.1† 6.8± 2.3

Leg Fatigue 5.7± 1.7 5.6± 1.8 6.2± 1.9

Mean ± standard deviation. ∗: P < 0.05. †indicates significance from WAT. PWR: peak work rate, VO2 : oxygen consumption, VCO2 : carbon dioxideelimination, VE : minute ventilation, RER: respiratory exchange ratio, VT : tidal volume, IC: inspiratory capacity, TLC: total lung capacity, EELV: end expiratorylung volume, EILV: end inspiratory lung volume, IRV: inspiratory reserve volume, RR: respiratory rate, SpO2: oxygen saturation, HR: heart rate.

the 3 tests. VCO2 at peak exercise (VCO2peak ) in the CPET washigher than VCO2peak in the WAT. VCO2peak in the SRAT was notsignificantly different from the other 2 tests. The respiratoryexchange ratio (RER) at end exercise in the CPET was higherthan both the SRAT and the WAT; however, the RER wasnot significantly different between the SRAT and the WAT.Dyspnea was significantly lower in the SRAT compared to theWAT; however, no difference in RPE in regards to leg fatiguebetween the tests.

Table 3 shows the correlation coefficients for the SRATtest data with respect to the corresponding data on each ofthe CPET and WAT tests. SRATpeak correlated strongly withboth the CPETpeak and the Wavg. Most ventilatory and phys-iological parameters for the SRAT were found to correlatesignificantly with those on the CPET and WAT. Physiologicexercise performance variables tended to correlate betterwith the WAT, whereas ventilatory parameters tended tocorrelate better with the CPET.

4. Discussion

The primary purpose of this study was to determine the test-retest reliability of the SRAT. Our data demonstrate excellentretest consistency. All subjects but one obtained the samepeak score on the SRAT between both test sessions. Thereliability of the WAT was similarly assessed to determinethe appropriateness of this test to be used as a criterionmeasure of anaerobic capacity in patients with COPD.Although reliability analysis of this test was not part of thepurposes of this study, we demonstrated that the WAT was

Table 3: Pearson’s r correlation coefficients between end-exercisemeasures during the SRAT and the cardiopulmonary exercise test(CPET) and Wingate anaerobic test (WAT).

End-exercise measuresTests

CPET WAT

PWR (CPET & SR) Wavg

(WAT)0.887∗ 0.887∗

VO2 (L/min) 0.891∗ 0.939∗

VCO2 (L/min) 0.837∗ 0.926∗

VE (L/min) 0.800∗ 0.930∗

RER 0.549 0.615∗

VT (L) 0.907∗ 0.954∗

VT /IC (%) 0.838∗ 0.806∗

IC/TLC (%) 0.905∗ 0.873∗

EELV/TLC (%) 0.905∗ 0.873∗

EILV/TLC (%) 0.916∗ 0.880∗

IRV/TLC (%) 0.916∗ 0.880∗

RR (breaths per minute) 0.559 0.877∗

SpO2 (%) 0.499 −0.017

HR (bpm) 0.684∗ 0.955∗∗

indicates significant correlation (P < 0.05).

also a reliable measure. Reliability of the WAT has beenpreviously established in health individuals [17] and patientswith COPD using an abbreviated WAT [8]. Although thereliability of the SRAT has not yet been reported, incrementalexercise tests of a smaller increment have demonstrated


excellent reliability [18]; therefore, it is not surprising thatthe SRAT would also do so. The SRAT has been used inprevious studies examining the effects of exercise training [9–11]; therefore, the results of the present study lend credibilityto the use of the SRAT as an outcome measure in theseprevious, and future studies.

The secondary purpose of the present study was tocompare the exercise responses and performance variableson the SRAT with those on the CPET and WAT. We demon-strated that the SRAT results in higher peak power outputthan the aerobic-based CPET, but lower than the anaerobic-based WAT. Despite these work load disparities, there wereno differences in end-test oxygen consumption, heart rate,ventilation, and levels of ventilatory constraint betweenthe tests. These findings complement those of Miyaharaet al. [19] who demonstrated that, during CPET, higherramp increments resulted in higher power outputs thanlower ramp increments, despite similar cardiorespiratoryresponses; however, the ramp increment used in the SRATwas much higher than that used previously. As has beenobserved in patients with COPD during a CPET, limitationson the ability of patients to increase ventilation duringexercise constrain performance, and consequently, oxygenconsumption and heart rate [20]. Our study supports thisassertion because we also found that mean values for peakheart rate were not maximal at end-exercise. The similarlevels of metabolic demand and ventilatory limitation foundin the present study suggest subjects performing any of thethree tests are primarily limited by the inability to increaseventilation, rather than by a physiologically maximal oxygenconsumption. Due to the short amount of time to completethe SRAT (67± 27 seconds) [21] and the high power outputcompared to the CPET, however, the SRAT elicits a greaterdegree of leg muscle anaerobic power than the CPET. Inaddition, the SRAT peak power was also strongly correlatedwith WAT average power output. These factors combinedsuggest that the SRAT may be a practical test of anaerobicpower, even in the setting of ventilatory limitation.

Ventilatory constraint at end-exercise is suggested byan inability to further increase tidal volume due in partto dynamic hyperinflation [20] and by nearing predictedmaximal ventilation. With dynamic hyperinflation, EELVincreases, IRV decreases, and therefore VT during exerciseoccupies a large percentage of IC [20]. In the present study, itwas assumed that the patients would be limited by ventilatoryfactors during the CPET, in part due to reliance upon aerobicmetabolism and the requirement to ventilate in proportionto aerobic demands. Therefore, it was also assumed thatpatients would hyperinflate less, demonstrate less ventilatoryconstraint (i.e., increased ventilatory reserve), and be limitedmore by peripheral muscle performance during tests lastingonly 30–90 seconds (i.e., the SRAT). Despite the varyingexercise durations, however, the similar level of ventilatoryrestriction observed at the end of the 3 tests suggests this maybe a shared limiting factor in all of the tests. This commonlimitation may help to explain the high degree of correlationbetween the three tests.

The WATavg was significantly larger than the SRATpeak,and this may be partially related to the protocol design of the

tests. The anaerobic metabolism present at the beginning ofthe WAT encourages high work rates without immediatelydriving ventilation. The patients gave maximal effort acrossthe 30 seconds without realizing the degree of dyspnea theywould incur due to the requirement for acid buffering, whichwas often near the end, or after cessation, of the WAT.Although not objectively measured in our study protocol,posttesting dyspnea scores were often reported to increasebeyond the end-test values during immediate recovery fromthe WAT. In contrast, the SRAT, although also at a veryhigh power output, builds incrementally to a patient-limitedmaximum. Patients were better able to control the amountof work performed prior to the development of disablingdyspnea, and the posttest increase observed in the WAT didnot occur in the SRAT. For this reason, the SRAT seems to bean appropriate compromise between the low peak work rateof the CPET and the high work rate, but demanding recovery,of the WAT. The power output on the SRAT, albeit statisticallylower than the WATavg, combined with the short duration ofthe SRAT suggest that the SRAT reflects performance on ananaerobic power test (WAT), while allowing the patients toappropriately and safely manage their symptoms.

Since ventilation may have been a common limitingfactor between the 3 tests, stratifying the population intocategories of disease severity may have elicited differentresults in this study. Similarly, stratifying according to gendermay have shown some differences. These options may beavailable in a study with a larger sample size.

This study demonstrates that the SRAT is a highly reliablemeasure of high-intensity muscle performance. In addition,it supports the assertion that leg power is often markedlyunderestimated in the traditional incremental design of theCPET, and that exercise is frequently terminated before amaximal muscular response has been achieved because ofventilatory limitations [6, 7, 22, 23]. Performance on theSRAT resulted in peak work rates 238% higher than thatof the CPET. This is comparable to the findings of bothMeyer et al. [10] and Puhan et al. [11], where SRATpeak wasapproximately double the CPETpeak in chronic heart failureand COPD patients, respectively. Although further researchis required, it is likely the SRAT would be more useful thanthe CPET in assessing and establishing intensities for exercisetraining that are sufficiently high to elicit clinical gains in legmuscle power and high-intensity performance.

5. Conclusions

The SRAT is a highly reliable, feasible, high-power test inpatients with COPD and may be useful in estimating legmuscle power. CPET underestimates the capabilities of theleg muscles to perform high levels of work, due to theattainment of ventilatory limitations in COPD patients.Despite similar degrees of ventilatory constraint, the SRATdemonstrates markedly greater work rates and better reflectsanaerobic performance in this population. The SRAT maythus be more suitable for prescribing high-intensity intervalexercise in order to increase the potential for training benefitin COPD patients.


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Review Article

Cardiopulmonary Exercise Testing inLung Transplantation: A Review

Katherine A. Dudley1 and Souheil El-Chemaly2

1 Harvard Combined Pulmonary Fellowship Program, Harvard Medical School, Boston, MA 02115, USA2 Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Harvard Medical School,Boston, MA 02115, USA

Correspondence should be addressed to Souheil El-Chemaly, [email protected]

Received 13 December 2011; Revised 11 February 2012; Accepted 28 February 2012


Copyright © 2012 K. A. Dudley and S. El-Chemaly. This is an open access article distributed under the Creative CommonsAttribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work isproperly cited.

There has been an increase in lung transplantation in the USA. Lung allocation is guided by the lung allocation score (LAS), whichtakes into account one measure of exercise capacity, the 6-minute walk test (6MWT). There is a paucity of data regarding the roleand value of cardiopulmonary stress test (CPET) in the evaluation of lung transplant recipients while on the transplant waiting listand after lung transplantation. While clearly there is a need for further prospective investigation, the available literature stronglysuggests a potential role for CPET in the setting of lung transplant.

1. Introduction

1.1. Lung Transplantation in the United States. There areapproximately 1,700 patients listed for and awaiting lungtransplantation in the United States [1]. Patients withunderlying idiopathic pulmonary fibrosis (IPF), chronicobstructive pulmonary disease (COPD), cystic fibrosis,alpha-1-antitrypsin deficiency, and pulmonary hypertensioncomprise the majority of those awaiting transplant [2].

Since the first successful human lung transplant in 1983[3], there have been significant efforts to improve morbidityand mortality associated with the procedure, particularly asthe number of annual lung transplants continues to rise from837 lung transplants in 1998 to 1,458 in 2007 [4]. One-yearsurvival rates have modestly improved from 73.4% to 80.4%over that time, yet they lag behind those of other solid organtransplant recipients [4].

Donor lungs in the United States are allocated foruse by the lung allocation score (LAS) system, whichwas instituted in May 2005, in an effort to reduce timeand mortality on the transplant waitlist. LAS takes intoaccount a number of factors (Table 1), including diagnosis,functional status, presence of diabetes, assisted ventilation,amount of supplemental oxygen, percent predicted forced

vital capacity (FVC), systolic and mean pulmonary arterypressures, mean pulmonary capillary wedge pressure, pCO2

levels, six-minute walk distance, and serum creatinine [5, 6].As recently reviewed by Eberlein et al., the LAS score placestwice as much weight on the waitlist urgency measure, whichis an estimate of the number of days expected to live on thewaitlist, as opposed to the posttransplant survival measure,which estimates the number of days expected to live inthe first year after transplant [7, 8]. The LAS system hasmet its initial goals of decreasing mortality while on thewaitlist for transplant, as well as making lung transplantan option for those at greatest need. However, there hasbeen no clear posttransplant benefit in terms of changes inmorbidity or mortality, though longer-term data are pending[2, 9]. Further, the usage of the LAS resulted in increaseduse of resources, morbidity, and mortality in the subgroupof patients with very high LAS [9]. Further insight intoother parameters, beyond those in the LAS score, and howthey could predict posttransplant outcomes are thereforeabsolutely necessary to guide future improvements in thefield of lung transplantation medicine.

The role of exercise testing in decisions surroundingappropriateness of listing patients, as well as counsel-ing those undergoing transplant regarding life expectancy,


Table 1: Components of the Lung Allocation Score.

Date of birth

Height

Weight

Lung diagnosis

Functional status

Performs activities of daily living

Some assistance

Total assistance

No assistance

Diabetes

Dependency unknown

Not diabetic

Insulin dependent

Not insulin dependent

Mechanical ventilation

BiPAP

CPAP

Continuous mechanical

Intermittent mechanical

No assisted ventilation

Supplemental O2 requirements

At rest

At night

With exercise

Not needed

Amount (liters)FiO2 (%)

Forced vital capacity (%predicted)

Pulmonary artery systolic pressure (mmHg)

Mean pulmonary artery pressure

Pulmonary capillary wedge pressure

Current pCO2

Highest pCO2

Lowest pCO2

Change in pCO2 (%)

Six minute walk distance (feet)

Serum creatinine (mg/dL)

performance status, and posttransplant outcomes, has notyet been fully explored. Exercise testing could provide veryuseful information that may not be otherwise captured bythe LAS.

We performed a National Library of Medicine (Pubmed)search of the English language literature using the followingqueries “lung transplantation” and “exercise” or “cardiopul-monary stress testing” or “six-minute walk test.” Additionalsearch words were later used for specific topics (e.g., “lungtransplantation” and “anemia” or “myopathy”). Manuscriptswere included if directly addressed the subject of thisreview and offered differing points of view or additionalexplanations.

1.2. Six-Minute Walk Test. The LAS takes into account sixminute walk test (6-MWT) performance, one measurement

of exercise capacity. The 6-MWT has been used in anumber of disease states, including conditions for whichpatients undergo lung transplantation, such as COPD orIPF, to assess functional status and therapeutic responseand define prognosis [18, 19]. One of the first evaluationsof the performance of 6-MWT as an assessment in thoseundergoing lung transplantation was completed in thelate 1990s [20]. Results of this retrospective study of 145patients demonstrated that a pretransplant 6-minute walkdistance of less than 400 meters predicted mortality with asensitivity, specificity, positive predictive value, and negativepredictive value of 0.8, 0.27, 0.27, and 0.91, respectively.The authors’ conclusion was that the 6-MWT could beuseful as a tool to help assess the timing of listing forlung transplantation. In addition, a subsequent retrospectivestudy of 454 patients demonstrated that 6-MWT results—both distance but also presence of desaturation—could beindependently associated with mortality for IPF patientsawaiting transplant, and, importantly, the test performancewas a better predictor of 6-month mortality than spirometry[21]. This demonstrated the relevance of measures of exercisecapacity, beyond the data provided by standard pulmonaryfunction tests.

Despite its incorporation in the LAS, until recently, the 6-MWT has not been evaluated in lung transplant recipients.In their retrospective chart review of 49 patients who hadcompleted 6-MWT six months following transplant, Seoaneand colleagues defined normative values for this populationand assessed whether test performance predicted mortality[22]. At the 6-month mark, distance walked on the 6-MWThad a normal distribution, with a mean (±SD) of 426 ±84.5 m (range 240–711 m). At 12 months, for the patients inwhom data was available for both 6 months and 12 months,a significant improvement from 348 ± 15 m to 478 ± 14 mwas seen (P = .0001), with 77% of patients demonstratingan improved walk distance. Despite these improvements, 6-month performance was not a predictor of survival (OR =1.002).

To date and to our knowledge, there have not been anystudies comparing pre- and post-transplant 6-MWT, so it isnot known how pretransplant 6-MWT performance relatesto posttransplant functional status and whether there isany significant prognostic information in that relationship.Furthermore, utility of 6-MWT as a marker of functionalstatus, morbidity, or other parameters has not been fullyexplored. One retrospective study evaluating pretransplant6-MWT performance of 130 patients concluded that thedistance covered was not useful as a marker to predict long-term mortality in the posttransplant setting, as there was nostatistically significant difference in survival curves based ondistance covered.

The 6-MWT is the most commonly used test for theassessment of exercise and functional capacity, in part, nodoubt, due to the relative ease of administration. Addition-ally, when compared to other measures of exercise capacity,such as the 2-minute walk test, 12-minute walk test, self-paced walk test, and shuttle walk test, the 6-MWT hasbeen established as well tolerated by patients and morereflective of the activities of daily living [23]. However,


performance on a 6-MWT is not a reliable surrogate markerof more traditional indices of exercise capacity—such as cycleergometry—though some studies have demonstrated utilityin some populations, such as COPD patients, who may notbe able to complete a CPET [24]. The 6-MWT has severallimitation [25]; for instance, it does not determine peakoxygen uptake, and therefore an objective determinationof functional capacity and impairment cannot be made,nor can the relative contributing factors that limit exercisebe elicited. In addition, the 6-MWT is limited by the so-called “ceiling effect” leaving patients who walked most atbaseline with little room for improvement without jogging[26]. Moreover, there is a learning effect when the 6-MWT is administered repeatedly which occurs after thefirst administration and is maintained for 2 months andoccurs to a lesser extent with repeated administration [27].Though in some clinical situations, the 6-MWT may clearlyprovide helpful information in terms of functional status.Given these limitations, the American Thoracic Society(ATS) recommendation outlines that 6-MWT data should becomplementary to cardiopulmonary exercise testing, and nota substitute [28].

1.3. Cardiopulmonary Exercise Test (CPET). Guidelines re-garding routine use of CPET pre- and post-transplant arelacking, which reflects the absence of significant data onhow to apply CPET results for optimization of timing oftransplant and predicting outcomes. Several studies from the1990s, described below, have attempted to evaluate the roleof CPET in the lung transplant population. Most of thesestudies (Table 2), however, are limited by small sample sizes,evaluation of a specific respiratory pathology, as well as typeof transplant (i.e., single versus bilateral sequential). The lackof generalizability thus makes real-life application of thesestudy results in the clinical setting challenging.

1.3.1. In the Evaluation Phase for Lung Transplantation.The utility of CPET has been evaluated in some specificlung diseases but has not been evaluated on a broaderscale in a heterogeneous population. The clinical courseof disease progression can be difficult to predict for manychronic respiratory disorders, thereby making selection ofthe optimal time of transplantation challenging.

(i) Interstitial Lung Disease (ILD). In ILD, there are conflict-ing reports on the value of different variables on CPET inpredicting mortality [10]. In one study of 117 patients withIPF, a peak VO2 of less than 8.3 mL/min/kg was associatedwith an increased risk of subsequent mortality. On adjustedmultivariate analysis, VO2 max threshold was a more robustpredictor of survival than resting PaO2 or desaturation below88% during 6-MWT [10]. However, in this study only 8patients (6.83%) were below the threshold, but about 46% ofpatients died. This strongly suggests that while the thresholdis informative, it does not help to predict a large part ofthe mortality in IPF. Surprisingly, not all patients below thethreshold peak VO2 desaturated below 88% during the 6-MWT, suggesting perhaps that “self pace” allowed patients

Table 2: CPET indicators that correlate with outcome.

Disease CPET outcome variable Reference

Idiopathicpulmonary fibrosis

VO2 ≤ 8.3 mL/min/kg [10]

PaO2 slope ≤ −60 mmHg/L/min [11]

Chronicobstructivepulmonary disease

VO2 max (<15 mL/min/kg) [12]

VO2 max < 60% predicted [12]

VO2 max [13]

PaO2 slope ≤ −80 mmHg/L/min [14]

Pulmonaryvascular disease

peak SBP ≤ 120 mm or less and [15]

peak VO2 ≤ 10.4 mL/kg/min

Cystic fibrosisVO2 max,VE/VO2, peak work [16]

VO2 of 45 mL/min/kg [17]

to trade oxygen saturation for distance walked and perhapsthe importance of the composite score distance-saturationproduct as a more accurate predictor of survival [29].

In a retrospective study of 41 patients, Miki, et al.,found that PaO2-slope (ΔPaO2/ΔVO2) was the most usefulpredictor of survival, independent of age [11]. A PaO2-slope equal or less than −60 mmHg/L/min was predictive ofdecreased survival with a median survival of 1.6 versus 4.5years. The important aspects of this paper are first that PaO2-slope could not be correlated to diffusion capacity of carbonmonoxide (DLCO) at rest; second that it suggests potentialbenefits for pulmonary rehabilitation in IPF (supplementalO2, suppressing shallow breathing, etc..). Pulmonary reha-bilitation was subsequently shown to improve 6-MWD andfatigue in subjects with IPF [30].

(ii) Chronic Obstructive Pulmonary Disease (COPD). Pul-monary function tests (PFTs) can correlate poorly with exer-cise performance on CPET for COPD patients, with somepatients exceeding exercise limits despite severe impairmenton PFTs and others with more surprising limitations thansuggested by PFTs. Ortega et al. [12], in their study of 78stable patients with COPD, showed that the definition ofimpairment would vary greatly whether an absolute cut-off value for VO2 max is used (15 mL/kg/min) or a percentpredicted VO2 max less than 60%. The absolute numberhas better specificity (79.5 versus 66.6), and the percentpredicted has better sensitivity (64.1% versus 41%) [12].In a prospective study of 150 male patients with COPD,a multivariate analysis showed that peak VO2 expressed asa continuous variable negatively correlated with survival(0.994; 0.992–0.996 P < 0.0001). Age was positively corre-lated with outcome albeit to a lesser degree than VO2 max(1.077; 1.010–1.149 P = 0.024) [13]. However, this studyexcluded patients with comorbidities, which are known toplay a key role in the outcome of patients with COPD. Thesame group that examined PaO2-slope in IPF [11] conducteda retrospective study of 195 patients and found that PaO2-slope correlated to survival more than PFT parameters [14].A measurement of functional capacity, beyond resting PFTsand 6-MWT, clearly adds valuable information to the care ofpatients with COPD, allowing improved prognostication andperhaps better timing for transplant listing.


(iii) Pulmonary Vascular Disease. In pulmonary hyperten-sion, CPET may be helpful to risk stratify patients into high-risk versus medium- or low-risk [15]. Specifically, in onestudy of 86 patients with primary pulmonary hypertension,a peak SBP of 120 mm or less and a peak VO2 equal to orless than 10.4 mL/kg/min have been established as a powerfulpredictor of survival threshold. Use of exercise data such asSBP and peak VO2 adds additional prognostic informationregarding overall survival outcomes, above resting hemody-namics such as right heart catheterization, or imaging such asechocardiogram. Even therapy did not predict survival in thisseries, strongly suggesting a key role for CPET in the manage-ment of patients with pulmonary vascular disease. In a recentreview Arena et al. described potential roles for CPET indiagnosed and undiagnosed pulmonary hypertension [31].Unfortunately, most of the studies included involved smallcohorts. Nevertheless, these small studies have consistentlyobserved a relationship between peak VO2, VE/VCO2 slope,and PETCO2 and survival [31].

(iv) Cystic Fibrosis. Data suggest that CPET may predictmortality in patients with cystic fibrosis. Specifically, VO2

max, VE/VO2, and peak work were found to be significantpredictors of mortality in a 5-year study of 92 adult patientswith cystic fibrosis however FEV1 values appeared to be abetter predictor of mortality than peak VO2 [16].

In striking contrast, an earlier prospective study of109 patients with cystic fibrosis found that peak VO2 wasa significant predictor of mortality, without independentcontribution of FEV1. Those with higher levels of aerobicfitness were more than three times likely to survive ascompared with those patients with lower levels of fitness,even after adjustment for other risk factors [32]. It remainstherefore unclear whether aerobic conditioning or restinglung function is the biggest predictors of outcome in cysticfibrosis. Recent evidence suggesting that muscle function isintrinsically abnormal in cystic fibrosis may perhaps be thelink to explain that apparent contradiction [33].

In one series of twenty-eight pediatric patients with cysticfibrosis, rate of decline in peak VO2 and peak VO2 of less than32 mL/min/kg was associated with increased mortality; peakVO2 of 45 mL/min/kg appeared to be associated with survival[17].

1.3.2. In Posttransplant. In the early 1990s, CPET per-formance was evaluated in patients who had undergonesingle and bilateral lung transplantation to evaluate peakVO2 [34]. Despite resolution of pretransplant respiratorysymptoms and essentially normal pulmonary function in thesix bilateral lung transplant recipients—with mild restrictiveabnormalities seen in the six patients with single lungtransplants—the maximum VO2 was markedly reducedone year after transplant. In bilateral lung recipients, VO2

maximum was 48.5% of predicted and only 44.2% predictedin single lung recipients. All subjects in this series hadsignificant respiratory reserve, indicating that there was noevidence of ventilatory limitation to exercise in either group.

Results of this study have been confirmed by subsequentstudies, one of which further demonstrated that CPET resultswere unchanged at 2 years after transplant in a cohort of 13subjects, [35] suggesting that additional improvement doesnot occur. The peak VO2 has been reported at 6 months,with decreases between 9 and 12 months after transplant ina group of eight bilateral transplant recipients as comparedto controls [36]. However, other studies suggest that peakVO2 may be at 12 or even 24 months [37]. In bilaterallung recipients, peak VO2 has been reported as low as31% of predicted, [38] and as high as 60% [39]. Heartrate and minute ventilation did not appear to be limitingexercise [40]. Interestingly, gains in VO2, typically a doublingto about 50% predicted VO2, may not be uniform acrossunderlying disease pathologies. One series of 153 subjectsevaluated prior to and following transplantation suggestedthat the most benefit was derived in patients with COPD,emphysema, or alpha-1-antitrypsin, while patients who hadinterstitial lung disease may have more modest improve-ments [41]. This may reflect differences in pretransplant levelof functioning however, the absolute VO2 after transplantagain appeared fairly fixed after transplant at 50% predictedVO2, regardless of pretransplant capacity. This same seriesalso failed to find a primary pulmonary or cardiac limit toexercise tolerance, with leg fatigue cited as overwhelminglyas reason for termination of exercise. CPET may thereforebe useful in identifying causes of poor exercise tolerance insubjects following lung transplant despite improvement ofPFT indices.

(i) Anemia. Anemia is common subsequent to transplanta-tion of solid organs, with much of the data coming from therenal transplant population. Despite preexisting renal diseasein some patients and medication effects on renal function,posttransplantation anemia does not appear to be solelymediated by renal dysfunction [42]. Losses during surgery,medication effects, immune-mediated factors, and differentforms of hemolysis can all additionally contribute [43].Several studies have shown that the percentage drop in VO2

max is 2/3 of the percentage drop in hemoglobin [44, 45].The effect of anemia, even mild anemia, may therefore beprofound when considered in conjunction with conditions(discussed below) that might affect tissue oxygen extraction.Understanding the effects of anemia on exercise capacityis important, since transfusion guidelines are increasinglystringent [46], and so other causes of declining functionalstatus in the presence of stable PFTs can be explored.

(ii) Chronic Muscle Deconditioning and Skeletal MuscleAbnormalities. Long-term pretransplant debilitation hasbeen felt to play a role in posttransplant exercise perfor-mance. However, even in the highest functioning pretrans-plant group, after transplant, peak VO2 reflects pretransplantperformance in the 153 patients followed by Bartels prior toand subsequent to transplantation [41]. Underlying diseasestate does not appear to play a role in absolute peakVO2 achieved in another series of fourteen single andeleven bilateral lung transplant recipients [47]. Subgroups


of patients, particularly those with COPD, showed greaterimprovements in VO2 max than those patients with IPF [41].Despite the younger age of patients with cystic fibrosis, agedoes not appear to play a role in posttransplant peak VO2

[41]. This may reflect skeletal muscle abnormalities inherentto CF patients, rather than a posttransplant effect [48].

Suggesting that there may be other factors at play in otherpopulations, Miyoshi’s study following six single and sixbilateral lung transplant recipients suggested that peak VO2 isnot significantly different after lung transplant as comparedto heart-lung recipients [34]. Indeed, consistent findingsclearly show that in posttransplant patients, peak VO2 is notlimited by ventilatory reserve, but instead, systemic oxygenextraction is abnormal [49, 50]. Specifically, peak cardiacoutput is normal, as much as 89% of predicted; there iseven evidence of beneficial right heart remodeling seen afterbilateral lung transplantation based on a prospective studyfollowing twenty subjects before and after transplantation[37]. Despite this, however, arterial-mixed venous oxygencontent difference at peak exercise was only half of expected,primarily due to inability of venous oxygen saturation todecline normally.

In one series comparing exercise capacity of nine lungtransplant recipients with that of eight healthy volunteers,ventilation, oxygen saturation, and mild anemia did notappear to account for decreased peak VO2 as comparedto healthy volunteers. Instead, quadriceps muscle pH andphosphorylation potential appeared to be reduced followingtransplant, suggesting that abnormalities of skeletal muscleoxidative capacity may be playing a role in decreased peakVO2 after transplant [51]. Indeed, another study of twenty-five subjects confirmed a correlation between quadricepsmuscle force after transplant and exercise capacity [52].The importance of posttransplant exercise and pulmonaryrehabilitation cannot be overstated, with multiple studiesshowing positive effects of rehabilitation on skeletal musclefunction and respiratory status, immediately after transplantand in long-term survivors of lung transplant, without clearbenefit for inpatient or outpatient rehabilitation [53–55].

(iii) Medications. Proximal myopathy due to corticos-teroids and peripheral skeletal muscle abnormalities dueto cyclosporine have all been proposed likely contributingfactors to reduced exercise capacity [56]. In particular, evena short course of 5 days of corticosteroids, as used foracute rejection, may have profound effects on respiratoryand skeletal muscle, with up to 45% of patients developingacute generalized weakness as measured in one series ofthirteen subjects after lung transplantation [57]. Anotherstudy evaluating muscle size and strength in six COPDpatients compared to six patients who had undergone single-lung transplantation for COPD found that despite similarstrength and muscle size, muscle endurance was lowerin the transplant population, pointing towards a possibleeffect of immunosuppressant medications, as suggested byanimal data [58, 59]. The calcineurin signaling pathwaymay play a role in signaling transition from fast-to-slowskeletal muscle fiber-type transition [60]. Inhibition of thispathway, therefore, could have important implications in

terms of peak work and exercise endurance in the post-transplant setting. This is of important clinical implications,particularly in patients whose lung function improves aftertransplant but their exercise tolerance does not correlate withthe improvement in lung function.

(iv) Effects of Native Lung in COPD. CPET has also beenevaluated in patients who have undergone single lungtransplantation for COPD [61]. VO2 max in six singlelung recipients with underlying COPD was similar to thatof six single lung recipients with underlying IPF, withoutevidence of ventilatory limitations to exercise. These findingsalleviate concerns over the hyperinflated native lung causingdecreased tidal volume, leading to ventilation perfusionmismatch and impaired gas exchange. This is of criticalimportance, since it should alleviate concerns over singlelung transplant in subjects with COPD [62] and should leadclinicians to focus on other causes of lack of symptomaticimprovement of subjects with COPD following lung trans-plant.

2. Summary

There clearly is a need for further prospective systematicinvestigation of the role of CPET in the pre- and post- lungtransplant period. The above findings suggest that CPETmay provide useful information prior to transplantationin various disease states, including risk stratification andprediction of mortality, in some cases beyond that ofpulmonary function tests and the 6-MWT. Additionally,usage of CPET after transplant has repeatedly demonstratedthat despite improvement in lung function, the primarylimitation to exercise appears to be at the level of oxygenextraction. If these contributing factors are better elucidated,further improvements in outcomes may occur, either byfurther optimizing factors in pretransplant setting or betterstratifying risk after transplant.

Acknowledgment

Supported by a grant from the National Institutes of Health/National Heart, Lung, and Blood Institute, K22 HL092223(to SE-C).

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Research Article

Validity of Reporting Oxygen Uptake Efficiency Slope fromSubmaximal Exercise Using Respiratory Exchange Ratio asSecondary Criterion

Wilby Williamson,1 Jonathan Fuld,2 Kate Westgate,1 Karl Sylvester,2

Ulf Ekelund,1 and Soren Brage1

1 Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Box 285, Addenbrooke’s Hospital,Cambridge CB2 0QQ, UK

2 Department of Respiratory Medicine, Cambridge University Hospitals, NHS Foundation Trust, Cambridge CB2 0QQ, UK

Correspondence should be addressed to Wilby Williamson, [email protected]

Received 24 December 2011; Revised 27 February 2012; Accepted 28 February 2012


Copyright © 2012 Wilby Williamson et al. This is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properlycited.

Background. Oxygen uptake efficiency slope (OUES) is a reproducible, objective marker of cardiopulmonary function. OUES isreported as being relatively independent of exercise intensity. Practical guidance and criteria for reporting OUES from submaximaltests has not been established. Objective. Evaluate the use of respiratory exchange ratio (RER) as a secondary criterion for reportingOUES. Design. 100 healthy volunteers (53 women) completed a ramped treadmill protocol to exhaustive exercise. OUES wascalculated from data truncated to RER levels from 0.85 to 1.2 and compared to values generated from full test data. Results. Mean(sd) OUES from full test data and data truncated to RER 1.0 and RER 0.9 was 2814 (718), 2895 (730), and 2810 (789) mL/minper 10-fold increase in VE, respectively. Full test OUES was highly correlated with OUES from RER 1.0 (r = 0.9) and moderatelycorrelated with OUES from RER 0.9 (r = 0.79). Conclusion. OUES values peaked in association with an RER level of 1.0. Sub-maximal OUES values are not independent of exercise intensity. There is a significant increase in OUES value as exercise movesfrom low to moderate intensity. RER can be used as a secondary criterion to define this transition.

1. Introduction

Exercise testing allows quantification of cardiopulmonaryfunction providing valuable diagnostic and prognostic data.Peak and maximal cardiopulmonary exercise testing are goldstandard modalities [1]. However, in clinical practice andfield research reporting peak exercise parameters can be com-promised by compliance and feasibility. There are a num-ber of extrinsic factors, including financial restraints andrisk mitigation protocols that can restrict exercise testing tosubmaximal intensities. Exercise testing in large-scale pop-ulation studies has to ensure high participant turnover andmaintain safety in often nonclinical, potentially resource-depleted environments. To maintain safety and efficiency,termination criteria are within the moderate, nonmaximalexercise intensity range. Tests stopped within 80–90% ofmaximal heart rate or when respiratory exchange ratio (RER)

reaches 0.9–1.0 [2, 3]. Terminating exercise within RER ran-ges of 0.9–1.0 places significant restrictions on the reportingof gas exchange data.

In clinical practice, the most frequently reported sub-maximal parameter is the ventilatory anaerobic threshold(VAT). However, reporting VAT is not without limitations,with potential for observer error or technical difficultieswhen defining values [4, 5]. These challenges are even morepronounced when using exercise tests with termination cri-teria of RER 0.9-1.0. Shorter test durations restrict thenumber of data points and intensity may not progress farenough beyond VAT to confidently report results using therecognised V-slope methods [6]. Under these conditions, itmay also be more difficult to accommodate irregularities inbreathing patterns. For example, hyperventilation that mightbe expected to settle as exercise progresses may compromisethe reporting of graphical submaximal data points. These


practical limitations provide an incentive to establish objec-tive, reproducible submaximal gas exchange parameters withfunctional and prognostic value.

There are a number of potential parameters that can bereported from submaximal gas exchange data ranging fromregressions of ventilation versus carbon dioxide exhalationto measures derived from oxygen uptake [7, 8]. The oxygenuptake efficiency slope (OUES) is a regression-derivedparameter from the relationship between log-transformedminute ventilation (VE) and oxygen uptake (VO2), with thecoefficient “a” from the regression VO2 = a logVE + b beingdefined as the OUES. The coefficient “a” represents the rateof change in VO2 in response to VE [9]. If an individualachieves a higher VO2 with only a small increase in VE, thiswill produce a higher OUES and this is taken to representmore efficient oxygen uptake. OUES is regarded as anobjective, reproducible marker of cardiopulmonary functioncalculated from sequential data points. Using sequential datapoints as opposed to time or intensity defined values has ledto the suggestion that OUES represents a composite valuefor cardiopulmonary function inclusive of the physiologicaltransition from low to vigorous intensity [9, 10].

In the context of maximal testing, OUES provides asimilar marker of function and prognosis as Peak VO2 [11–13]. However, the evidence that OUES remains relativelystable across moderate-to-high intensity exercise has pro-moted acceptance of OUES as a valid submaximal measureof function and disease prognosis [10]. Baba and coworkerswere first to report the use of OUES in cardiovascularpopulations and identify the relative stability of OUES inthe final quartile of a maximal test [9]. Hollenberg et al.then confirmed these results; OUES reported from 75% ofthe completed test differed by less than 2% of the OUEScalculated from the full test [12]. These seminal papersprovided the template to establish reporting criterion forsubmaximal OUES values and facilitate expansion into clin-ical practice. However, translation into the clinical domainhas been slow despite the literature continuing to grow insupport of OUES as a functional and prognostic parameterduring peak and maximal tests [13, 14]. The majority ofstudies continue to report OUES defined by percentage dataacknowledging that there is a strong correlation betweensubmaximal and full test results [15]. Lending support tothe statement that OUES is relatively independent of exerciseintensity but not defining reporting criterion. Pogliahgi etal. explored defining submaximal OUES with regards topercentages of predicted heart rate reserve [16]. Heart rateis commonly used in noninvasive exercise testing to defineintensity but the variance and potential error, especially inthe clinical sitting, is well established [17]. Overall, therehas been minimal progression in the literature on defininghow to practically use OUES with reference to submaximaltesting. This is potentially limiting the expansion of OUES inboth the clinical and research domains.

Clinically, there is rarely the luxury of collecting peakexercise data, and in the context of submaximal tests, it is dif-ficult to define percentage efforts. The same limitation holdsfor researchers working with large populations where fieldtesting is limited by feasibility to submaximal tests. In both of

these contexts OUES could be an ideal parameter to report.The objective of the current study is to report the reliability ofusing RER as a reporting criterion for OUES, exploring thequestion of whether there is a submaximal threshold belowwhich OUES is not valid or incurs significant error whencompared to true maximal data.

2. Methods

2.1. Participants. A total of 100 participants were recruitedfrom the Cambridge area (UK). Participants were free fromcardiopulmonary and metabolic diseases. Ethical approvalfor the study was obtained from the local research ethicscommittee. All participants provided written, informedconsent.

2.2. Study Procedure. Participants were asked to refrain fromeating, drinking (except water), smoking, and vigorous exer-cise for at least 2 hours before they arrived at the laboratory.Height and weight of participants in light clothing wererecorded using a rigid stadiometer and calibrated scales,respectively.

2.3. Treadmill Test. A Jaeger Oxycon Pro system was con-figured to control a motorized treadmill (HP Cosmo Pulsar4.0). The treadmill protocol was a nonindividualised rampprotocol adapted from an original epidemiological studyprotocol [18]. The original protocol was extended to includea 4th phase to ensure participants exercised to an exhaustiveintensity. Phase 1 (level walking) involved level walking withincreasing speed (3 min at 3.2 km/h and then accelerating at0.33 km·h−1 per min for the next 6 min), phase 2 (gradedwalking) consisted of brisk walking (5.2–5.8 km/h) withincreasing gradient (at a rate of 1.7% increased gradient/minfor 6 min), phase 3 (level running) involved level runningwith speed increasing from 9 to 12.5 km/h for 4.5 min(average acceleration of 0.78 km·h−1 per min), and phase 4(uphill running) in which the gradient increased by 0.5%and the speed increased by 0.25 km·hr−1 every 15 secondsuntil exhaustion. Transition between phases 2 and 3 was firsta change in gradient by −10.2% over 30 seconds (now level),followed by a change in speed by 3.2 km/h over 30 seconds.

Continuous recording of respiratory gas exchange para-meters was taken during the treadmill test and for 2 minutesduring recovery after exercise test termination. Participantswere asked to exercise until maximal exertion. Clinical indi-cators for terminating the treadmill test were onset of angi-na or angina-like symptoms or signs of poor perfusionincluding light-headedness, confusion, ataxia, pallor, cyano-sis, nausea, or cold and clammy skin. In addition, testswere stopped following physical or verbal manifestations ofsevere fatigue, the volunteer requesting to stop despite verbalencouragement.

2.4. Respiratory Gas Analysis. Gas exchange data were ac-quired breath by breath and averaged over 20-second inter-vals for generation of graphic data and regression analysis


for OUES and VE/VCO2 [7]. Peak VO2 and peak respiratoryexchange ratio was expressed as the highest averaged valuesover sequential 30-second periods obtained from completeexercise data [11, 19]. Breath by breath averaging was ex-panded to 30 seconds in an attempt to reduce the effect oftransient fluctuations in RER when calculating RER trun-cated OUES. RER data for each individual test was plottedagainst time to review trend. The VAT was determined by theV-slope method [6].

2.5. Determination of Oxygen Uptake Efficiency across ExerciseDuration. OUES was calculated from complete and trun-cated gas exchange data according to a series of criteria.

(1) Defined as percentile of the complete test. OUEScalculated from data taken from the first 25%, 50%,and 75% of time defined test data.

(2) Defined according to Ventilatory Anaerobic Thresh-old (VAT). Calculating OUES using data from start oftest until time of VAT.

(3) Defined according to increasing Respiratory Exchan-ge Ratio (RER). OUES values were calculated fromtest data limited to RER ≤ 0.85, RER ≤ 0.90, RER ≤0.95, RER ≤ 1.00, RER ≤ 1.10, and RER ≤ 1.20.

2.6. Gas Exchange Reference Values. Mean Peak VO2, OUES,and VEVCO2 results were compared with predicted referenceranges accounting for age, weight, height, and sex [7, 11, 19].

Peak VO2 prediction equation:

Men: VO2max = [50.2 − (0.394 (age (yrs))],

Women: VO2max = [42.83 − (0.371 (age (yrs))].

OUES prediction equations:

Men: OUES [L/min/log(L/min)] = [−0.61 − 0.032(age (yrs)) + 0.023(height (cm)) + 0.008 (weight(kg))],

Women: OUES [L/min/log(L/min)] = [−1.178 −0.032 (age (yrs)) + 0.023 (height (cm)) + 0.008(weight (kg))].

VE/VCO2 prediction equations:

Men: VE/VCO2 slope = 34.5 + 0.1 (age (yrs)) − 0.05(height) (cm),

Women: VE/VCO2 slope = 35.5 + 0.1(age (yrs)) −0.05(height) (cm).

2.7. Statistics. Statistical analysis was performed using Sta-taIC 11 (Stata Corp LP, TX).

Summary statistics for continuous variables are ex-pressed as means with standard deviation (+SD). Relativeand absolute agreement between the different measures ofOUES were reported via correlation and Bland-Altmanagreement analysis (including root mean square error).When displayed graphically, mean values are presented with

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Figure 1: Mean OUES values between 50th percentile and full testdefined by submaximal criteria. VAT Ventilatory Anaerobic Thre-shold, RER respiratory exchange ratio. Percentage of test defined bytime to complete full test.

standard error bars. Subgroup analysis was performed usingStudent t-test and regression analysis.

3. Results

One hundred participants (53 women) underwent cardio-pulmonary exercise testing (CPET) recording Peak VO2,VAT, OUES, and VE/VCO2 slope values. The group werehealthy volunteers. The mean age was 41.4 yrs (range 22 to65 yrs). Mean weight was 70.3 kg (range 44 to 109 kg) andmean height was 170.5 cm (range 144 to 195 cm).

Mean test duration was 1228 seconds (sd 152 s). Meantime to VAT was 821 seconds associated with an RER of 0.92(sd 0.07). Only one individual passed the anaerobic thresholdprior to 600 seconds. No participants were limited by clinicalsymptoms. Mean Peak VO2 was 39.8 (sd 8.8) mL/kg/min;mean Peak RER was 1.19 (sd 0.11). Mean OUES using allavailable test data was 2814 (sd 718) (mL/min/logVE); meanVE/VCO2 slope using all test data was 30.2 (sd 4.15). Exercisecharacteristics from the complete test are shown in Table 1.The results are consistent with high cardiovascular fitnesswithin this sample; measured Peak VO2 was 131.5% of theexpected value.

3.1. OUES Values during Cardiopulmonary Exercise Testing.OUES values from complete test data were strongly corre-lated with Peak VO2 (r = 0.86, P < 0.0001). OUES valueswere not independent of exercise intensity. The exercise testhad to progress beyond 50% of the max duration andthe ventilatory anaerobic threshold before OUES values ap-proached that generated from full test data. Compared tofull test data, the correlation increased and the error reducedfor OUES values in association with increasing RER [Figures2(a)–2(c)]. Using root mean squared error as a relativeindicator of accuracy when reporting submaximal OUES,there is over 40% improvement reporting values from in-tensity corresponding with RER 1.0, compared with RER0.85 (Figure 1). In the current study, OUES values peak inassociation with an RER of 1.0 (Table 2, Figure 1).


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Figure 2: OUES values reported from exercise intensity defined by RER with associated correlation and relative error compared to full testdata. Mean values are reported with standard error bars. (a) presents mean values of OUES generated from data associated with increasingRER. (b) presents the R value from Pearson Correlation comparing submaximal RER-defined exercise data with complete data. (c) showsthe root mean squared error (RMSE) from the regression of the submaximal RER data versus complete exercise data. RMSE is presented asa value of the relative error reporting submaximal values as opposed to values from full test data.

4. Discussion

Using peak and maximal exercise tests, OUES has beenshown to be a valid, reproducible parameter of cardiopulmo-nary function and prognosis [10]. It is believed that OUESparameterises the relationship between peripheral oxygendemand and the associated increases in cardiac output andalveolar ventilation. In a noninvasive setting, it provides acomposite value for the efficiency of the cardiopulmonarysystem to oxygenate and perfuse peripheral tissues and sub-sequent oxygen utilisation [10].

Recent OUES literature explores the test-retest validityand functional outcomes in new clinical cohorts reportingfavourable results [20, 21]. This is supporting a potentialtransition in preoperative and respiratory clinical practice touse OUES in preference to the ventilatory anaerobic thre-shold. However, the use of OUES largely remains confined tothe research field. This may change if the value and reliabili-ty of OUES as a submaximal measure is reported in the con-text of secondary reporting criterion. In noninvasive exercisetesting, clinicians and physiologists commonly use predeter-mined criteria including heart rate percentages, RER, andvisual analogy scores to validate the test. However, there are

no established criteria for reporting OUES from submaximaltests.

The stability of OUES in submaximal ranges has beennoted since it was first introduced in the literature [9]. Thisunderstanding has expanded to recognise that OUES from50% of a completed test is comparable to the full test OUESin both healthy and noncyanotic disease groups [22]. Davieset al. were the first to report that OUES from the first50% of a modified Bruce protocol exercise test could beused as a prognostic indicator in heart failure [13]. Thiswas subsequently confirmed by Arena et al. [14]. In thesestudies the difference between OUES from the first 50%of a maximal test and full test OUES was 1% and 2.6%,respectively. In the current study, the difference was 7.5%between full test OUES and OUES from 50% of a completetest. Not surprisingly, the difference between OUES andsubmaximal measures increased with increasing truncationof data. OUES values reported using data from the firstquarter of the test could be as much as 35% lower than thehighest values and correlation with full test values was low(r = 0.35) (Figure 3).

The current study has presented the characteristics ofOUES in relation to RER values. Using RER criteria, the


Table 1: Gas exchange parameters using the complete test to exhaustion data (peak exercise test data).

Variable Mean Standard deviation

Peak RER 1.19 0.11

Peak VO2 mL/min 2796 783

Peak VO2 mL/kg/min 39.8 8.8

Peak VO2 as percentage of predicted VO2max 131.5 20.4

VAT mL/kg/min 23.6 4.5

OUES mL/min per 10-fold ventilation increase 2814 718

OUES mL/min/kg per 10-fold ventilation increase 40.1 8.2

% Predicted OUES 130.7 34.2

VE/VCO2 slope 30.2 4.15

Predicted VE/VCO2 slope 30.6 1.56

Predicted values for VO2max, OUES, and VE/VCO2 slope generated from prediction equations. Abbreviations: RER, respiratory exchange ratio; VO2, oxygenconsumption; VE, minute ventilation; VCO2, carbon dioxide production; VAT, ventilatory anaerobic threshold; OUES oxygen uptake efficiency slope.

Table 2: OUES values generated from restricting data points by percentile, respiratory exchange ratio, and ventilatory anaerobic threshold.

Submax criterion Mean OUES Standard deviation

Percentile of test data

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0–50% 2604 750

0–75% 2832 667


<0.85 2687 860

<0.90 2810 789

<0.95 2871 759

<1.0 2895 730

<1.10 2863 719

<1.20 2824 734

Up to ventilatory anaerobic threshold 2672 687

Complete test data 2814 718

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Figure 3: Correlation between submaximal and full test OUES val-ues reported from percentiles of test data.

OUES has been identified to peak at submaximal intensities.The highest mean OUES value occurs in association with anRER of 1.0.

This is consistent with the reported patterns of oxygenuptake efficiency using other parameters. Sun et al. identifiedthat the highest values for the ratio of minute ventilationto oxygen uptake (VE/VO2) occurred in submaximal ranges[11].

Using RER criteria, there is less than a 3% difference bet-ween OUES values reported from full test data and data fromstart of test to RER 1.0 (Table 2). Bland-Altman plots help

to visualise the relationship between complete data OUESand results from RER 0.9, 0.95, and 1.1 (Figure 4). Meandifference between OUES from complete data and data uptill RER 0.9 was less than 0.2% (3.86) with a correlation rvalue of 0.79. However, it should be noted that the limits ofagreements contracted by over 50% when reporting OUESfrom RER 1.1 (limits −505 to 407) compared to using RER0.9 (limits −961 to 969). Similar findings were reportedby Van Laethem et al. when considering submaximal per-centile data. Van Laethem also identified that test-retestreliability increased when OUES was calculated from peakexercise compared to submaximal [15]. Therefore, there arethresholds of reliability and reproducibility for reportingsubmaximal OUES values. In the current study, reportingOUES as RER increases above 0.9 provides values morereflective of the full test value.

4.1. The Validity of RER as Secondary Criterion for ReportingOUES. At rest and low intensity exercise, there are multipleextrinsic determinants of the RER ranging from dietaryintake to previous exercise load. However, the determinants


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Figure 4: Bland Altman plots comparing full test data and data truncated using RER 0.9, 0.95, and 1.1 as cutoff criteria.

of RER become intrinsic to exercise-induced metabolism asintensity increases between 25% and 70% of peak work rate[23].

In the current study, phase 1 exercise (level walking)generated a mean RER of 0.85 at a relative intensity of 40%of peak exercise (mean VO2 16.0 mL/kg/min sd 2.4), whilephase 3 (level jogging/running) generated a mean RER of0.94 reaching a relative intensity of 71% peak VO2 (meanVO2 28.4 mL/kg/min sd 3.5). These results are comparableto those of Goedecke who reported RER values of 0.86 (sd0.037) and 0.976 (sd 0.043) during steady state 25% and 70%peak work rate (41% and 80% VO2 Peak) [23]. This wouldsuggest that provided exercise intensity progresses beyond40% of the predicted peak value of VO2 that RER can be usedas valid criterion for identifying moderate intensity exercise.

RER is commonly used as a secondary criterion for sa-tisfying the attainment of Peak VO2. This has recently beenchallenged with reports that there could be as much as a27% difference in the Peak VO2 values recorded between anRER of 1.1 and maximal data [24]. The current study wouldsuggest that the same concerns do not exist for reportingOUES. There is less than a 3% difference in comparisonof the mean values across the RER range from 0.9 to 1.2(Figure 2(a)).

In the current study, four individuals (4%) had peak RERless than 1.0 and there were a total of 16 individuals withpeak RER values less than 1.1. It could be argued that these 16individuals did not achieve true peak exercise. This prompteda review of the exercise characteristics of this group. Meantest duration completed by the group was 1087 seconds(sd 149). Although this test duration is lower than the fullstudy group, all these low peak RER individuals continuedto exercise beyond the start of phase 3 and 5 individuals en-tered the final stage of the protocol. The mean VAT of thegroup was 1516 mL/min (sd 509); this was not significantlydifferent from the group with peak RER > 1.1. From thegraphical data, 7 of the 16 individuals in this lower peakRER group attained a plateau in the VO2 curve includingthe individual with the lowest peak RER. The exercisecharacteristics of this subgroup do not raise immediate con-cern about the validity of these test results. They representthe normal distribution of peak RER identified by Goedeckeet al. [23] and highlight the difficulties of defining maximaltests under noninvasive conditions.

As a sub-analysis the study group was divided into threegroups dependent on the peak RER: group 1 peak RER lessthan 1.1 (n = 16), group 2 peak RER 1.1 to 1.2 (n = 42),and group 3 peak RER > 1.2 (n = 42). Statistical analysis of


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Figure 5: Subgroup analysis of 84 individuals with Peak RER > 1.1. (a) Mean OUES from exercise data limited by RER criteria. Group datafor 84 individuals with peak RER > 1.1. (b) Correlation between OUES defined by RER criteria. Group data for 84 individuals with peakRER > 1.1.

the group OUES values identified no significant differencesbetween the group means. Group 1 mean OUES 2711 (sd970), Group 2 mean OUES 2742 (sd 688), and Group 3 meanOUES 2926 (sd 636). Combining groups 2 and 3 to representa maximal group (peak RER > 1.1), a further sensitivityanalysis was performed identifying the same patterns asFigures 2(a) and 2(b) with regards to change in mean OUESwith RER. OUES peaks in association with RER 1.0 (mean2939 sd 682) with an associated r value of 0.89 compared tofull test results, reflecting the pattern from the whole group(Figures 5(a) and 5(b)).

These subanalyses show that maximal OUES values donot vary significantly with the normal distribution of peakRER in a fit healthy cohort. There are not the same concernsabout reporting OUES using RER criteria as there is a report-ing peak VO2. The normal distribution of RER may raiseconcern that low peak RER individuals would be excluded ifapplying threshold exclusion criteria. However, in the presentstudy only one individual had a peak RER less than 0.97. Fur-ther research is required to explore the characteristics anddeterminants of peak OUES at submaximal intensities; thehighest value in this study occurred at RER 1.0 not withmaximal data. The clinical relevance of using the highestvalue of OUES has not been explored. One of the rationalesfor using OUES is that it provides a composite value ofcardiopulmonary fitness from across the spectrum of exer-cise intensity. Therefore, the assumption is that OUES re-ported from full test data would remain superior as a pro-gnostic marker. These considerations require further formalinvestigation using invasive physiological measures.

4.2. Limitations. This study explored the validity of reportingsubmaximal values for OUES in healthy adults. It provides anindication of the variability in submaximal reporting, but itis limited by not being inclusive of a disease population. Therelatively high peak VO2 suggests that the sample is biasedtowards inclusion of fitter individuals. It could be arguedthat using RER as reporting criterion with submaximal datawould need a prospective validation within a disease popula-tion.

The RER levels defining the submaximal criteria werearbitrarily selected. Mean RER in the first 25% of the test

was not below 0.85. To accurately define the cascade of phys-iological events in association with OUES, invasive monitor-ing and sampling would be required.

When combined with a rest test, the treadmill test pro-tocol was designed to be representative of the intensity spec-trum of physical activity encountered in daily living. Therise in intensity (ramp slope) is slower in comparison toother established protocols. This may affect the validity of theresults; however, gas exchange patterns on the Wassermanplots were consistent with protocols of shorter test duration[8].

5. Conclusion

Cardiopulmonary exercise testing is a valuable research andclinical tool. Sub-maximal exercise testing produces a dis-crete set of limitations on the parameters that can be relia-bly reported. The ventilatory anaerobic threshold is oftenthe preferred submaximal parameter. However, using VATintroduces potential observational error and can be timeconsuming to validate. The oxygen uptake efficiency slopesare validated as a prognostic indicator of cardiopulmonarydisease that has the advantage of being objectively derived.

Previous studies reported that OUES can be regardedas independent of exercise intensity. This study identifiesthat there are certain caveats to that statement. OUES val-ues change significantly during the transition from low tomoderate intensity. There are definable reliability thresholdsabove 40% of peak exercise intensity, above 50% of timedefined test, and in proximity of the VAT. During a rampexercise protocol using RER as a reporting criterion, thecurrent results predict higher confidence and reliability in anOUES value reported above an RER threshold of 1.0

Acknowledgments

The authors thank all participants for giving up their timeand the field team of the MRC Epidemiology Unit for collec-ting the data, also special thanks to Stephanie Mayle for allher help and support. This paper was funded by the MedicalResearch Council.


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[22] A. Giardini, S. Specchia, G. Gargiulo, D. Sangiorgi, and F. M.Picchio, “Accuracy of oxygen uptake efficiency slope in adultswith congenital heart disease,” International Journal of Cardio-logy, vol. 133, no. 1, pp. 74–79, 2009.

[23] J. H. Goedecke, A. St Clair Gibson, L. Grobler, M. Collins,T. D. Noakes, and E. V. Lambert, “Determinants of the var-iability in respiratory exchange ratio at rest and during exer-cise in trained athletes,” American Journal of Physiology—En-docrinology and Metabolism, vol. 279, no. 6, pp. E1325–E1334,2000.

[24] D. C. Poole, D. P. Wilkerson, and A. M. Jones, “Validityof criteria for establishing maximal O2 uptake during rampexercise tests,” European Journal of Applied Physiology, vol. 102,no. 4, pp. 403–410, 2008.


Clinical Study

Abnormalities of the Ventilatory Equivalent forCarbon Dioxide in Patients with Chronic Heart Failure

Lee Ingle,1 Rebecca Sloan,1 Sean Carroll,1 Kevin Goode,2

John G. Cleland,2 and Andrew L. Clark2

1 Department of Sport, Health & Exercise Science, University of Hull, Cottingham Road, Kingston-upon-Hull HU6 7RX, UK2 Department of Cardiology, Hull York Medical School, Daisy Building, University of Hull, Castle Hill Hospital, Cottingham,Kingston-upon-Hull HU16 5JQ, UK

Correspondence should be addressed to Lee Ingle, [email protected]

Received 21 December 2011; Accepted 9 February 2012

Academic Editor: Roland Wensel

Copyright © 2012 Lee Ingle et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Introduction. The relation between minute ventilation (VE) and carbon dioxide production (VCO2) can be characterised by theinstantaneous ratio of ventilation to carbon dioxide production, the ventilatory equivalent for CO2 (VEqCO2). We hypothesisedthat the time taken to achieve the lowest VEqCO2 (time to VEqCO2 nadir) may be a prognostic marker in patients with chronicheart failure (CHF). Methods. Patients and healthy controls underwent a symptom-limited, cardiopulmonary exercise test (CPET)on a treadmill to volitional exhaustion. Results. 423 patients with CHF (mean age 63 ± 12 years; 80% males) and 78 healthycontrols (62% males; age 61± 11 years) were recruited. Time to VEqCO2 nadir was shorter in patients than controls (327± 204 sversus 514± 187 s; P = 0.0001). Univariable predictors of all-cause mortality included peak oxygen uptake (X2 = 53.0), VEqCO2

nadir (X2 = 47.9), and time to VEqCO2 nadir (X2 = 24.0). In an adjusted Cox multivariable proportional hazards model, peakoxygen uptake (X2 = 16.7) and VEqCO2 nadir (X2 = 17.9) were the most significant independent predictors of all-cause mortality.Conclusion. The time to VEqCO2 nadir was shorter in patients with CHF than in normal subjects and was a predictor of subsequentmortality.

1. Introduction

Cardiopulmonary exercise testing (CPET) is used to stratifyrisk in patients with cardiorespiratory disease [1]. In patientswith chronic heart failure (CHF), the normal linear relationbetween ventilation (VE) and carbon dioxide production(VCO2) is maintained, but the slope of the relation isgreater than normal, so that, for a given volume of carbondioxide production, the ventilatory response is greater [2–6].Another way of characterising the relation between minuteventilation and carbon dioxide production is the instanta-neous ratio of ventilation to carbon dioxide production, theventilatory equivalent for CO2 (VEqCO2). Recently, we haveshown that the lowest VEqCO2 (VEqCO2 nadir) providesgreater prognostic value than other CPET-derived variablesin patients with suspected CHF [7]. Other studies havereported that the lowest VEqCO2 has similar prognostic

power to the VE/VCO2 slope derived from the whole ofexercise [8].

During an incremental CPET, as exercise intensityincreases, both VCO2 and VE increase linearly. However,VEqCO2 falls at the onset of exercise, possibly due to areduction in dead space ventilation. Beyond the ventilatorycompensation point (VCP), lactic acid production causes anincrease in ventilation relative to carbon dioxide production,and thus the VEqCO2 rises. Although patients with CHFhave the same pattern of VEqCO2 during exercise as normalsubjects, with more severe heart failure, the increase inVEqCO2 towards the end of exercise becomes more marked[9]. In the most severely affected patients, VEqCO2 increasesfrom the start of exercise [9]. We hypothesised that the timetaken to reach VEqCO2 nadir would be shorter in patientswith CHF compared to healthy controls and thus may be animportant prognostic indicator.


2. Methods

The Hull and East Riding Ethics Committee approvedthe study, and all patients provided informed consent.We recruited consecutive patients referred to a commu-nity heart failure clinic with symptoms of breathlessness(NYHA functional class II-III) who were found to haveleft ventricular systolic dysfunction on investigation. Clinicalinformation obtained included past medical history anddrug and smoking history. Clinical examination includedassessment of body mass index (BMI), heart rate, rhythm,and blood pressure. Patients were excluded if they wereunable to exercise because of noncardiac limitations (such asosteoarthritis) or had significant respiratory disease (definedas a predicted FEV1 < 70%).

Heart failure was defined as the presence of currentsymptoms of HF, or a history of symptoms controlledby ongoing therapy, and impaired left ventricular systolicfunction. Left ventricular function was determined from 2Dechocardiography which was carried out by one of threetrained operators. Left ventricular function was assessed byestimation on a scale of normal, mild, mild-to-moderate,moderate, moderate-to-severe, and severe impairment andwas assessed by a second operator blind to the assessmentof the first; where there was disagreement on the severity ofleft ventricular (LV) dysfunction, the echocardiogram wasreviewed jointly with the third operator and a consensusreached. Where possible, left ventricular ejection fraction(LVEF) was calculated using the Simpson’s formula frommeasurements of end-diastolic and end-systolic volumes onapical 2D views, following the guidelines of Schiller andcolleagues [10], and LVSD was diagnosed if LVEF was≤45%.When LVEF could not be calculated, LVSD was diagnosedif LVEF ≤45 or there was at least “mild-to-moderate”impairment.

Patients underwent a symptom-limited, maximal CPETon a treadmill using the Bruce protocol modified by theaddition of a Stage 0 (2.74 km·h−1 and 0% gradient) at theonset of exercise. Metabolic gas exchange was measured withan Oxycon Delta metabolic cart (VIASYS Healthcare Inc.,Philadelphia, PA, USA). Peak oxygen uptake (pVO2) wascalculated as the average VO2 for the final 30 s of exercise.The ventilatory anaerobic threshold (AT) was calculated bythe V-slope method [11]. The gradient of the relationshipbetween VE and VCO2 (VE/VCO2 slope) was calculated bylinear regression analysis using data acquired from the wholetest. The VEqCO2 relation was plotted from start to thefinish of exercise. Each consecutive 30-second reading wasaveraged, and the lowest point was defined as the VEqCO2

nadir [7]. The time taken to reach the VEqCO2 nadir wasreported in seconds (s). The peak respiratory exchange ratio(pRER) was calculated as the mean VCO2/VO2 ratio forthe final 30 s of exercise. For comparative purposes, we alsoincluded a healthy control group who had no evidence ofcardiac, respiratory, or musculoskeletal limitation. Healthycontrols were randomly invited to participate from two localGP practices.

2.1. Statistical Analysis. We used SPSS (version 17.0) forstatistical analysis. Continuous variables are presented as

mean ± SD, and categorical data are presented as per-centages. Continuous variables were assessed for normalityby the Kolmogorov-Smirnov test. An arbitrary level of 5%statistical significance was used throughout (two tailed).An independent t-test was used to measure differencesbetween CHF patients and healthy controls. All survivorswere followed for a minimum of 12 months, and we thereforegive the probability of 12-month survival. Receiver operatorcharacteristic (ROC) curves were used to identify the valueof the strongest predictor variables of survival to 12 months.We reported the area under the curve (AUC) with 95%confidence intervals (CI), sensitivity, specificity, and optimalcut-points in our ROC analysis. To define the optimal cut-point, we used the point closest to the upper left corner ofthe ROC curve, often known as the (0, 1) criterion.

All baseline variables (Table 1) were entered as potentialunivariable predictors of mortality using Cox analysis, andwe adjusted for age, sex, BMI, aetiology of heart failure,and severity of LV dysfunction (none, trivial, mild, mild-to-moderate, moderate, moderate-to-severe, severe). Modelbuilding was based on backward elimination (P value forentry was <0.05; P value for removal >0.1). A multivari-able Cox proportional hazards model using the backwardlikelihood ratio method was used to identify independentpredictors of all-cause mortality from all significant candi-date predictor variables. The outcome measure was all-causemortality. Kaplan-Meier survival curves were plotted for thestrongest candidate predictors; data were dichotomised byoptimal cut-points.

3. Results

423 patients with CHF (mean age 63 ± 12 years; 80%males; LVEF 36± 6%; peak VO2 22.3 ± 8.1 mL·kg−1·min−1;VE/VCO2 slope 34± 8) were included in the study. Of these,75% were taking ACE inhibitors, 77% beta blockers, and 67%loop diuretics. Seventy eight healthy subjects (62% males;age 61 ± 11 years) were recruited as a control group. Thehealthy controls had a higher peak oxygen uptake, lowerVE/VCO2 slope, and lower VEqCO2 nadir (Table 1). Timeto VEqCO2 nadir was shorter in patients than controls (327± 204 s versus 514 ± 187 s; P = 0.0001) but was similar as apercentage of the total exercise duration in both groups (55±23% versus 60±17%; P = 0.077). We performed a subgroupanalysis in 62 NYHA class III patients and found that thetime to VEqCO2 nadir was significantly lower (199 ± 59 s)compared to other less symptomatic patients (344 ± 202 s;P < 0.0001). We also performed a subgroup analysis by sexand found that the time to VEqCO2 nadir was very similarbetween males (327±209 s) and females (328±94 s; P > 0.05;n = 85).

In patients, time to VEqCO2 nadir correlated with age(r = −0.17; P = 0.0001) and LVEF (r = 0.24; P = 0.0001)but was not associated with BMI (r = 0.001; P = 0.98). Timeto VEqCO2 nadir correlated with peak oxygen uptake (r =0.59; P = 0.001) and showed an inverse association with bothVE/VCO2 slope (r = −0.55; P = 0.001) and VEqCO2 nadir(r = −0.56; P = 0.001). Scatter plots showing the associationbetween time to VEqCO2 nadir, peak oxygen uptake, and


Table 1: Baseline clinical characteristics between CHF patients andhealthy controls.

Variable(mean ± SD)

CHFpatients

Healthycontrols

P value∗

N 423 78 —

Males (%) 80 62 0.0001∗

Age (years) 63 (12) 61 (11) 0.529

BMI (kg·m−2) 28 (5) 26 (3) 0.001∗

LVEF (%) 36 (6) 60 (6) 0.0001∗

pVO2 (mL·kg−1·min−1) 22.3 (8.1) 36.2 (8.8) 0.0001∗

VE/VCO2 slope (full) 33.8 (7.7) 27.7 (3.0) 0.0001∗

VEqCO2 nadir 32.4 (6.2) 26.5 (3.0) 0.0001∗

Time to VEqCO2

nadir(s)327 (514) 514 (187) 0.0001∗

AT (mL·kg−1·min−1) 15.2 (5.7) 23.4 (6.6) 0.0001∗

Peak RER 1.07 (0.10) 1.08 (0.06) 0.223

Exercise duration (s) 564 (250) 881 (256) 0.0001∗

Heart rate (rest)(beats·min−1)

76 (15) 72 (12) 0.079

Heart rate (peak)(beats·min−1)

136 (30) 165 (20) 0.0001∗

Systolic BP (rest)(mmHg)

137 (25) 148 (20) 0.0001∗

Systolic BP (peak)(mmHg)

172 (36) 199 (22) 0.0001∗

Diastolic BP (rest)(mmHg)

84 (15) 90 (9) 0.0001∗

Diastolic BP (peak)(mmHg)

93 (22) 101 (21) 0.003∗

Loop diuretic (%) 67 — —

ACE-I (%) 75 — —

Beta-blocker (%) 77 — —

BMI: body mass index; LVEF: left ventricular ejection fraction; pVO2: peakoxygen uptake; ACE-I: ACE inhibitor; peak RER: peak respiratory exchangeratio; AT: anaerobic threshold; BP: blood pressure; ∗differences betweenCHF and healthy controls, P < 0.05.

VE/VCO2 slope in patients and controls are shown inFigures 1 and 2.

One hundred and eighteen patients (28%) died duringfollowup. The median followup in survivors was 8.6 ±2.1 years. Univariable predictors of outcome derived fromCPET are shown in Table 2. With the exception of restingheart rate, all candidate variables were significant univariablepredictors. The strongest univariable predictors of all-causemortality were peak oxygen uptake (χ2 = 53.0), VEqCO2

nadir (χ2 = 47.9), VE/VCO2 slope (χ2 = 31.7), and timeto VEqCO2 nadir (χ2 = 24.0). In a Cox multivariableproportional hazards model adjusted for age, sex, BMI, andseverity of LV dysfunction, peak oxygen uptake (χ2 = 16.7;HR = 0.91; 95% CI 0.88–0.95; P = 0.0001) and VEqCO2

nadir (χ2 = 17.9; HR = 1.12; 95% CI 1.04–1.20; P =0.0001) were the most significant independent predictors ofmortality.

0

200

400

600

800

1000

1200

0 10 20 30 40 50 60 70

Controls

Patients

.

Peak oxygen uptake (mL·kg−1·min−1)

Tim

e to

VE

qCO

2n

adir

(se

con

ds)

Patients: y = 20.2x − 91.1; R2 = 0.29

Controls: y = 10.2x + 143.2; R2 = 0.24

Figure 1: Relation between time to VEqCO2 nadir and peak oxygenuptake in patients with CHF and controls.

0

200

400

600

800

1000

1200

20 25 30 35 40 45 50 55 60 65 70

Patients

VE/VCO2 slope

Tim

e to

VE

qCO

2n

adir

(se

con

ds)

Patients: y = 14.6x + 821.9; R2 = 0.3

Controls: y = −17.3x + 993.9; R2 = 0.08

Controls

Figure 2: Relation between time to VEqCO2 nadir and VE/VCO2

slope in patients with CHF and controls.

ROC curve analysis of the relation between time toVEqCO2 nadir (and both VEqCO2 nadir and peak VO2)and all-cause mortality at 12 months is shown in Figure 3.Time to VEqCO2 nadir (AUC = 0.75; P < 0.0001; 95% CI= 0.67–0.84; sensitivity = 81; specificity = 62; optimal cut-point = 250 s); VEqCO2 nadir (AUC = 0.81; P < 0.0001;95% CI = 0.74–0.89; sensitivity = 86; specificity = 62;optimal cut-point = 33); peak VO2 (AUC = 0.76; P <0.0001; 95% CI = 0.67–0.85; sensitivity = 86; specificity =57; optimal cut-point = 20 mL·kg−1·min−1) were similar intheir relation to all-cause mortality at 12 months. Optimalcut-points determined from ROC analysis were used toconstruct Kaplan-Meier survival curves for time to VEqCO2

nadir (Figure 4), VEqCO2 nadir (Figure 5), and peak VO2

(Figure 6).


Table 2: Unadjusted univariable predictors of outcome (in order of Chi-square value).

Variables P value HR 95% CI Chi-square

Peak oxygen uptake 0.0001 0.891 0.862 0.920 53.0

VEqCO2 nadir 0.0001 1.095 1.068 1.122 47.9

VE/VCO2 slope 0.0001 1.060 1.041 1.079 31.7

Time to VEqCO2 nadir∗ 0.0001 0.705 0.523 0.905 24.0

Heart rate at peak exercise 0.0001 0.995 0.978 0.992 18.5

Systolic blood pressure (rest) 0.001 0.986 0.978 0.994 12.0

Diastolic blood pressure (rest) 0.02 0.977 0.963 0.991 9.3

Heart rate (rest) 0.744 1.002 0.990 1.014 0.1

HR: hazard ratio; 95% CI: 95% confidence intervals; ∗adjusted HR associated with 10 s increase in time to VEqCO2 nadir.

10.80.60.40.20

1

0.8

0.6

0.4

0.2

0

1-specificity

Sen

siti

vity

VEqCO2 nadir

Time to VEqCO2 nadir

Peak oxygen uptake

Figure 3: Receiver operating characteristic curve showing value ofVEqCO2 nadir, time to VEqCO2 nadir, and peak oxygen uptake forpredicting all-cause mortality at 12 months. VEqCO2 nadir: AUC =0.81; P < 0.0001; 95% CI = 0.74–0.89; sensitivity = 86; specificity =62; optimal cut-point = 33; time to VEqCO2 nadir: AUC = 0.75;P < 0.0001; 95% CI = 0.67–0.84; sensitivity = 81; specificity = 62;optimal cut-point = 250 s; peak VO2: AUC = 0.76; P < 0.0001; 95%CI = 0.67–0.85; sensitivity = 86; specificity = 57; optimal cut-point= 20 mL·kg−1·min−1.

4. Discussion

We have shown that the time to VEqCO2 nadir is significantlylower in patients with CHF compared to controls. To ourknowledge, no previous study has evaluated the prognosticvalue of time to VEqCO2 nadir. Sun and colleagues [12]showed that the lowest VEqCO2 (VEqCO2 nadir) was themost stable marker of ventilatory inefficiency in healthycontrols. During maximal exercise testing, the VEqCO2 nadirwas achieved at around the ventilatory anaerobic thresholdand occurred during “moderate” exercise intensity. BothVE and VCO2 are linearly related up to the ventilatory

0 20 40 60 80 100

Cu

m s

urv

ival

Alive (months)

< 250 s

> 250 s

1

0.8

0.6

0.4

0.2

0

Time to VEqCO2 nadir

log rank χ2 = 23.9; P < 0.0001

Figure 4: Kaplan-Meier survival curve showing time to VEqCO2

nadir-data dichotomised by optimal cut-points (<250 s; n = 170,event free survival 61%;≥250 s n = 254 patients, event free survival80%).

compensation point (VCP). Beyond this point (during heavyto maximal exertion), an increase in VE relative to VCO2 isdependent upon the fall in pH and PaCO2 [12].

The exaggerated ventilatory response of patients withCHF is seen at the outset of exercise; that is, the VE/VCO2

slope is abnormal from the moment exercise starts. A widevariety of factors has been proposed as the reason forthe increase in VE/VCO2 slope including an increaseddead space and resultant “wasted” ventilation [13–15], earlymetabolic acidosis [16], and overactivation of chemorecep-tors and ergoreceptors [17, 18]. The fall in the VEqCO2 atthe onset of exercise is at least in part due to the reductionin fixed anatomical dead space ventilation as a proportion oftotal ventilation at the onset of exercise, but the increase after


0 20 40 60 80 100

Cu

m s

urv

ival

Alive (months)

< 33

1

0.8

0.6

0.4

0.2

0

Log rank χ2 = 61.9; P < 0.0001

≥ 33

VEqCO2 nadir

Figure 5: Kaplan-Meier survival curve showing VEqCO2 nadir-data dichotomised by optimal cut-points (<33 n = 252 patients,event free survival 85%; ≥33 n = 171 patients, event free survival54%).

the plateau phase is due to a non-CO2 stimulus to ventilation,wheather lactate production or an alternative stimulus toventilation, such as the ergoreflex [9, 19]. The shorter time toVEqCO2 nadir reflects the earlier onset (and more importantinfluence of) the non-CO2 stimulus to ventilation in patientswith CHF.

We found a strong relation between the time to VEqCO2

nadir and mortality. The time to VEqCO2 nadir wasan important univariable predictor of all-cause mortalityalthough it was outperformed by peak oxygen uptake andVEqCO2 nadir in a multivariable survival model. We havepreviously shown that peak oxygen uptake [20] and VEqCO2

nadir [7] are independent predictors of all-cause mortalityin patients with CHF. Other investigators have also reportedsimilar findings [8, 21].

A limitation of our study is that we do not have test-retestCPET data for individual patients/controls; therefore, wecannot determine the reproducibility of the time to VEqCO2

nadir in either healthy or diseased populations.Cardiopulmonary exercise testing provides two broad

types of prognostic variable: a measure of exercise capacity,such as peak VO2, reflecting the complex relation betweenpump, ventilator, and muscle extraction; and a measure ofthe ventilatory response to exercise, such as the VE/Vco2

slope or time to VEqCO2 nadir, reflecting the abnormalstimulus to ventilation in CHF. The time to VEqCO2 nadirfollowing maximal CPET was shorter in patients with CHFthan in normal subjects and is a predictor of subsequentmortality.

0 20 40 60 80 100

Cu

m s

urv

ival

Alive (months)

1

0.8

0.6

0.4

0.2

0

≥ 20mL·kg−1·min−1

< 20mL·kg−1·min−1

log rank χ2 = 44.8; P < 0.0001

Peak VO2

Figure 6: Kaplan-Meier survival curve showing peak VO2-datadichotomised by optimal cut-points (<20 mL·kg−1·min−1 n = 184patients, event free survival 60%; ≥ 20 mL·kg−1·min−1 n = 239patients, event free survival 82%).

5. Clinical Messages

(i) Cardiopulmonary exercise testing is becomingincreasingly important for prescribing appropriateexercise training volumes in patients withcardiovascular disease including CHF.

(ii) The ventilatory response to exercise is abnormalin patients with CHF compared to age-matchedcontrols.

(iii) Metabolic responses to exercise are important predic-tors of risk and should be monitored prior to andfollowing a program of rehabilitation in patients withCHF.

Conflict of interests

The authors declare that there is no conflict of interests.

Acknowledgment

This research received no specific grant from any fundingagency in the public, commercial, or not-for-profit sectors.

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