A Simple Echocardiographic Method to Estimate PulmonaryVascular Resistance

Alexander R. Opotowsky, MD, MPHa,b,*, Mathieu Clair, MDb, Jonathan Afilalo, MD, MScc,Michael J. Landzberg, MDa,b, Aaron B. Waxman, MD, PhDa, Lilamarie Moko, BAb,

Bradley A. Maron, MDa, Anjali Vaidya, MDd, and Paul R. Forfia, MDd

Pulmonary hypertension includes heterogeneous diagnoses with distinct hemodynamic

aCardiovasculaWomen’s HospitaBoston ChildrenCardiology and CUniversity, MontDepartment of MPhiladelphia, Penrevised manuscrip

This work wNational Institutesthe Dunlevie Fund

See page 881*CorrespondinE-mail addres

0002-9149/13/$ -http://dx.doi.org/1

pathophysiologic features. Identifying elevated pulmonary vascular resistance (PVR) iscritical for appropriate treatment. We reviewed data from patients seen at referralpulmonary hypertension clinics who had undergone echocardiography and right-sidecardiac catheterization within 1 year. We derived equations to estimate PVR using the ratioof estimated pulmonary artery (PA) systolic pressure (PASPDoppler) to right ventricularoutflow tract velocity time integral (VTI). We validated these equations in a separatesample and compared them with a published model based on the ratio of the transtricuspidflow velocity to right ventricular outflow tract VTI (model 1, Abbas et al 2003). The derivedmodels were as follows: PVR [ 1.2 3 (PASP/right ventricular outflow tract VTI) (model 2)and PVR[ (PASP/right ventricular outflow tract VTI)D 3 if notch present (model 3). Thecohort included 217 patients with mean PA pressure of 45.3 – 11.9 mm Hg, PVR of 7.3 –5.0 WU, and PA wedge pressure of 14.8 – 8.1 mm Hg. Just >1/3 had a PA wedge pressure>15 mm Hg (35.5%) and 82.0% had PVR >3 WU. Model 1 systematically underestimatedcatheterization estimated PVR, especially for those with high PVR. The derived modelsdemonstrated no systematic bias. Model 3 correlated best with PVR (r [ 0.80 vs r [ 0.73and r [ 0.77 for models 1 and 2, respectively). Model 3 had superior discriminatory powerfor PVR >3 WU (area under the curve 0.946) and PVR >5 WU (area under the curve0.924), although all models discriminated well. Model 3-estimated PVR >3 was 98.3%sensitive and 61.1% specific for PVR >3 WU (positive predictive value 93%; negativepredictive value 88%). In conclusion, we present an equation to estimate the PVR, using theratio of PASPDoppler to right ventricular outflow tract VTI and a constant designatingpresence of right ventricular outflow tract VTI midsystolic notching, which providessuperior agreement with catheterization estimates of PVR across a wide range of val-ues. � 2013 Elsevier Inc. All rights reserved. (Am J Cardiol 2013;112:873e882)

Right-sided cardiac catheterization is the reference stan-dard for hemodynamic evaluation of patients with pulmonaryhypertension, including pulmonary vascular resistance (PVR)estimation.1e3 Right-sided cardiac catheterization is invasiveand cannot be applied repeatedly to all patients with suspectedpulmonary hypertension. The estimated pulmonary artery(PA) systolic pressure (PASPDoppler) from transtricuspid flowvelocity (TTFV) is at the core of the echocardiographicevaluation of suspected pulmonary hypertension.4e6 ThePASP does not define PVR, however; patients with vastly

r Division, Department of Medicine Brigham andl, Boston, Massachusetts; bDepartment of Cardiology,’s Hospital, Boston, Massachusetts; cDivisions oflinical Epidemiology, Jewish General Hospital, McGillreal, Quebec, Canada; and dCardiovascular Division,edicine, Hospital of the University of Pennsylvania,nsylvania. Manuscript received February 25, 2013;t received and accepted May 2, 2013.as supported by grant 5-T32-HL07604-25 from theof Health (Bethesda, Maryland) to Dr. Opotowsky andto Drs. Opotowsky and Landzberg.

for disclosure information.g author: Tel: (617) 355-6508; fax: (617) 739-8632.s: sasha.opo@gmail.com (A.R. Opotowsky).

see front matter � 2013 Elsevier Inc. All rights reserved.0.1016/j.amjcard.2013.05.016

different PVR values can have identical PASP. NoninvasivePVR estimationwould be useful for diagnosis andmonitoringresponse to therapy. Several investigators have proposedechocardiographic PVR prediction models.7e14 Some haveused the timing of the right ventricular outflow tract flow toestimate PVR.12 Others have used a ratio of a correlate of PApressure to PA flow in the denominator. An earlymodel basedon the ratio of TTFV to right ventricular outflow tract velocitytime integral (VTI) has been validated to predict PVR inpatients with cirrhosis and adverse outcomes among coronarydisease patients.8,15,16 This model, however, was derived inpatients with normal or mildly elevated PVR and has pooragreement at higher levels of PVR.17,18 Equations usingtranstricuspid pressure gradient (4 � TTFV2) in place ofTTFV have been derived for patients with markedly elevatedPVR.9 Others have evaluated a multiparameter approachinvolving the estimation of all variables involved in calcu-lating the PVR (mean PA pressure, left atrial pressure, strokevolume, and heart rate).11 This approach is time intensive andtherefore less clinically useful. Our objectives were to deriveand validate a simple equation to accurately estimate PVR forwidespread application in a diverse referral population ofpatients with pulmonary hypertension or suspected pulmo-nary hypertension.

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Table 1Descriptive statistics, including demographics, clinical characteristics, and hemodynamics, for derivation and validation cohorts

Parameter Overall (n ¼ 217) Derivation (n ¼ 109) Validation (n ¼ 108) p Value*

Age (yrs) 60.6 � 15.2 58.6 � 15.4 62.6 � 14.8 0.05Men (%) 25.3 20.2 30.6 0.09White (%) 85.5 86.5 84.4 0.84Hypertension (%) 53.9 52.3 55.6 0.68Dyslipidemia (%) 32.3 29.3 35.2 0.39Coronary artery disease (%) 22.6 20.2 25.0 0.42Atrial fibrillation (%) 23.0 19.3 26.9 0.20Pacemaker or defibrillator (%) 7.8 7.3 8.3 0.81Congenital heart disease (%) 6.5 5.5 7.4 0.59Connective tissue disease (%) 14.7 13.8 15.7 0.71Sarcoidosis (%) 4.6 1.8 7.4 0.06Chronic obstructive lung disease (%) 21.7 19.3 24.1 0.41Obstructive sleep apnea (%) 15.2 16.5 13.9 0.71Interstitial lung disease (%) 16.2 16.5 15.9 1.00Asthma (%) 4.1 5.5 2.8 0.50HIV (%) 1.8 1.8 1.9 1.00Diabetes mellitus (%) 18.0 12.8 23.1 0.05Liver cirrhosis (%) 5.1 5.5 4.6 1.00Tobacco, current (%) 6.5 9.2 3.7 0.17Tobacco, former (%) 49.0 45.7 52.4 0.41WHO group (%)I 30.9 37.6 24.1 0.04II 25.8 22.9 28.7 0.36III 16.1 12.8 19.4 0.20

Heart rate (beats/min) 76.9 � 14.6 76.6 � 15.1 77.2 � 14.2 0.75Systolic blood pressure (mm Hg) 125.3 � 20.8 123.2 � 20.1 127.3 � 21.3 0.16Right atrial pressure (mm Hg) 10.7 � 6.1 10.6 � 6.0 10.8 � 6.2 0.78Systolic PA pressure (mm Hg) 72.8 � 21.2 72.8 � 19.7 72.8 � 22.6 1.00mPAP (mm Hg) 45.2 � 13.0 45.4 � 12.5 45.0 � 13.6 0.80PAWP (mm Hg) 13.8 � 7.9 13.2 � 7.4 14.4 � 8.2 0.25PVR (WU) 7.7 � 5.1 7.7 � 5.1 7.7 � 5.1 0.97Cardiac output (L/min) 4.8 � 1.8 4.9 � 1.8 4.7 � 1.7 0.43

Categorical data are presented as % and continuous data as mean � SD.Hypertension refers to a clinical diagnosis of systemic arterial hypertension, consistent with recommendations of the Joint National Committee 7 as either

systolic blood pressure �140 mm Hg, diastolic blood pressure �90 mm Hg, or treatment with an antihypertensive medication (except if prescribed for analternative use such as calcium channel blockers for treatment of Raynaud’s phenomenon).

Dyslipidemia was defined clinically according to the Adult Treatment Panel III guidelines and/or treatment with a lipid medication.Coronary artery disease refers to atherosclerotic coronary disease, either documented significant epicardial coronary stenosis, with or without previous

intervention, or a history of acute coronary syndrome.HIV ¼ human immunodeficiency virus; mPAP ¼ mean pulmonary artery pressure; PAWP ¼ PA wedge pressure; WHO ¼ World Health Organization.* For differences between derivation and validation cohorts.

874 The American Journal of Cardiology (www.ajconline.org)

Methods

The cohort included patients seen by the pulmonaryhypertension services at the Hospital of the University ofPennsylvania, Brigham and Women’s Hospital, BostonChildren’s Hospital, or Massachusetts General Hospital whohad undergone right-sided cardiac catheterization andtransthoracic echocardiography from March 2002 to January2012. The exclusion criteria were >12 months betweenright-sided cardiac catheterization and transthoracic echo-cardiography, interval initiation of pulmonary vasodilator orloop diuretic or cardiovascular or abdominal surgery ora change in clinical status, unrepaired congenital heartdisease, or intravenous inotropes or vasopressors or positivepressure ventilation at the time of either study. We excluded44 of 261 (17.1%) otherwise eligible patients because ofpoor-quality tricuspid regurgitation signals. The medianinterval between right-sided cardiac catheterization and

transthoracic echocardiography was 21.5 days (interquartilerange 5 to 55.5). The patients were classified according tothe World Health Organization pulmonary hypertensiondiagnostic groups.2

The patients had undergone clinically indicated trans-thoracic echocardiography with the Philips Sonos 7500 orIE33 (Philips Medical Systems, Andover, Massachusetts) orGE Vivid 7 Ultrasound (GE, Milwaukee, Wisconsin). Themeasurements were made by an echocardiographer(A.R.O.), who was unaware of the invasive hemodynamicsdata, in accordance with the American Society of Echo-cardiography guidelines19 using AcusonKinetDx (WS3000Diagnostic Workstation, Siemens Medical Solutions USA,Mountain View, California) or Showcase Premier 5.3(Trillium Technology, Ann Arbor, Michigan). At least 3cardiac cycles were measured (5 to 10 with irregularrhythms), and average values were used.

Figure 1. Bland-Altman plots for the derivation (Left) and validation (Middle) cohorts for model 1 (Top) and model by Kouzu et al9 (Bottom). The average ofthe PVR value from catheterization and the respective PVR estimation model is plotted on the x axis, and the difference between the catheterization PVR andthe PVR estimated by the model is plotted on the y axis. Blue dotted line specifies mean bias; black dotted lines represent the 95% limits of agreement. (Right)Data for all subjects are plotted for model 1 and model by Kouzou et al,9 with 95% limits of agreement using regression of the absolute value of the residuals toestimate the SD at varying levels of average PVR. These plots demonstrate that model 1 has greater negative bias at higher values of PVR, and the model byKouzou et al9 overestimates PVR at low PVR values.

Figure 2. Bland-Altman plots for the derivation (Left) and validation (Middle) cohorts for model 2 (Top) and model 3 (Bottom). The average of the PVR valuefrom catheterization and the respective PVR estimation models is plotted on the x axis, and the difference between the catheterization PVR and the PVRestimated by the model is plotted on the y axis. Blue dotted line specifies mean bias; black dotted lines represent 95% limits of agreement. (Right) Data plottedfor all subjects and 95% limits of agreement using regression of the absolute value of the residuals to estimate the SD at varying levels of average PVR formodels 2 (Top) and 3 (Bottom). Although both models have minimal bias at all levels of PVR, because of its small positive bias and narrow 95% limits ofagreement at low PVR values, model 3 is unlikely to estimate PVR <3 WU for a patient with elevated PVR.

Miscellaneous/Echocardiographic PVR 875

The left atrial anteroposterior end-systolic dimension wasmeasured from the parasternal long-axis view. Rightventricular outflow tract pulse wave Doppler interrogationwas performed from the basal short-axis views just proximal

to the pulmonic valve. By inspecting the pulse wave Dopplersignal from the right ventricular outflow tract, the presence ofa midsystolic notch was characterized as a distinct notch ornadir within the initial 2/3 of the systolic ejection period.20

Figure 3. (A) PVR versus estimated PVR using model 2, with red and blue designating the presence of midsystolic notching. The best-fit line for patients withmidsystolic notching was significantly higher for a given value of estimated PVR (p �0.0001 for intercept ¼ 0). (B,C) PVR versus model 3 estimated PVR(B, red and blue indicate presence of midsystolic notching). Adding a constant of 3 for patients with midsystolic notching resulted in overlapping best-fit lineswith equal intercepts (p ¼ 0.34). (C) The best-fit line for model 3.

876 The American Journal of Cardiology (www.ajconline.org)

TTFV was obtained from continuous wave Doppler interro-gation in the view that provided the best envelopewith the highest estimates. Left ventricular inflow pulse waveDoppler was used to measure the peak E/A velocities.Lateral mitral annular tissue Doppler was used to measuree0 velocity.21

Right-sided cardiac catheterization was performed usinga balloon-tipped, fluid-filled catheter, as previouslydescribed.20 No sedation was administered for most cathe-terizations, and minimal to moderate conscious sedation wasused for a subset with spontaneous ventilation. Supple-mental oxygen was not administered unless the patient usedchronic supplemental oxygen at rest, in which case the samedose was used during right-sided cardiac catheterization.The cardiac output was estimated either by triplicate ther-modilution (n ¼ 83) or assumed Fick (n ¼ 134). Oxygenconsumption was assumed to be 125 ml/m2 body surfacearea. No difference (p ¼ 0.74) was seen in the mean cardiacoutput between the 2 groups, and the method used did notsignificantly modify the relationship between the rightventricular outflow tract VTI and cardiac output (i.e., no2-way interaction).

Categorical data are expressed as proportions, andcontinuous data are presented as the mean � SD or medianand interquartile range, as appropriate. Unpaired t tests andWilcoxon rank sum test were used to compare the meanvalues for normally and non-normally distributed contin-uous variables, respectively. The chi-square test or Fisher’sexact test was performed to compare the proportions forcategorical variables.

We compared the derived models to a published modelfrom Abbas et al8 based on the ratio of TTFV to VTI:

PVR ¼ 10� TTFVRVOT VTI

þ 0:16 ðmodel 1Þ

The overall sample was divided using random samplinginto derivation and validation samples. To derive the model,we used linear regression analysis and forward selection,with p <0.10 for entry. The variables were retained if they

increased r2 by �0.02. Potential variables included the leftatrial anteroposterior dimension, lateral mitral E/e0, TTFV,4 � TTFV2, estimated PASP using a fixed right atrialpressure of 8 mm Hg (PASPDoppler) or variable right atrialpressure of 5, 10, or 15 mm Hg or 3, 8, or 15 mm Hgaccording to the inferior vena caval diameter andcollapse,22,23 PASPDoppler-E/e0, acceleration time, rightventricular outflow tract midsystolic notching, rightventricular outflow tract VTI, and ratios of the followingvariables to VTI: TTFV, 4 � TTFV2, and PASPDoppler(¼ 4 � TTFV2 þ 8). Given the potential for model insta-bility in the setting of collinear parameters, we also assessedthe predictive value of alternative models to confirm thederived model was associated with the highest r2.

The derived model was PVR ¼ �0.05 þ 1.0 �(PASPDoppler/VTI) þ 2.8 � (midsystolic notching), wheremidsystolic notching was 1 if present and 0 if absent (partialr2 ¼ 0.596 for PASPDoppler/VTI and partial r2 ¼ 0.068 forthe midsystolic notching term). To simplify, we rounded thecoefficients to the nearest integer, given the clinical insig-nificance of PVR � 0.25 WU. We further evaluated a modelthat included only PASPDoppler/VTI, given the potential forincreased simplicity and to test the value of midsystolicnotching in the validation cohort. Sensitivity analyses wereperformed to assess model performance in specific subsetsof patients (e.g., high PA wedge pressure) and whetheralternative parameters might improve the model. Bland-Altman plots were used to assess the agreement between thederived models and PVR. Model discrimination for clini-cally relevant levels of PVR (>3 or >5 WU) was assessedusing receiver operating characteristic curves, area under thecurve, and sensitivity, specificity, and negative and positivepredictive values.

Thus, the simple PASPDoppler/VTI model was asfollows:

PVR ¼ 1:2� PASPRVOT VTI

ðmodel 2Þ

where RVOT is the right ventricular outflow tract.

Figure 4. Receiver operating characteristic curves for discrimination ofPVR >3 WU.

Miscellaneous/Echocardiographic PVR 877

The comprehensive model was as follows:

PVR ¼ PASPRVOT VTI

þ 3 if notch present ðmodel 3Þ

where RVOT is the right ventricular outflow tract.We also assessed the agreement between PVR and

a model derived by Kouzu et al9 in a population enriched forelevated PVR (mPVR ¼ 16.2 WU):

PVR ¼ 1:48� TTFV2

RVOT VTIþ 2:34

For simplicity, we have limited the reported results onthis equation to Bland-Altman plots illustrating the over-estimation of PVR at low values. This equation correlatedless well with PVR than did model 3 (r ¼ 0.77 vs r ¼ 0.80).Statistical analyses were performed using the StatisticalAnalysis Systems for Windows, version 9.3 (SAS Institute,Cary, North Carolina) and GraphPad Prism, version 5.02,for Windows (GraphPad Software, San Diego, California).

Results

Demographic, clinical, and hemodynamic data are listedin Table 1. The mean patient age was 60.6 years, and theprevalence of common chronic diseases was high. The meanPA pressure was markedly elevated (45.2 � 13.0 mm Hg),as was the PVR (7.7 � 5.1 WU). Just >1/3 (35.5%) hada PA wedge pressure >15 mm Hg, and 10% had a PAwedge pressure of �23 mm Hg. The derivation and vali-dation cohorts were similar, although the validation cohorttended to be older, with a smaller proportion of WorldHealth Organization group I pulmonary hypertension.

In the derivation cohort, we found that the publishedmodel using TTFV/right ventricular outflow tract VTI(model 1) underestimated the PVR, especially at higherPVR values. The best linear approximation using TTFV/right ventricular outflow tract VTI was PVR ¼ 22.8 TTFV/VTI (or PVR ¼ 0.76 þ 2.37x, where x is the PVR estimateper model 1), with r2 ¼ 0.531. Model 1 demonstratedsignificant bias (bias �4.1) and increasingly underestimatedPVR with higher PVR values (Figure 1). The model derivedby Kouzu et al9 (Figure 1) was accurate at high PVR valuesbut overestimated normal to mildly elevated PVR(bias þ2.7 � 3.4, 95% limits of agreement �3.9 to 9.3).

Model 2 (PVR ¼ 1.2 � [PASP/right ventricular outflowtract VTI]) correlated more closely with PVR (r2 ¼ 0.593).Model 3 (PVR ¼ [PASP/right ventricular outflow tractVTI] þ 3, if midsystolic notching present) correlated betterstill (r2 ¼ 0.669). Figure 2 shows Bland-Altman plots formodels 2 and 3 in the derivation cohort. As shown inFigure 3 (data for the entire cohort; equivalent results for thederivation and validation cohorts), lines approximating PVRwith and without midsystolic notching are parallel (p ¼ 0.07for difference in slope) but y intercepts at x ¼ 0 differed byw3 WU (p <0.0001). Including the additional term formidsystolic notching resulted in equivalent intercepts (p ¼0.34; Figure 3).

We evaluated whether including estimates of PA wedgepressure improved the predictive models. Neither the leftatrial dimension nor lateral mitral annular E/e0 met the criteria

Figure 5. Tracings from 4 subjects, along with PVR estimates by each model and catheterization PVR. (A) Normal TTFV and right ventricular outflow tractVTI without midsystolic notching. Models 1 and 3 accurately estimated PVR, and model 2 overestimated PVR. (B) TTFV was likely underestimated because ofsignal quality. All models predicted PVR >3 WU, although model 1 underestimated PVR the most. (C) High TTFV with high right ventricular outflow tractVTI. (D) Model 1 markedly underestimated PVR with high PVR.

878 The American Journal of Cardiology (www.ajconline.org)

to be included in the model. Given the theoretical benefit ofincluding a PA wedge pressure estimate, however, weexplored various methods to estimate the transpulmonarypressure gradient (difference between PASPDoppler andcorrelates of PA wedge pressure). No resulting equationprovided statistically significant improvement in the esti-mates. For example, using a previously proposed method(numerator¼ PASPDoppler�E/e0),13 we found the correlationfor n ¼ 87 with E/e0 data (r2 ¼ 0.595) was the same as formodel 2, with a numerator of PASPDoppler alone (r

2¼ 0.596).To understand whether this resulted from variability in esti-mating the PA wedge pressure or to limited intrinsic valueto including PA wedge pressure, we estimated the trans-pulmonary pressure gradient as PASPDoppler�actual (cathe-terization) PA wedge pressure. Using this as the numerator inmodel 2, the correlation improved modestly (from r2¼ 0.593to r2¼ 0.622), but no improvement was seen inmodel 3 (fromr2 ¼ 0.669 to r2 ¼ 0.662).

We examined how well any 2 of 3 component variables(mean PA pressure, PA wedge pressure, cardiac output) ofcatheter PVR could explain PVR by substituting the meanvalue for each parameter as a constant in the equation.Cardiac output and mean PA pressure provided much moreinformation on PVR than did PA wedge pressure. SettingPA wedge pressure at 15, the equation (PVR ¼[mPAP�15]/cardiac output) resulted in r2 ¼ 0.876. Equa-tions substituting a constant for the mean PA pressure andcardiac output, respectively, produced r2 ¼ 0.653 and r2 ¼0.687. Likewise, the mean PA pressure and cardiac outputcorrelated with PVR much better than did the PA wedgepressure (r2 ¼ 0.575, r2 ¼ 0.342, and r2 ¼ 0.129,

respectively). No single catheter-derived variable predictedPVR as well as did the derived echocardiographic models;even perfect echocardiographic estimation of 1 parametercould not predict PVR as well as did a combination.

In the validation cohort, model 3 correlated better withPVR (r2 ¼ 0.622) than did model 2 (r2 ¼ 0.597) or model 1(r2 ¼ 0.551). The intercept and coefficients of the best-fitline did not differ significantly from the derivation cohortfor either model 2 or 3. No additional predictors of PVRachieved statistical significance (all p >0.15) or providedadditional explanatory power (Dr2 <0.02).

Figure 1 shows Bland-Altman plots for the validationcohort for model 1 and the model by Kouzu et al.9 Model 1again displayed bias (bias �4.1 � 4.1, 95% limits ofagreement �12.2 to 4.0) and progressively underestimatedPVR at higher PVR values. The model by Kouzu et al9

tended to overestimate lower PVR values (bias þ2.7 � 3.2,95% limits of agreement �3.7 to 9.0). Figure 2 shows theequivalent data for models 2 and 3, neither of whichdemonstrated systematic bias (model 2, bias �0.29 � 3.3,95% limits of agreement �6.7 to 6.1; model 3, bias þ0.03 �3.2, 95% limits of agreement �6.2 to 6.2). Figures 1 and 2show the data for all subjects, but the 95% limits ofagreement were estimated using regression of the absolutevalue of the residuals to estimate the SD at varying levels ofaverage PVR, instead of assuming a model would have thesame bias and limits of agreement across the spectrum ofPVR values. It can be seen (Figure 1) that model 1progressively underestimated the PVR at higher values, andthe model by Kouzu et al9 overestimated the PVR at lowlevels. However, models 2 and 3 demonstrated narrow limits

Table 2Pearson correlation coefficients between pulmonary vascular resistance (PVR) and various predictors

Variable Acceleration Time RVOT-VTI TTFV Estimated PASP Model

1 2 3

DerivationPVR �0.514 �0.549 0.624 0.633 0.733 0.768 0.803Acceleration time 0.512 �0.457 �0.444 �0.546 �0.538 �0.604RVOT-VTI �0.264 �0.258 �0.827 �0.736 �0.722TTFV 0.993 0.607 0.733 0.710Estimated PASP 0.602 0.738 0.712Model 1 0.978 0.922Model 2 0.939

ValidationPVR �0.576 �0.545 0.603 0.601 0.743 0.772 0.789Acceleration time 0.546 �0.528 �0.510 �0.618 �0.622 �0.666RVOT-VTI �0.263 �0.253 �0.854 �0.765 �0.727TTFV 0.994 0.586 0.731 0.707Estimated PASP 0.577 0.732 0.706Model 1 0.974 0.909Model 2 0.934

PVR <8 WU*PVR �0.578 �0.397 0.564 0.564 0.579 0.622 0.697Acceleration time 0.407 �0.368 �0.362 �0.485 �0.483 �0.583RVOT-VTI �0.054 �0.047 �0.796 �0.656 �0.603TTFV 0.995 0.552 0.726 0.626Estimated PASP 0.544 0.725 0.626Model 1 0.967 0.824Model 2 0.850

RVOT ¼ right ventricular outflow tract.* Subset of the combined cohort with PVR <8 WU (n ¼ 135).

Miscellaneous/Echocardiographic PVR 879

of agreement at low PVR values, with a slight positivebias (Figure 2), consistent with a high sensitivity for PVR>3 WU.

Figure 4 shows the receiver operating characteristiccurves for PASPDoppler and models 1 to 3 as predictors ofPVR >3. The results were similar in the derivation andvalidation cohorts. Model 3 estimated PVR >3 had 98.3%sensitivity and 61.1% specificity for PVR >3 WU (positivepredictive value 93% and negative predictive value 88%),and an estimated PVR >7 had 63.0% sensitivity and 97.2%specificity for PVR >3 (positive predictive value 99% andnegative predictive value 34%). All subjects with estimatedPVR >8 (n ¼ 95) had PVR >3 WU, just as did 94.3% (50of 53) with an estimated PVR of 5 to 8.

Figure 5 gives examples of Doppler tracings and PVRestimates for each model and catheterization PVR.

We performed sensitivity analyses to assess the modelperformance in specific subsets of patients and to determinewhether specific physiologic findings were associated withmodel performance. First, among patients with PASPDoppler<60 mm Hg (TTFV �3.6 m/s), in whom this equation ismost likely to be used clinically, PASPDoppler had lowerdiscriminatory ability for PVR >3 (area under the curve0.712), and models 1 and 2 had intermediate discriminatorypower (area under the curve 0.849 and 0.846, respectively;model 3, area under the curve 0.873). Second, amongpatients with PVR <8 WU, model 3 again demonstrated thehighest correlation with PVR >3 (r ¼ 0.70 vs r ¼ 0.58 andr ¼ 0.62 for models 1 and 2, respectively; Table 2). Third,excluding PVR outliers, patients with PVR in the top or

bottom 5% (n ¼ 195, PVR 1.5 to 17.4 WU) producedsimilar results, albeit with slightly lower correlation coeffi-cients (r ¼ 0.55, r ¼ 0.64, r ¼ 0.67, and r ¼ 0.73 forPASPDoppler and models 1 to 3, respectively). Fourth, toassess the effect of the time delay between echocardiog-raphy and catheterization, we limited the analysis to studiesperformed <22 days apart (median). However, this had littleeffect on the results (n ¼ 109, r ¼ 0.67, r ¼ 0.70, and r ¼0.76 for models 1 to 3, respectively). Fifth, among the 77patients with PA wedge pressure >15 mm Hg, the corre-lation with PVR was slightly lower (r ¼ 0.63, r ¼ 0.70, r ¼0.74, and r ¼ 0.75 for PASPDoppler and models 1 to 3,respectively). This was also true for the subset with a diag-nosis of World Health Organization group II pulmonaryhypertension (n ¼ 56; r ¼ 0.62, r ¼ 0.74, r ¼ 0.79, and r ¼0.81 for PASPDoppler and models 1 to 3, respectively).Bland-Altman analysis for model 3 for patients with PAwedge pressure >15 mm Hg suggested minimal systematicbias (bias þ0.46 � 2.62). Finally, to determine whether theheart rate affected the model, we repeated the assessment ofmodel 2, using the product of VTI and heart rate as thedenominator. This product and VTI alone correlated simi-larly with cardiac output among the 209 subjects with heartrate data available (r ¼ 0.49 to r ¼ 0.50); the resulting PVRestimates performed less well (r2 ¼ 0.530 vs r2 ¼ 0.590).

Discussion

We have described the derivation and validation of anequation to estimate PVR using clinically available

880 The American Journal of Cardiology (www.ajconline.org)

echocardiographic parameters. The model includes the ratioof estimated PASP (PASPDoppler ¼ 8 þ 4 � TTFV2) to rightventricular outflow tract VTI, with the addition of a constant(þ3) for patients with right ventricular outflow tract Dopplerflow envelope midsystolic notching. The equation is simpleand easily integrated into clinical practice. In addition, it isas applicable to patients with normal PVR as it is to thosewith markedly elevated PVR.

Previous investigators have demonstrated that an echo-cardiographic estimate using a ratio of estimated PA pressureto flow can approximate the PVR. Initial models includedTTFV as the numerator. Although a direct correlation ispresent between TTFV/VTI and PVR at any level of PVR,24

the absolute agreement between this ratio and PVR is notrobust. TTFV/VTI underestimates PVR at higher levels,because it does not account for the quadratic relation betweenvelocity and pressure. Other models, such as that reported byKouzu et al,9 were derived from populations with elevatedPVR to address this limitation. They used estimates of thetranstricuspid pressure gradient or PASP (4� TTFV2� rightatrial pressure estimate) in the numerator. Because of theuniversally high PVR in the derivation samples, however, theequation includes a large constant, resulting in overestimationof the PVR in the normal range.9 The present model providedgood agreement with true PVR throughout the range ofPVR, addressing a limitation of the previously proposedmodels.

All the models correlated reasonably well with thecatheter-derived PVR (r � 0.7). Although model 3 corre-lated best with PVR, the small difference makes ease of useand interpretation important considerations in choosingamong these options. Most clinical echocardiographiclaboratories will report the PASPDoppler or transtricuspidgradient. The units used for PASPDoppler or transtricuspidgradient are consistent (mm Hg), but different options existfor TTFV (cm/s or m/s); thus, consideration must be givento the units in the calculation of model 1. Including mid-systolic notching might seem to add complexity, but itresulted in a coefficient of 1 for PASP/VTI (vs 1.2 in model2). Finally, although a given cutoff of model 1 has similartest characteristics to PVR ¼ 3 for model 3, model 3’ssuperior agreement between the absolute values of theestimated PVR makes it a more reliable predictor of theactual numerical PVR in an individual patient, and inter-pretation is thereby simplified. Because of these consider-ations, we believe model 3 is preferable.

The inclusion of midsystolic notching is useful froma clinical perspective and is consistent with previous datathat almost all patients with midsystolic notching haveelevated PVR.20 It improves the sensitivity for high PVR,because it provides a second mechanism for the equationto produce a result with elevated PVR; even if the VTIand TTFV have been acquired or measured incorrectly,midsystolic notching will highlight the likelihood of PVR>3 WU.

The derived equations include an estimate of pressure inthe numerator and flow in the denominator. Both terms aresimplifications of the physiology captured by catheteriza-tion. First, PVR is calculated with the transpulmonarygradient, the difference between the mean PA pressure andPA wedge pressure (or left atrial pressure), as the numerator.

The proposed equations use PASPDoppler as a surrogate.PASP is not equivalent to mean PA pressure, althoughhighly correlated (r ¼ 0.95 in this sample). Although meanPA pressure can be estimated using echocardiography, thisis more time consuming and not commonly applied inpractice. Likewise, PA wedge pressure can be estimated byechocardiography, but including such estimates did notimprove model performance. This relates to the imprecisionof PASP and PA wedge pressure estimates and the limitedrelative contribution of PA wedge pressure to the absolutePVR value compared with the greater influence of PApressure and cardiac output. Second, the equations use rightventricular outflow tract VTI as a marker of flow. VTIreflects the average distance traveled by the blood columnbut only indirectly suggests the stroke volume in theabsence of data on area. The right ventricular outflow tractVTI varies inversely with the cross-sectional area of theright ventricular outflow tract and, presumably, indirectlywith PVR.24 The left ventricular outflow tract area might beless susceptible to such, and some have suggested the leftventricular outflow tract VTI would provide a better markerof stroke volume. One study reported no benefit to using theleft ventricular outflow tract VTI instead of the rightventricular outflow tract VTI.10 In addition, cardiac output isthe product of stroke volume and heart rate. Integrating heartrate into the equation did not improve the estimates. Inpatients with elevated PVR, we would expect a larger rightventricular outflow tract area and higher heart rate, both ofwhich would result in the VTI underestimating the cardiacoutput in patients with elevated PVR and would bias themodel to overestimate the PVR at high values. Not only didwe not observe that in our cohort, such a phenomenonwould actually increase the likelihood that patients withhigh PVR are identified. The absolute value in a patient withvery high PVR is less important than that the clinician isalerted to the presence of high PVR.

We found a lower correlation between the predicted andactual PVR than in most previous reports. This might havebeen, in part, owing to use of clinical echocardiograms orthe interval between studies. Our sample size and PVRdistribution also differed. Most of the 44 subjects used toderive model 1 had normal PVR; only 6 had PVR >3 WUand 3 had PVR >4 WU.8 In small studies, outliers will haveundue influence and inflate the correlation.

We see several potential uses for the derived equation.First, it can be used to clinically assess whether a patient hasimportantly elevated PVR. An estimate significantly <3WU argues against elevated PVR. Patients with suspectedpulmonary hypertension or elevated PASP in whom itwould be unclear whether the pulmonary hypertension isdue to elevated PVR, flow (e.g., cirrhosis), or left atrialpressure (e.g., left-sided cardiac disease) would be a relevantpopulation. Second, it can be used to assess the treatmentresponse to vasodilators. This would not replace right-sidedcardiac catheterization but could supplement information,because right-sided cardiac catheterization is invasive andexpensive. Finally, large epidemiologic studies have shownelevated estimated PASP is a predictor of heart failure andpoor outcomes in patients with a normal and reduced ejec-tion fraction.25e27 Being able to estimate PVR on a pop-ulation scale would help distinguish whether this reflects the

Miscellaneous/Echocardiographic PVR 881

adverse prognostic import of elevated left atrial pressure oran independent vascular remodeling, as suggested by 1study of patients with coronary disease.16

These data were derived from clinical echocardiogramsand a time delay was present between the echocardiogramand catheterization. Although empirically we found themodel performed equally well whether performed close tocatheterization or with many months in between tests, sucha delay could in theory increase the nondifferential error andbias the results toward the null. A nondifferential errorwould be expected to be more of an issue for PASPDopplerthan for TTFV, because PASP estimation involves squaringany error. Midsystolic notching can vary with respirationand sample volume placement. With these considerations,we would expect the test characteristics of models 2 and 3 tobe more susceptible to random error than model 1. Theinclusion of midsystolic notching improved the sensitivityfor elevated PVR as outlined; even if the VTI and TTFVunderestimate PVR, midsystolic notching will highlight thelikelihood of PVR >3 WU.

PASP estimation using TTFV requires the addition of theright atrial pressure to transtricuspid gradient. Some havesuggested adding a constant value (e.g., 8 or 14 mm Hg), andothers have used a clinical estimate from the assessment of thejugular venous pressure or have made a gross estimate basedon the features of the inferior vena cava. In choosinga constant, we opted for a middle value (8 mm Hg). Theaverage VTI in the sample in which the PVR was <5 WU(the subset likely to affected by small differences in thenumerator) was w15 cm. Omitting the right atrial pressurealtogether might be expected to underestimate PVR, onaverage, byw0.5WU; adding 14mmHgmight overestimateit by w0.4 WU. The relevance of such small differences isquestionable when considered in light of the intrinsic vari-ability of echocardiographic variables and catheter PVR.

Disclosures

The authors have no conflicts of interest to disclose.

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