International Journal of Cardiology 187 (2015) 256–263

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International Journal of Cardiology

j ourna l homepage: www.e lsev ie r .com/ locate / i j ca rd

Defining normality in a European multinational cohort: Critical factorsinfluencing the 99th percentile upper reference limit for high sensitivitycardiac troponin I

Magdalena Krintus a,⁎, Marek Kozinski b, Pascal Boudry c, Karl Lackner d, Guillaume Lefèvre e,Lieselotte Lennartz f, Johannes Lotz d, Slawomir Manysiak a, Jessie Shih g, Øyvind Skadberg h,Ahmed Taoufik Chargui e, Grazyna Sypniewska a

a Department of Laboratory Medicine, Nicolaus Copernicus University, Collegium Medicum in Bydgoszcz, Polandb Department of Cardiology and Internal Medicine, Nicolaus Copernicus University, Collegium Medicum in Bydgoszcz, Polandc Department of Clinical Biology, CHR Mons-Hainaut, Mons, Belgiumd Laboratory Medicine, University Medical Center, Johannes Gutenberg University Mainz, Mainz, Germanye Department of Biochemistry and Hormonology, AP-HP, Hôpital Tenon, Paris, Francef Abbott Laboratories, Wiesbaden, Germanyg Abbott Laboratories, Abbott Park, IL, USAh Laboratory of Clinical Biochemistry, Stavanger University Hospital, Stavanger, Norway

Abbreviations: cTn, cardiac troponin;MI,myocardial ilimit; CV, coefficient of variation; hs, high-sensitivity; IFClinical Chemistry and LaboratoryMedicine; CAD, coronartriuretic peptide; HbA1c, glycated hemoglobin; eGFR, estimTC, total cholesterol; LDL-C, low-density lipoprotein cholesprotein cholesterol; TG, triglycerides; LoD, limit of detectiinRenalDisease;BMI,bodymass index;CI, confidence interaacetic acid.⁎ Corresponding author at: Department of Laboratory

University, Collegium Medicum, 9 Sklodowskiej-Curie StrE-mail address: krintus@wp.pl (M. Krintus).

http://dx.doi.org/10.1016/j.ijcard.2015.03.2820167-5273/© 2015 Elsevier Ireland Ltd. All rights reserved

a b s t r a c t

a r t i c l e i n f o

Article history:

Received 13 August 2014Received in revised form 9 January 2015Accepted 15 March 2015Available online 20 March 2015

Keywords:High-sensitivity troponinNormality99th percentile URL

Objective: To establish and critically evaluate the 99th percentile upper reference limit (URL) for high-sensitivitycardiac troponin I (hs-cTnI) in a large healthy European cohort using different selection criteria.Methods: 1368 presumably healthy individuals from 9 countries were evaluated with surrogate biomarkers fordiabetes (glycated hemoglobin [HbA1c] b48 mmol/mol), myocardial (B-type natriuretic peptide [BNP]b35 pg/mL) and renal dysfunction (estimated glomerular filtration rate [eGFR] N60 mL/min/1.73 m2), anddyslipidemia to refine the healthy cohort. The 99th percentile URLs were independently determined by thenon-parametric and robust methods.Results: The use of biomarker selection criteria resulted in a decrease of the 99th percentile URL for hs-cTnI from23.7 to 14.1 ng/L and from 11.2 to 7.1 ng/L, when using the non-parametric percentile and robust methods, re-

spectively; a further reduction after exclusion of individuals with dyslipidemia was noted. Male gender, BNP,HbA1c and smoking statuswere independently associatedwith hs-cTnI concentration in the presumably healthypopulation, while the impact of age, present in the univariate analysis, decreased after adjustments for genderand surrogate biomarkers. The BNP-based inclusion criterion had the most pronounced effect on the 99th per-centile URL, excluding 21% of the study participants and decreasing its value to 11.0 (7.1) ng/L according to thenon-parametric (robust) method. Gender, but not age-specific, differences at 99th percentile URL have beenidentified.Conclusion: The selection of a reference population has a critical impact on the 99th percentile value for hs-cTnI. Auniform protocol for the selection of the healthy reference population is needed.

© 2015 Elsevier Ireland Ltd. All rights reserved.

nfarction; URL, upper referenceCC, International Federation ofy artery disease; BNP, B-type na-ated glomerularfiltration rate;terol; HDL-C, high-density lipo-on;MDRD,Modification of Dietrval; EDTA,ethylenediaminetet-

Medicine, Nicolaus Copernicuseet, 85-094 Bydgoszcz, Poland.

.

1. Introduction

The Universal Definition of Myocardial Infarction has strengthenedthe role of biomarkers ofmyocardial necrosis, positioning cardiac tropo-nin (cTn) as the preferred biomarker for the diagnosis of myocardial in-farction (MI) [1]. The 99th percentile upper reference limit (URL),established in a healthy reference population, has been designated asthe decision threshold for the diagnosis of MI with precision definedas coefficient of variation (CV) of b10% at this concentration [1,2].High-sensitivity cardiac troponin (hs-cTn) assays were designed tofurther facilitate clinical decision making with their improved precisionat the 99th percentile URL. Since hs-cTn assays are able to detect

257M. Krintus et al. / International Journal of Cardiology 187 (2015) 256–263

measurable cTn concentrations in a substantial proportion of healthy in-dividuals [1,2] and detectable cTn in healthy individuals is a strongprognostic indicator for future cardiac events [3], the appropriate deter-mination of the diagnostic cutoff values is of particular importance.

To date, the selection criteria for a presumably healthy populationfor the determination of the 99th percentile URL for hs-cTn have notbeen thoroughly defined. Several approaches have been proposed,which include: data collection from a questionnaire, screeningwith sur-rogate biomarkers, cardiac imaging techniques and implementation ofother diagnostic tests in order to evaluate health status [4–7]. The Inter-national Federation of Clinical Chemistry (IFCC) Task Force recom-mends a minimum of 300 individuals and the use of appropriatestatistical methods [4,5,8]. From the laboratory perspective, the use ofsurrogate biomarkers to identify clinically asymptomatic disease(e.g., diabetes, myocardial dysfunction or renal insufficiency) amongthose representing reference populations remains a challenge [5,6].Although not recommended for the purpose of defining a healthy refer-ence population, dyslipidemia constitutes one of the strongest cardio-vascular risk factors [9,10]. Therefore, we hypothesize that assessmentof the lipid profile may be relevant for identification of truly healthy in-dividuals by excluding those with subclinical coronary artery disease(CAD).

Of major importance, the diagnosis of MI is associated with numer-ous clinical, social, psychological, epidemiological, economic, legal andresearch implications [11]. In fact, the only criterion differentiating pa-tients with acute MI and unstable angina is the presence or absence ofa rise and/or fall of cTnwith at least one value above the 99th percentileURL [1].

The goals of our study were: i) to establish the 99th percentile URLfor the new ARCHITECT STAT hs-cTnI assay in a large healthyEuropean cohort and ii) to evaluate the impact of laboratory screening,population selection and statistical approach on the hs-cTnI 99th per-centile URL.

2. Materials and methods

2.1. Study design and population

This European study with the ARCHITECT STAT High SensitiveTroponin-I assay was designed as an observational, community-based,multicenter cohort study. Samples frompresumably healthy individualsfrom 9 European countries were collected from March to August 2013,except for participants from the Nordic countries, whose samples werecollected in 2004. In Belgium and France samples were obtained fromindividuals coming for health check-up and from hospital staff, respec-tively. In Germany random samples from participants of the GutenbergHealth Study (a large-scale prospective and population-based studyaimed at improving the individual risk prediction for diseases) present-ing for their follow-up visit were included [12]. The cohort from theNordic countries was a random selection of samples from the NOBIDAproject [13]. In Poland community healthy volunteers were recruitedin several workplaces. Plasma and serum samples were obtained from1368 presumably healthy individuals including community-based vol-unteers who declared good health by questionnaire, completed byeach individual before blood draw and provided informed consent forstudy participation. The presumably healthy reference population wasdefined as individuals who were free of known cardiac diseases (hadno history of cardiac disease, cardiac treatment, cardiac intervention),hypertension and diabetes mellitus according to the questionnaire. Bio-markers were used to further refine the healthy population as follows:B-type natriuretic peptide (BNP) b35 pg/mL for both genders [14],glycated hemoglobin (HbA1c) b48 mmol/mol (b6.5%) [15], and esti-mated glomerular filtration rate (eGFR) N60 mL/min/1.73 m2 [16].Using these parameters we progressively excluded subjects at risk ofmyocardial dysfunction, diabetes and moderate to severe kidney dys-function. Dyslipidemia was defined by at least one abnormal lipid

parameter: total cholesterol (TC) N5 mmol/L (190 mg/dL), low-density lipoprotein cholesterol (LDL-C) N3 mmol/L (115 mg/dL), HDL-C b1 mmol/L (40 mg/dL) for men and b1.2 mmol/L (45 mg/dL) forwomen, and triglycerides (TG) ≥1.7 mmol/L (150 mg/dL) [10]. Addi-tional exclusion criteria included pregnancy, current infection andchronic inflammatory disease. No cardiac imaging studies were obtain-ed in this reference population. The study protocol was approved bylocal Ethics Committees/Investigational Review Boards.

2.2. Blood sampling and laboratory analyses

Samples were collected in ethylenediaminetetraacetic acid (EDTA),Lithium Heparin and serum tubes. A summary of available data includ-ing sample types for cTnI testing and laboratory parameters assessed byeach site is provided in Supplemental Table 1.

All measurements were performed on the Abbott ARCHITECT ana-lyzers using commercially available tests (Abbott Laboratories, Wiesba-den, Germany). cTnI was measured using the ARCHITECT STAT highsensitive Troponin-I immunoassay with a limit of detection (LoD) of1.9 ng/L. The lowest cTnI concentration corresponding to a total CV of10% was 3.6 ng/L [17]. eGFR was calculated using the Modification ofDiet in Renal Disease (MDRD) formula [18,19].

2.3. Definition of populations and selection criteria

2.3.1. Presumably healthy populationThe baseline presumably healthy populationwas comprised of 1368

enrolled participants, who confirmed their good health status by ques-tionnaire. For the 99th percentile URL calculation this population wasfurther stratified by age and gender.

2.3.2. Healthy cohortNext we defined a healthy cohort within the presumably healthy

population, according to stringent selection criteria based on the useof surrogate blood biomarkers. We progressively performed differentclassifications according to HbA1c and BNP concentrations and eGFRvalues. Finally, the healthy cohort was most stringently selected usinglaboratory screening methods and defined as those reference individ-uals (n = 634) who had HbA1c b48 mmol/mol, BNP b35 pg/mL, andeGFR N60 mL/min/1.73 m2. For the 99th percentile URL calculationthis healthy population was further stratified by gender.

Participants with available lipid profile measurements (TC, HDL-C,LDL-C and TG) in both populations (715 and 305 individuals in the pre-sumably healthy population and healthy cohort, respectively) wereused to evaluate the impact of dyslipidemia on the 99th percentile URL.

2.4. Statistical analysis

Continuous variables were expressed as medians with 25th–75thpercentiles and categorical data as numbers and percentages. TheKolmogorov–Smirnov test was used to assess normality of distributionof investigated parameters. Comparison between the groups was per-formed by using the Chi-square test for categorical variables and theMann–WhitneyU-test or the Kruskal–Wallis test for continuous variableswith non-parametric distribution. The Spearman's and Kendall's correla-tion tests were used to analyze associations between variables where ap-propriate. With the use of multiple regression analysis we evaluated theimpact of other variables as potential sources of hs-cTnI variation. Multi-ple attempts of applications of non-linearmodels inmultivariable analysisshowed that they were not substantially more useful than the linear one.hs-cTnI concentrations were logarithmically transformed before their in-troduction inmultiple regression analysis in order to improve their adher-ence to the normal distribution and to decrease/eliminate the presence ofoutliers. p value b0.05 was considered statistically significant.

The hs-cTnI 99th percentile URL values were determined by using oftwo methods: non-parametric percentile method and robust method,

258 M. Krintus et al. / International Journal of Cardiology 187 (2015) 256–263

both described and recommended in the CLSI C28-A3c document [8].Ninety percent confidence intervals (CI) were calculated for the 99thpercentile URL values, if possible. We excluded outlier observationsfrom the analyzed population based on the method described by Reedet al. [20]. Statistical analysis was performed using Analyse-it 2.2 forMicrosoft Excel (Analyse-it Software, Leeds, UK) and MedCalc 12.7.0(MedCalc Software, Ostend, Belgium).

3. Results

3.1. Characteristics of the study population and major findings of basicstatistics

Baseline characteristics of the study population, including the pre-sumably healthy population (n = 1368) and healthy cohort (n =634), are presented in Table 1. The presumably healthy populationhad a higher median age than the healthy population and men wereolder than women in both cohorts. Additionally, the proportion ofwomenwas slightly higher thanmen at 58% and 56% in the presumablyhealthy and healthy population, respectively. Detectable hs-cTnI con-centrations with values equal or higher than LoD (1.9 ng/L) werefound in 64% (n= 881) and in 54% (n= 341) of individuals in the pre-sumably healthy population and healthy cohort, respectively. Elevensubjects in the presumably healthy population had hs-cTnI valuesabove the 99th percentile URL. The median hs-cTnI concentration wassignificantly lower in the healthy cohort than in the presumably healthypopulation and men had significantly higher concentrations of hs-cTnIthan women regardless of the investigated population. BNP concentra-tions were significantly lower in men compared to women. HbA1c, cre-atinine and eGFR values were higher in men than in women in bothpopulations. Dyslipidemia was found in 76% of the study participantsfrom the presumably healthy population and in 66% of subjects fromthe healthy cohort. There were significant differences in the lipid pa-rameters between the presumably healthy and healthy population, ex-cept for HDL-C concentration.

Median hs-cTnI concentrations and 99th percentile URL values forboth the presumably healthy population and healthy cohort from eachparticipating site are shown in Supplemental Table 2 and 3.

We observed a non-parametric (positively right-skewed) distribu-tion of hs-cTnI in both, the presumably healthy population and in the

Table 1Baseline characteristics of the study population. Data are presented as medians and interquart

Variable Presumably healthy population

Overall(n = 1368)

Women(n = 798)

Men(n = 570)

p v

Age [years] 51 (37–61) 46 (34–59) 53 (43–63) b0Current or former smokerd 135 (32%) 79 (30%) 56 (36%) 0Positive history ofpremature CADd

100 (24%) 77 (29%) 23 (15%) 0

BMI [kg/m2]d 24.5 (21.9–27.7) 23.5 (21.0–26.7) 26.5 (24.4–29.4) 0hs-cTnI [ng/L] 2.4 (1.4–3.8) 2.1 (1.1–3.1) 3.2 (2–4.6) b0BNP [pg/mL] 17.7 (10.0–30.4) 20.5 (11.9–35.0) 13.0 (10.0–25.0) b0HbA1c [mmol/mol] 36.6 (34.4–39.9) 35.5 (33.3–38.8) 37.7 (35.5–40.9) b0Creatinine [μmol/L] 70.7 (63.6–79.5) 65.4 (60.5–70.1) 80.4 (74.3–88.6) b0eGFR [mL/min/1.73 m2] 86.1 (77.3–96.5) 85.1 (77.0–94.2) 87.9 (77.8–98.1) 0TC [mmol/L] 5.4 (4.8–6.2) 5.4 (4.7–6.2) 5.5 (4.9–6.2) 0LDL-C [mmol/L] 3.2 (2.6–3.9) 3.1 (2.5–3.8) 3.5 (2.8–4.1) 0HDL-C [mmol/L] 1.5 (1.3–1.8) 1.6 (1.4–1.9) 1.3 (1.1–1.6) 0TG [mmol/L] 1.1 (0.8–1.5) 1.0 (0.7–1.3) 1.0 (0.7–1.3) 0Dyslipidemiae 544 (76%) 293 (72%) 251 (82%) 0

BMI, bodymass index; BNP, B-type natriuretic peptide; CAD, coronary artery disease; eGFR, estprotein cholesterol; hs-cTnI, high-sensitivity cardiac troponin I; LDL-C, low density lipoprotein

a For comparison between presumably healthy population and healthy cohort.b For comparison between women and men within the presumably healthy population.c For comparison between women and men within the healthy cohort.d Data available only for study participants recruited in Poland (n = 423).e The proportion of individuals with dyslipidemia was calculated for study participants with

healthy cohort. A tighter distribution and less tailing were seen in thehealthy cohort (Fig. 1).

Correlation analysis showed a number of significant relationshipsbetween hs-cTnI and other laboratory and demographic variables(Table 2). There was a significant positive correlation between hs-cTnIconcentration and age in the presumably healthy population, howeverthe strength of this relationship was negligible when only healthy sub-jects were entered in the analysis (Fig. 2). This finding was further sup-ported by the regression analysis. In linear regression, age aloneachieved an R2 of 0.16 for the presumably healthy population to predictlog-transformed hs-cTnI values, whereas for the healthy cohort therewas no similar relationship. Bodymass index (BMI), HbA1c, BNP, creat-inine, eGFR and lipid profile parameters were weakly, but statisticallysignificantly correlated with hs-cTnI concentration in the presumablyhealthy population. In the healthy cohort however, only BMI, HbA1c,creatinine and lipid profile parameters were associated with hs-cTnIconcentration (Table 2, Supplemental Fig. 1). Additionally, smoking sta-tus, but not family history of premature CAD, was linked with increasedhs-cTnI concentration both in the presumably healthy population (theKendall's correlation coefficient= 0.176; p b 0.0001) and in the healthycohort (the Kendall's correlation coefficient = 0.160; p b 0.0001).

3.2. Potential sources of hs-troponin variation presented by using multipleregression analysis

To examine in depth the relationship between log-transformed hs-cTnI concentration expressed as a continuous variable and other labora-tory and demographic variables, we developed multiple linearregressionmodels. Online Supplemental Table 4 shows the adjusted as-sociations of hs-cTnI values in the presumably healthy population andin the healthy cohort. In the presumably healthy population bothHbA1c and BNP concentrations were significantly associated with log-transformed hs-cTnI concentration after adjustment for age and gender.However, after adjustment for dyslipidemia, only BNP remained signif-icantly related to log-transformed hs-cTnI concentration and this modelexplained 25% of the variation for log-transformed hs-cTnI. In contrast,in the healthy cohort after adjustment for age and gender, only HbA1cconcentration contributed to higher values of log-transformed hs-cTnI.When dyslipidemia was entered in the model, none of the biomarkersremained significantly correlated with log-transformed hs-cTnI

ile ranges or numbers and percentages.

Healthy cohort p valuea

alueb Overall(n = 634)

Women(n = 354)

Men(n = 280)

p valuec

.0001 44 (34–54) 42 (34–52) 47 (36–56) 0.0001 0.0001

.24 83 (27%) 45 (24%) 38 (32%) 0.12 0.0006

.0013 67 (22%) 53 (28%) 14 (12%) 0.001 0.53

.0001 24.3 (21.9–27.3) 23.4 (21.0–25.6) 26.2 (24.2–28.8) 0.0001 0.12

.0001 2.0 (1.1–3) 1.8 (0.8–2.5) 2.4 (1.4–3.5) 0.0001 0.0001

.0001 14.0 (10.0–21.1) 16.0 (10.4–23.0) 10.5 (10.0–18.2) 0.0001 0.0001

.0001 36.6 (34.4–38.8) 35.5 (33.3–37.7) 37.7 (34.4–39.9) 0.0001 0.03

.000 70.7 (63.6–78) 65.4 (61.0–68.9) 78.7 (73.4–87.5) 0.0001 0.79

.011 88 (80.2–97) 86.4 (80.3–93.7) 90.9 (80.8–101.1) 0.0001 0.0003

.22 5.1 (4.5–5.7) 5.0 (4.4–5.7) 5.2 (4.6–5.8) 0.1 0.0001

.0004 3.1 (2.5–3.7) 2.9 (2.3–3.7) 3.4 (2.7–3.9) 0.0007 0.007

.0001 1.5 (1.2–1.8) 1.6 (1.4–1.9) 1.2 (1.1–1.5) 0.0001 0.47

.0001 1.0 (0.7–1.4) 0.8 (0.7–1.2) 1.2 (0.9–1.7) 0.0001 0.04

.001 200 (66%) 112 (60%) 88 (75%) 0.006 0.001

imated glomerular filtration rate; HbA1c, glycated hemoglobin; HDL-C, high-density lipo-cholesterol; TC, total cholesterol; TG, triglycerides.

available lipid profile.

Fig. 1. Distribution of hs-cTnI concentrations in the presumably healthy (n = 1368); healthy cohort (n = 634). hs-cTnI, high-sensitivity cardiac troponin I.

259M. Krintus et al. / International Journal of Cardiology 187 (2015) 256–263

concentration, only age and gender significantly affected log-transformed hs-cTnI concentration. In an analysis restricted to partici-pants recruited in Poland, where data on smoking status and BMIwere available, smoking status, but not BMI, was independently associ-ated with hs-cTnI concentration both in the presumably healthy popu-lation and in the healthy cohort (Supplemental Table 4).

3.3. hs-cTnI 99th percentile URL values in the presumably healthypopulation

Values of the 99th percentile URL in the presumably healthy popula-tion are shown in Table 3.

The 99th percentile URL values (with 90% CI) for the overall presum-ably healthy population were 23.7 (15.6–33.5) and 14.1 (11.4–16.7)ng/L with the use of the non-parametric percentile and the robust

Table 2The Spearman coefficients for hs-cTnI, age, bodymass index and laboratory parameters correlatcorrelation coefficients are marked with * and ** for p b 0.0001 and p b 0.05, respectively.

Variable (number of participantswith available data)

Presumably healthy opulation

Overall (n = 1368) Women (n = 798) M

Age (n = 1368) 0.43* 0.41*BMI (n = 409)a 0.32* 0.24*HbA1c (n = 904) 0.29* 0.22*BNP (n = 839) 0.13* 0.15**Creatinine (n = 1337) 0.28* 0.09**eGFR (n = 1337) −0.21* −0.23* −TC (n = 715) 0.22* 0.31*HDL-C (n = 698) −0.19* 0.08LDL-C (n = 568) 0.25* 0.25*TG (n = 709) 0.22* 0.20* −

BMI, bodymass index, BNP, B-type natriuretic peptide; eGFR, estimated glomerularfiltration ratsensitivity cardiac troponin I; LDL-C, low density lipoprotein cholesterol; TC, total cholesterol;

a Data available only for study participants recruited in Poland.

methods, respectively. The derived 99th percentile cutoffs were mark-edly higher for males compared to females and increased with in-creased age for both women and men (Table 3 and SupplementalFig. 2). Additionally, we found considerably higher values of the 99thpercentile URL in smokers than in non-smokers. In the course of furtheranalysis, we identified and excluded 3 additional outliers. Therefore, thefinal 99th percentile values for hs-cTnI in the presumably healthy pop-ulationwere 20.1 (14.2–26.5) and 12.9 (10.3–15.5) ng/L with the use ofthe non-parametric percentile and the robust methods, respectively.

3.4. hs-cTnI 99th percentile values in the healthy cohort

Values of 99th percentile for hs-cTnI in the healthy cohort classifiedaccording to surrogate biomarkers are summarized in Table 4.

ion in the presumably healthy population and in the healthy cohort. Statistically significant

Healthy cohort

en (n = 570) Overall (n = 634) Women (n = 354) Men (n = 280)

0.38* 0.09** 0.06 0.060.14 0.27* 0.17** 0.100.30* 0.24* 0.18* 0.26*0.30* −0.03 −0.05 0.15**0.18* 0.23* 0.10 0.050.27* −0.03 −0.10 −0.040.07 0.14** 0.21* 0.010.03 −0.32* −0.12 −0.21**0.11 0.23* 0.24* 0.090.01 0.27* 0.14 0.18

e;HbA1c, glycatedhemoglobin;HDL-C, high-density lipoprotein cholesterol; hs-cTnI, high-TG, triglycerides.

Fig. 2. Scatter plots of the relationship between age and log hs-cTnI: A. in the presumably healthy (n = 1368); B. in the healthy cohort (n = 634). hs-cTnI, high-sensitivity cardiac troponin I.

260 M. Krintus et al. / International Journal of Cardiology 187 (2015) 256–263

The progressive use of stringent biomarker selection criteria sub-stantially reduced the number of subjects from 839 to 634 as well as de-creased the 99th percentile from 15.2 ng/L (6.3 ng/L) to 11.2 ng/L(7.1 ng/L) using non-parametric (robust) method. 99th percentile forwomen was significantly lower compared to men. eGFR separatelyhad no and HbA1c had only a marginal effect on the derived hs-cTnI99th percentile values. The inclusion of criterion based on BNP valuesb35 pg/mL had the most pronounced effect on the 99th percentile forhs-cTnI and substantially reduced the number of persons in the healthycohort. Neither addition of eGFR nor HbA1c to the exclusion criteriabased exclusively on BNP had considerable impact on the hs-cTnI 99thpercentile values. Finally, both the median hs-cTnI concentrations andthe hs-cTnI 99th percentile values did not differ substantially amongage ranges (Table 4 and Supplemental Fig. 2). No outliers were identi-fied in the course of the analysis.

Importantly, the hs-cTnI 99th percentile values were higher insmokers than in non-smokers according to both the non-parametric(12.9 vs. 6.7 ng/L; insufficient sample sizes to determine 90% CI) and ro-bust (8.8 [7.4–10.1] vs. 5.2 [4.8–5.6] ng/L) methods. One outlier(16.1 ng/L) was identified in non-smokers.

4. Discussion

Ourmulticenter study evaluating the 99th percentile URL has clearlyidentified multiple factors that influenced the determination of thiscritically important decision limit. First and foremost, we have demon-strated that the application of different selection criteria and two

Table 3Values of 99th percentile for hs-cTnI with 90% confidence intervals in relation to gender and a

Presumably healthy population according to: 99th percentilpercentile me

Gender All (n = 1368) 23.7 (15.6–33Women (n = 798) 12.9 (9.7–16.1Men (n = 570) 35.2 (23.5–72

Age b40 years (n = 408) 8.6 (7.7–11.8)40–60 years (n = 590) 13.2 (9.2–26.5N60 years (n = 368) 58.9 (28–86.7

Gender and age Women b40 years (n = 292) 8.4b

Men b40 years (n = 116) 11.6b

Women 40–60 years (n = 323) 10.9 (7.3–16.1Men 40–60 years (n = 263) 20.9b

Women N60 years (n = 178) 23.8b

Men N60 years (n = 189) 73.5b

Smoking statusc Current/former (n = 135) 37.4b

Never (n = 288) 10.0b

a Outliers were identified by the MedCalc 12.7.0 software (MedCalc Software, Ostend, Belgiub Insufficient sample size to determine 90% confidence interval.c Data available only for study participants recruited in Poland.

independent statistical approaches may result in a large variability inthe 99th percentile URL, despite the use of the same hs-cTnI assay andthe same baseline reference population. Male gender, BNP and HbA1cwere independently associated with hs-cTnI concentration in the pre-sumably healthy population,while the impact of age on hs-cTnI concen-tration, present in the univariate analysis, decreased after adjustmentsfor gender and surrogate biomarkers. The inclusion of criterion basedon BNP values b35 pg/mL had the most pronounced effect on the 99thpercentile URL value. Additionally, we have shown that selection ofthe reference populations exclusively based on the questionnaire ledto substantial differences in the 99th percentile values among the par-ticipating sites. As demonstrated in our study, the choice of selectioncriteria determines this key threshold and therefore may potentially af-fect clinical decision making.

Particular strengths of our study include the number of healthy indi-viduals screened, its multinational design, and the statistical approachfor the determination of the 99th percentile URLs [21,22]. Our healthycohort consisted of a greater number of participants than previouslyconducted studies [6,23,24]. Additionally, HbA1c measurements werenot performed in any of these studies. Moreover, the majority of thestudies that determined 99th percentile URLs for hs-cTn assays provid-ed only an imprecise description of the applied statistical methods.Thus, a particular advantage of this study is a detailed statistical ap-proach including both the recommended non-parametric percentileand the robustmethods for the determination of hs-cTnI 99thpercentilevalues [8]. The robust method is recommended for the most skewedpopulation, as having a slight advantage over the non-parametric

ge in the presumably healthy population.

e non-parametricthod [ng/L]

99th percentile robustmethod [ng/L]

hs-cTnI excludedvaluea [ng/L]

.5) 14.1 (11.4–16.7) None) 8.2 (7–9.3) 53.4) 19.1 (14.2–23.4) None

5.7 (5.2–6.2) None) 8.7 (7.3–10.2) 66.9) 22.8 (16.6–28.3) None

5.2 (4.6–5.7) None6.6 (5.5–7.7) None

) 6.3 (5.5–7.1) 33.89.7 (7.6–11.5) None

11.6 (9.3–13.8) None27.2 (17.7–35.3) None14.9 (9.5–19.3) 72.06.9 (5.5–8.2) 33.8

m) using the method described by Reed et al. [20].

Table 4Values of 99th percentile for hs-cTnIwith 90% confidence intervals in the healthy cohort classified according to surrogate biomarkers and age ranges. The percentages are calculated as theproportions of individuals in the particular subgroup and all questionnaire screened individuals with measured HbA1c and BNP concentrations and calculated eGFR.

Population hs-cTnI 99th percentile value [ng/L]a

Non-parametric percentile method Robust method

All Women Men All Women Men

Questionnaire screened individuals with measured HbA1cand BNP concentrations and calculated eGFR[n = 839; 353 men]

15.2 (10.9–24.3) 9.1 (8.2–13.7) 25.3 (13.8–39.3) 6.3 (5.7–6.9) 6.2 (5.6–6.9) 11.6 (9.2–13.9)

Cohort after exclusion of 33 subjects with elevated HbA1c[n = 806 (96.1%); 330 men]

13.8 (9.7–24.3) 9.2 (8.0–13.7) 25.8 (11.8–39.3) 8.9 (7.5–10.3) 6.2 (5.6–6.9) 11.6 (9.0–14.0)

Cohort after exclusion of 19 subjects with decreased eGFR[n = 820 (97.7%); 341 men]

15.6 (910.6–24.3) 9.1 (8.2–13.7) 25.5 (11.8–39.3) 8.9 (7.5–10.3) 6.2 (5.6–6.9) 11.6 (9.0–14.0)

Cohort after exclusion of 175 subjects with elevated BNP[n = 664 (72%); 301 men]

11.0 (8.2–16.1) 9.2 (7.2–16.1) 11.8 (8.2–26.5) 7.1 (6.2–8.0) 5.8 (5.0–6.6) 8.3 (6.7–9.9)

Cohort after exclusion of 50 subjects with elevatedHbA1c and decreased eGFR[n = 789 (94%); 320 men]

14.0 (9.7–24.3) 9.2 (8.0–16.1) 26.0 (11.08–39.3) 8.8 (7.3–10.2) 6.2 (5.5–6.8) 11.5 (8.8–14.0)

Cohort after exclusion of 198 subjects with elevated HbA1c and BNP[n = 641 (76.4%); 284 men]

11.1 (8.2–16.1) 9.2 (7.2–16.1) 12.9b 7.1 (6.1–8.0) 5.8 (5.0–6.6) 8.3 (6.6–10.0)

Cohort after exclusion of 184 subjects with decreasedeGFR and elevated BNP[n = 655 (78.1%); 295 men]

11.1 (8.2–16.1) 9.3 (7.2–16.1) 12.1b 7.1 (6.2–8.0) 5.8 (5.0–6.6) 8.3 (6.7–9.9)

Final healthy cohort after exclusion of 205 subjects withabnormal HbA1c, BNP and eGFR[n = 634 (75.6%); 280 men]

11.2 (8.2–16.1) 9.3 (7.2–16.1) 13.2b 7.1 (6.1–8.1) 5.8 (5.0–6.6) 8.3 (6.6–10.0)

Final healthy cohort subdivided according to age rangesb40 years (n = 240)40–60 years (n = 322)N60 years (n = 72)

10.4b

15.3 (8.2–26.5)11.3b

9.0b

13.7b

NA

11.8b

22.5b

NA

6.1 (5.4–6.8)7.9 (6.2–9.4)6.8 (5.5–8.0)

5.6 (4.9–6.3)6.2 (4.7–7.6)NA

6.9 (5.4–8.2)9.4 (6.5–11.9)NA

hs-cTnI, high-sensitivity cardiac troponin I; NA, not applicable due to insufficient sample size to determine the 99th percentile value.a Outliers were identified by the MedCalc 12.7.0 software (MedCalc Software, Ostend, Belgium) using the method described by Reed et al. [20].b Insufficient sample size to determine 90% confidence interval.

261M. Krintus et al. / International Journal of Cardiology 187 (2015) 256–263

method, though leading to the narrower CI [25,26]. We have clearly in-dicated that bothmethods lead to substantially different 99th percentilevalues, pointing to a large number of outliers or right-skewed results.On other hand, the use of outlier detection is not routinely recommend-ed, because higher values from a skewed population may bemislabeledas outliers [25]. With this in mind, the use of the robust method seemsreasonable, however further clinical outcome-oriented studies compar-ing both statistical approaches are required. These advantages allow ourstudy to confirm and substantially extend the innovative observationson the influence of population selection on the URL value made byCollinson et al. [6].

We have shown that exclusion of subjectswith abnormal concentra-tions of surrogate biomarkers reduced the 99th percentile value for hs-cTnI approximately by a half, both for the non-parametric percentileand robust methods. Moreover, the 99th percentile value for hs-cTnIin the overall presumably healthy reference population wasmuch lower than the value reported in the package insert (14.1 vs.26.2 ng/L), althoughwe used the same assay, analytical platform, statis-tical approach (the robustmethod) and similar sample size [17,27]. Themost challenging issue in the derivation of 99th percentile values fromthe healthy reference population is how to exclude those who are af-fected by cardiac, metabolic and renal disorders. There is a common be-lief, that the traditional approach for defining the reference population,using healthy blood donors, ambulant outpatients or questionnaire-screened volunteers is not sufficient [28,29]. A recent expert review rec-ommends that reference populations for the determination of the 99thpercentile value should include: clinical history and medication use,surrogate biomarkers for diabetes, myocardial and renal dysfunction,sufficient sample size with minimum of 300 men and women with di-verse distribution of ages, appropriate statistical approach and finallydescription of specimen type [4,5]. Importantly, in other studies con-ducted in the healthy populations, median hs-cTnI concentrationswere comparable among studies, but not the 99th percentile values [7,23,24]. This suggests that a large percentage of concentrations deviatefrom normality, making distribution more right-skewed, with probably

a high number of outliers. Although in the overall population distribu-tion of hs-cTnI concentrations is right-skewed, the Gaussian or at leastnear-Gaussian distribution is expected in the truly healthy population[28]. In our healthy cohort the distribution was tighter with less tailingand closer to a Gaussian distribution in line with other studies [30,31].

Another important issue is the percentage of detectable (≥ LoD) hs-cTnI values in the healthy population. This parameter varied in ourstudy depending on the investigated population and was higher forthe presumably healthy population than for the healthy cohort (64%vs. 54%). These values are substantially lower than in previousquestionnaire-based studies utilizing the same assay (e.g., 92.3% [32],96% [33], 98.6% [34]). Our observation underscores the fact that the per-centage of hs-cTn detectability is highly dependent on the populationselection. The healthier and more selected population, the lower theproportion of subjectswith detectable hs-cTn, regardless of the sensitiv-ity of the assay.

Wehave identified that gender, smoking status and to a lesser extentage are important factors influencing the hs-cTnI concentrations andconsequently the derived 99th percentile values. Male gender contrib-uted in our study to higher values of the 99th percentile URL. This find-ing is consistent with other studies, which reported gender-specificcutoffs for hs-cTn [6,23,24], although in a study by Koerbin et al. thegender difference was less pronounced [24]. The gender differencemay be partially explained by the average heart muscle being larger inmales when compared to females. The potentially profound clinical im-portance of gender specific cutoffs was demonstrated from the prelim-inary results of a study byMills et al. that showed that the use of gender-specific diagnostic thresholds for hs-cTnI (men 34 ng/L and women16 ng/L) markedly increased the diagnosis of MI in women (from 13%to 23%; p b 0.001) and improved outcomes but had a minimal effect inmen (from 23% to 24%, p = 0.021) [35]. In contrast to other predomi-nantly questionnaire-based reports [7,32,36], but in line with asurrogate biomarker-guided study by Collinson et al. [6], age was pre-dominantly associatedwith elevated cTn concentrations in our presum-ably healthy population and did not affect the value of 99th percentile

262 M. Krintus et al. / International Journal of Cardiology 187 (2015) 256–263

URL in our healthy cohort. The association between smoking status andhs-cTnI concentration observed in our study may be explained by thepresence of subclinical CAD in smokers. Nevertheless, we hypothesizethat application of strict laboratory selection criteria in our study is re-sponsible for this finding by excluding a vast majority of individualswith subclinical CAD and left ventricular dysfunction.

Introduction of BNP as a surrogate biomarker for myocardial dys-function and subclinical CAD contributed to themost effective selectionof the healthy cohort with the exclusion of 21% of individuals from thepresumably healthy population, which reduced the 99th percentilevalues substantially. Efficacy of N-terminal pro-BNP to facilitate popula-tion selectionwas previously demonstrated byKeller et al. in the Guten-berg Health Study, but with the use of contemporary sensitive cTnI [7].Of major importance, almost identical 99th percentile values were de-termined using elevated BNP concentration for exclusion when com-pared with application of all biomarkers for exclusion.

Among laboratory biomarkers in our study, HbA1c substantially in-fluenced hs-cTnI concentrations in univariate andmultivariate analysis.Positive correlations were present in both the presumably healthy pop-ulation and healthy cohort. Our results are in agreement with the Ath-erosclerotic Risk in Communities study [37]. The causal role of thisassociation has yet to be elucidated, but evidence suggest that subclini-cal cardiac damage may begin much earlier, before CAD becomes clini-cally apparent [38]. Proposed mechanisms of this phenomenoninclude: hyperglycemia-mediatedmicrovascular dysfunction, oxidativestress, adverse glycation endproducts andmyocardial fibrosis [37,38]. Aminor impact of eGFR on hs-cTnI concentrations and the 99th percentilevalue observed in our data is in line with a study by McKie et al. [23].

In addition to controversies regarding optimal determination of 99thpercentile URL, an accumulating body of evidence question its clinicalusefulness [5,29]. Previous studies assessing prognostic values of hs-cTn in patients with acute coronary syndromes have indicated the pres-ence of a progressively increasing risk from the assay LoD, without anyarbitrary cutpoint [29,39,40]. This continuum represents the relation-ship between the number of cardiomyocytes undergoing necrosis dueto thromboembolism from unstable coronary plaque and the occur-rence of unfavorable clinical events.

Several limitations of our study should be acknowledged. Firstly,surrogate biomarkers as well as data on smoking status, family historyof premature CAD and BMIwere not available for the entire presumablyhealthy population included in the study. Secondly, wewere not able toestablish the 99th percentile of hs-cTnI using the non-parametric per-centile method in some subgroups due to their limited size. Thirdly,we did not perform cardiac imaging examinations. However, the lowcutoff value (35 pg/mL) for BNP applied in our study was demonstratedto successfully identify patient with both heart failure and left ventricu-lar dysfunction [14,41,42]. Fourthly, the investigated population, al-though multinational, was restricted to white Europeans.

In conclusion, our study clearly indicates that selection of the refer-ence population has a critical impact on the value of the 99th percentileof hs-cTnI. Additionally, it underscores the need for further clinical stud-ies aimed to establish the optimal protocol for the selection of the refer-ence population and the optimal cutpoint for diagnosis of MI. Until suchstudies are completed, we suggest in everyday clinical decision making,particularly in patients with borderline hs-cTn concentrations, to inte-grate the clinical presentation and ECG findings rather with the dynam-ics of hs-cTn concentrations than with the absolute results of hs-cTnmeasurement.

Supplementary data to this article can be found online at http://dx.doi.org/10.1016/j.ijcard.2015.03.282.

Conflict of interest

The minor support by Abbott Diagnostics did not influence the con-tent of this paper. L. Lennartz and J. Shih are employees of Abbott

Laboratories. G. Lefevre received speaker honoraria from Abbott. Otherauthors declared no conflict of interest.

Acknowledgments

The study was designed by the researchers from the participatingsites and Abbott Laboratories. Abbott Laboratories provided thereagents, calibrators, controls and minor support for this study. Wethank the laboratory scientists and technicians who participated in thestudy, particularly Magali Grastilleur (Biochimie, Paris), RosemarieLott (Laboratory Medicine, Mainz) and Rønnaug Grude (Clinical Bio-chemistry, Stavanger).

References

[1] K. Thygesen, J.S. Alpert, A.S. Jaffe, et al., Third universal definition of myocardial in-farction, J. Am. Coll. Cardiol. 60 (2012) 1581–1598.

[2] K. Thygesen, J. Mair, E. Giannitsis, et al., How to use high-sensitivity cardiac tropo-nins in acute cardiac care, Eur. Heart J. 33 (2012) 2252–2257.

[3] J.T. Neumann, A.S. Havulinna, T. Zeller, et al., Comparison of three troponins as pre-dictors of future cardiovascular events—prospective results from the FINRISK andBiomaCaRE studies, PLoS One 9 (2014) e90063.

[4] F.S. Apple, P.O. Collinson, Analytical characteristics of high-sensitivity cardiac tropo-nin assays, Clin. Chem. 58 (2012) 54–61.

[5] Y. Sandoval, F.S. Apple, The global need to define normality: the 99th percentilevalue of cardiac troponin, Clin. Chem. 60 (2014) 455–462.

[6] P.O. Collinson, Y.M. Heung, D. Gaze, et al., Influence of population selection on the99th percentile reference value for cardiac troponin assays, Clin. Chem. 58 (2012)219–225.

[7] T. Keller, F. Ojeda, T. Zeller, et al., Defining a reference population to determine the99th percentile of a contemporary sensitive cardiac troponin I assay, Int. J. Cardiol.167 (2013) 1423–1429.

[8] Clinical Laboratory Standards Institute, Defining, Establishing and Verifying Refer-ence Intervals in the Clinical Laboratory; Approved Guideline, Third Edition Clinicaland Laboratory Standards Institute, Wayne, PA, 2010. EP28-A3c().

[9] S. Yusuf, S. Hawken, S. Ounpuu, et al., Effect of potentially modifiable risk factors as-sociated with myocardial infarction in 52 countries (the INTERHEART study): case–control study, Lancet 364 (2004) 937–952.

[10] F.M. Task, J. Perk, G. De Backer, et al., European Guidelines on cardiovascular diseaseprevention in clinical practice (version 2012): The Fifth Joint Task Force of theEuropean Society of Cardiology and Other Societies on Cardiovascular Disease Pre-vention in Clinical Practice (constituted by representatives of nine societies and byinvited experts) developed with the special contribution of the European Associa-tion for Cardiovascular Prevention & Rehabilitation (EACPR), Eur. Heart J. 33(2012) 1635–1701.

[11] F.M. Costa, J. Ferreira, C. Aguiar, et al., Impact of ESC/ACCF/AHA/WHF universal def-inition of myocardial infarction on mortality at 10 years, Eur. Heart J. 33 (2012)2544–2550.

[12] R.B. Schnabel, P.S. Wild, A. Schulz, et al., Multiple endothelial biomarkers and nonin-vasive vascular function in the general population: the Gutenberg Health Study, Hy-pertension 60 (2012) 288–295.

[13] P. Rustad, P. Simonsson, P. Felding, et al., Nordic Reference Interval Project Bio-bankand Database (NOBIDA): a source for future estimation and retrospective evaluationof reference intervals, Scand. J. Clin. Lab. Invest. 64 (2004) 431–438.

[14] J.J. McMurray, S. Adamopoulos, S.D. Anker, et al., ESC Guidelines for the diagnosisand treatment of acute and chronic heart failure 2012, Eur. J. Heart Fail. 14 (2012)803–869.

[15] The International Expert Committee, International expert committee report on therole of the A1C assay in the diagnosis of diabetes, Diabetes Care 32 (2009)1327–1334.

[16] Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group, KDIGO2012 clinical practice guideline for the evaluation and management of chronic kid-ney disease, Kidney Int. Suppl. 3 (2013) 1–150 (http://www.kdigo.org/clinical_practice_guidelines/pdf/CKD/KDIGO_2012_CKD_GL.pdf (Accessed May 25, 2014)).

[17] Abbott ARCHITECT STAT High Sensitive Troponin—I. Package Insert2013. (G1-0139/R02).

[18] A.S. Levey, J.P. Bosch, J.B. Lewis, et al., A more accurate method to estimate glomer-ular filtration rate from serum creatinine: a new prediction equation. Modificationof Diet in Renal Disease Study Group, Ann. Intern. Med. 130 (1999) 461–470.

[19] A.S. Levey, J. Coresh, T. Greene, et al., Using standardized serum creatinine values inthe modification of diet in renal disease study equation for estimating glomerularfiltration rate, Ann. Intern. Med. 145 (2006) 247–254.

[20] A.H. Reed, R.J. Henry, W.B. Mason, Influence of statistical method used on theresulting estimate of normal range, Clin. Chem. 17 (1971) 275–284.

[21] International Federation of Clinical Chemistry and Laboratory Medicine, TroponinAssay Analytical Characteristics, http://www.ifcc.org/media/245202/IFCC%20Tropo-nin%20I%20and%20T%20(ng_L%20units)_%20update%20December%202013.pdf2013 (Accessed August 2, 2014).

[22] E.P. Cardinaels, A.M. Mingels, L.H. Jacobs, et al., A comprehensive review of upperreference limits reported for (high-)sensitivity cardiac troponin assays: the chal-lenges that lie ahead, Clin. Chem. Lab. Med. 50 (2012) 791–806.

263M. Krintus et al. / International Journal of Cardiology 187 (2015) 256–263

[23] P.M. McKie, D.M. Heublein, C.G. Scott, et al., Defining high-sensitivity cardiactroponin concentrations in the community, Clin. Chem. 59 (2013) 1099–1107.

[24] G. Koerbin, W.P. Abhayaratna, J.M. Potter, et al., Effect of population selection on99th percentile values for a high sensitivity cardiac troponin I and T assays, Clin.Biochem. 46 (2013) 1636–1643.

[25] P.S. Horn, A.J. Pesce, B.E. Copeland, A robust approach to reference intervalestimation and evaluation, Clin. Chem. 44 (1998) 622–631.

[26] P.S. Horn, A.J. Pesce, B.E. Copeland, Reference interval computation using robust vsparametric and nonparametric analyses, Clin. Chem. 45 (1999) 2284–2285.

[27] M. Krintus, M. Kozinski, P. Boudry, et al., Europeanmulticenter analytical evaluationof the Abbott ARCHITECT STAT high sensitive troponin I immunoassay, Clin. Chem.Lab. Med. 52 (2014) 1657–1665.

[28] P. Collinson, Reference ranges for cardiac troponin in the era of high sensitivityassays, Clin. Biochem. 46 (2013) 1629–1630.

[29] P.E. Hickman, B. Lindahl, J.M. Potter, et al., Is it time to do away with the 99thpercentile for cardiac troponin in the diagnosis of acute coronary syndrome andthe assessment of cardiac risk? Clin. Chem. 60 (2014) 734–736.

[30] K.M. Eggers, P. Venge, B. Lindahl, et al., Cardiac troponin I levels measured with ahigh-sensitive assay increase over time and are strong predictors of mortality inan elderly population, J. Am. Coll. Cardiol. 61 (2013) 1906–1913.

[31] G. Koerbin, J.M. Potter, W.P. Abhayaratna, et al., The distribution of cardiac troponin Iin a population of healthy children: lessons for adults, Clin. Chim. Acta 417 (2013)54–56.

[32] T.C. Aw, S.K. Phua, S.P. Tan, Measurement of cardiac troponin I in serumwith a newhigh-sensitivity assay in a large multi-ethnic Asian cohort and the impact of gender,Clin. Chim. Acta 422 (2013) 26–28.

[33] F.S. Apple, R. Ler, M.M. Murakami, Determination of 19 cardiac troponin I and Tassay 99th percentile values from a common presumably healthy population, Clin.Chem. 58 (2012) 1574–1581.

[34] G. Koerbin, J. Tate, J.M. Potter, et al., Characterisation of a highly sensitive troponin Iassay and its application to a cardio-healthy population, Clin. Chem. Lab. Med. 50(2012) 871–878.

[35] N.L. Mills, High-STEACS: high-sensitivity cardiac troponin and the under diagnosisof myocardial infarction in women, ESC Congress 2013, 2013 (http://www.escardio.org/congresses/esc-2013/congress-reports/Pages/711-High-STEACS.aspx#.U6dRGUBAfjI (Accessed 2 August 2014)).

[36] M.O. Gore, S.L. Seliger, C.R. deFilippi, et al., Age- and sex-dependent upper referencelimits for the high-sensitivity cardiac troponin T assay, J. Am. Coll. Cardiol. 63 (2014)1441–1448.

[37] J. Rubin, K. Matsushita, C.M. Ballantyne, et al., Chronic hyperglycemia and subclinicalmyocardial injury, J. Am. Coll. Cardiol. 59 (2012) 484–489.

[38] H.D. White, Pathobiology of troponin elevations: do elevations occur with myocar-dial ischemia as well as necrosis? J. Am. Coll. Cardiol. 57 (2011) 2406–2408.

[39] E.A. Bohula May, M.P. Bonaca, P. Jarolim, et al., Prognostic performance of a high-sensitivity cardiac troponin I assay in patients with non-ST-elevation acute coronarysyndrome, Clin. Chem. 60 (2014) 158–164.

[40] N.L. Mills, K.K. Lee, D.A. McAllister, et al., Implications of lowering threshold of plas-ma troponin concentration in diagnosis of myocardial infarction: cohort study, BMJ344 (2012).

[41] P. Krishnaswamy, E. Lubien, P. Clopton, et al., Utility of B-natriuretic peptide levels inidentifying patients with left ventricular systolic or diastolic dysfunction, Am. J. Med.111 (2001) 274–279.

[42] K. Yamamoto, J.C. Burnett Jr., E.A. Bermudez, et al., Clinical criteria and biochemicalmarkers for the detection of systolic dysfunction, J. Card. Fail. 6 (2000) 194–200.