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Soluble Urokinase Plasminogen Activator Receptorfor Risk Prediction in Patients Admitted with Acute

Chest PainStig Lyngbk,1* Charlotte Andersson,2 Jacob L. Marott,3 Daniel V. Mller,4 Michael Christiansen,4

Kasper K. Iversen,5 Peter Clemmensen,5 Jesper Eugen-Olsen,6 Peter R. Hansen,2 and Jrgen L. Jeppesen1

BACKGROUND: Plasma concentrations of soluble uroki-nase plasminogen activator receptor (suPAR) predictmortality in several clinical settings, but the long-termprognostic importance of suPAR in chest pain patientsadmitted on suspicion of nonST-segment elevationacute coronary syndrome (NSTEACS) is uncertain.

METHODS: suPAR concentrations were measured onadmission in 449 consecutive chest pain patients in asinglecenter between January 3, 2005, andFebruary14,2006. Patients were followed for all-cause mortalityfrom discharge until July 28, 2011.

RESULTS: The diagnoses at discharge comprised high-risk NSTEACS [nonST elevation myocardial infarc-tion or unstable angina with electrocardiogram (ECG)abnormalities] in 77 patients (17.2%) and low-riskNSTEACS without evidence of myocardial ischemia in257 (57.2%) of patients. Another 115 (25.6%) of pa-tients received other diagnoses. During a median

follow-up of 5.7 years (range, 0.016.6 years) therewere 162 (36.1%) deaths. suPAR was predictive ofmortality independent of age, sex, smoking, final diag-nosis for the hospitalization, comorbidities (diabetes,hypertension, previous myocardial infarction, andheart failure), and variables measured on the day ofadmission (renal function, inflammatory markers, andmarkers of myocardial ischemia) with a hazard ratio(95% CI) of 1.93 (1.482.51) per SD increase in log-transformed suPAR, P 0.0001. The use of suPARimproved the predictive accuracy of abnormal ECGfindings and increased troponin concentrations re-garding all-cause mortality (cstatistics, 0.7510.805;

P 0.0001).

CONCLUSIONS: suPAR is a strong predictor of adverselong-term outcomes and improves risk stratificationbeyond traditional risk variables in chest pain patientsadmitted with suspected NSTEACS. 2013 American Association for Clinical Chemistry

The postdischarge management of acute chest pain pa-tients admitted for suspected nonST-segment elevationacute coronarysyndrome(NSTEACS)7 remains a clinicalchallenge. The common denominator of acute chest painreflects a heterogeneous blend of diagnoses with varyingprognoses, and onlya minorityof patients with suspectedNSTEACS are subsequently diagnosed with nonST-elevationmyocardial infarction(NSTEMI).Newdiagnostictools may help uncover important pathologies that areotherwise overlooked in the clinical evaluation of thesepatients during their hospital stay.

Risk assessment of patients with suspected

NSTEACS currently relies on medical history, electro-cardiogram (ECG), and troponin measurements. Inte-grated risk scoring systems such as the Global Registryof Acute Coronary Events (GRACE) and the Throm-bolysis in Myocardial Infarction (TIMI) risk scoreshave shown better predictive accuracy for major ad-verse cardiac events at up to 6 months in unselectedchest pain patients than ECG and troponins alone(1 ).These and other clinical risk algorithms were mainlydeveloped for the purpose of identifying patients withhigh-risk ACS. However, patients in whom ACS is ini-tially ruled out may still carry a relatively poor progno-sis with respect to future morbidity and mortality for

both cardiovascular and noncardiovascular causes (2 ).

1 Copenhagen University Hospital, Glostrup, Denmark; 2 Copenhagen UniversityHospital Gentofte, Hellerup, Denmark; 3 Copenhagen City Heart Study, Copen-hagen University Hospital, Frederiksberg, Copenhagen, Denmark; 4 StatensSerum Institut, Copenhagen, Denmark; 5 Copenhagen University Hospital, Rig-shospitalet, Copenhagen, Denmark; 6 Clinical Research Centre, CopenhagenUniversity Hospital, Hvidovre, Denmark.

* Address correspondence to this author at: Department of Medicine, Division ofCardiology, Copenhagen University Hospital Glostrup, Nordre Ringvej 57, 2600Glostrup, Denmark. E-mail [email protected].

Received January 20, 2013; accepted June 24, 2013.Previously published online at DOI: 10.1373/clinchem.2013.203778

7 Nonstandard abbreviations: NSTEACS, nonST-segment elevation acute coro-nary syndrome; NSTEMI, nonST-elevation myocardial infarction; ECG, electro-cardiogram; GRACE, Global Registry of Acute Coronary Events; TIMI, Throm-bolysis in Myocardial Infarction; suPAR, soluble urokinase plasminogenactivator receptor; CVD, cardiovascular disease; PCI, percutaneous coronaryintervention; TnT, cardiac troponin T; HF, heart failure; ICD, InternationalClassification of Disease; CHF, congestive heart failure; CK-MB, creatininekinase MB; CRP, C-reactive protein; NRI, net reclassification improvement; IDI,integrated discrimination improvement; LDL-C, LDL cholesterol; HR, hazardratio; LVEF, left ventricular ejection fraction.

Clinical Chemistry59:11000000 (2013)

Lipids, Lipoproteins, and Cardiovascular Risk Factors

1

http://hwmaint.clinchem.org/cgi/doi/10.1373/clinchem.2013.203778The latest version is atPapers in Press. Published July 10, 2013 as doi:10.1373/clinchem.2013.203778

Copyright (C) 2013 by The American Association for Clinical Chemistry
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Therefore, new biomarkers that are simple to measureand improve risk stratification for the entire spectrumof acute chest pain patients are warranted to guide theuse of any interventional measures.

Soluble urokinase plasminogen activator recep-tor (suPAR) is an inflammatory biomarker, whichis released into the circulation by cleavage of themembrane-bound uPAR from various cells, includ-ing inflammatory and endothelial cells(3 ). uPA anduPAR have been implicated in atherogenesis becauseof their roles in orchestrating cellular adhesion, mi-gration, and proliferation during tissue remodellingin the atherosclerotic plaque(3 ). suPAR is intimatelylinked to these processes and has therefore been sug-gested as a marker of low-grade inflammation (3 ).Increased circulating concentrations of suPAR arestrongly associated with increased risk of cardiovascu-

lar disease (CVD), diabetes, cancer, and mortality inthe general population(4 6 ), with poor outcomes incritically ill patients(7, 8 ), and with recurrent MI andmortality in patients with STEMI undergoing primarypercutaneous coronary intervention (PCI) (9 ). In thepresent retrospective cohort study, we investigatedwhether suPAR was also an independent marker ofoutcome in chest pain patients admitted with sus-pected NSTEACS.

Methods

STUDY POPULATION

The study population included 538 consecutive pa-tients admitted with acute chest pain and suspectedNSTEACS at a single regional Danish hospital betweenJanuary 3, 2005, and February 14, 2006 (10). In Den-mark, patients with suspected STEMI are immediatelytransferred for primary PCI, and therefore these pa-tients were not part of the present study.Blood samplesand complete follow-up data for the current study wereavailable from 449 patients. High-risk NSTEACS wasdiagnosed if patients had ischemic changes in the ECG(ST depression or negative T waves) and/or increasedcardiac troponin T (TnT) as described previously(10).The diagnostic criteria were based on recommenda-

tions in recent guidelines(11 ). Information regardingclinical variables was obtained from patients charts.All patients gave their written informed consent totheir participation in the study. The study compliedwith the Declaration of Helsinki and was approved bythe local ethics committee.

OUTCOME EVENTS AND FOLLOW-UP

The endpoints for this study were all-cause mortalityand a combined endpoint of fatal and nonfatal MI.Information about deaths was obtained from the Dan-ish Central Civil Register, which records the vital status

of all inhabitants in Denmark. Information about re-admissions with MI [International Classification ofDisease (ICD) codes I21.0-I21.9] and heart failure(HF) (ICD I50-I50.9) was obtained from dischargecodes from the Danish National Hospital Register, adatabase of all nonpsychiatric hospitalizations. Pa-tients were followed from discharge until July 28, 2011.

DEFINITION OF INDEX DIAGNOSIS AND ECG ANALYSES

The discharge diagnoses were grouped according to(a) high-risk NSTEACS, (b) low-risk NSTEACS(suspected high risk NSTEACSthat did notfulfill thediagnostic criteria), (c) other cardiac diseases [e.g.,arrhythmias, congestive heart failure (CHF)], and(d) noncardiac diseases (e.g., infectious diseases,stroke, uremia). The ECGs at admission were analyzedby one investigator (K.K. Iversen), who was blinded to

follow-up data, and were divided into normal ECGsand abnormal ECGs (signs of acute or previous ische-mic heart disease, i.e., Q-waves, ST-depression, T-waveinversion, or bundle-branch block). A recent study hassuggested the validity of this distinction of ECGchanges for prediction of adverse coronary events (12).

LABORATORY ANALYSES

Blood samples were taken at baseline and typically re-peated every 68 h until the concentrations of bio-markers reflecting myocardial necrosis [TnT and/orcreatinine kinase MB (CK-MB)] either consistently de-creased (typically after 3 sequential blood samples) or

remained within the reference interval (typically after 2sequential blood samples). The following assays wereused locally during admission for ruling out MI: thirdgeneration Elecsys troponin T immunoassay (productnumber 12017644122 with a lower limit ofdetectionof10ng/L and a CV of 10% at a TnT concentration of 30 ng/L)andCK-MB (both Roche Diagnostics).Blood samples forsubsequent analyses were centrifuged for 10 min at 3 000rpm, and plasma was stored at 20 C until analysis.Plasma concentrations of high-sensitivity C-reactiveprotein (CRP) and suPAR were measured fromthawed biobanked samples using commercially avail-able kits according to the manufacturers instructions

[CRPus kit (detection limit, 0.06 mg/L), (BRAHMSAG) and suPARnostic kit (validated to measure su-PAR concentrations between 0.6 and 22 ng/mL) (Viro-Gates)]. Both CRP (13)and suPAR(14)have previ-ously been shown to be stable in frozen samples.

STATISTICAL ANALYSES

Tests for differences between groups were done byANOVA and the 2 test for continuous and discretevariables, respectively. Differences of plasma suPARconcentrations on admission between individuals withand without adverse events were compared with the

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KruskalWallis nonparametric test. The relation-ships between suPAR and baseline characteristicswere explored using Pearson correlation analysis afterlogarithmic transformation of the following variables:suPAR, CRP, TnT, creatinine, and glucose concentra-tions. KaplanMeier survival plots were constructedfrom baseline suPAR concentrations divided into ter-tiles [as done previously (15)]. Adjusted survivalcurves (adjusted for age and sex) were based on a Coxproportional hazards model (16). The log-rank testwas used to compare differences in survival across thegroups. Cox proportional hazards models were alsoused for the analysis of the relationship between suPARmeasurements after admission and long-term out-come. The assumption of proportionality in the Coxregression models was assessed with the score processtest (17)and Schoenfeld tests and found to be valid.

Interactions between the variables were tested by add-ing interaction terms in the models and evaluating bythe Wald test. Unless specified, no interactions wereobserved. For the combined endpoint, participantswere censored at the time of the first event.

The predictive performances of the Cox modelswere summarized by means of improvements in csta-tistics (18). Model fit was tested by use of the Grnnes-byBorgan statistic(19 ). Net reclassification improve-ment (NRI) was assessed based on category-free NRI(20). The expected numbers of events and noneventswere used in the estimation of NRI to account for cen-sored data and calculated by multiplying the total

number of people by the KaplanMeier rates at the endof follow-up. This approach was found optimal forassessing calibration of survival models(21 ). We esti-mated the bias-corrected CIs for NRI by bootstrap re-sampling (1000 replicates). Finally, we estimated theintegrated discrimination improvement (IDI) (22),which is the difference in mean probability of im-proved reclassification for events minus nonevents.

All statistical tests were 2-sided and significance wasbased onPvalues of0.05. All analyses were performedwith SAS, version9.1.3 (SAS Institute), or R version2.15.0(R Foundation for Statistical Computing).

Results

BASELINE CHARACTERISTICS AND CORRELATIONS

Baseline characteristics for the 449 patients overall anddivided into tertiles of suPAR concentrations areshown in Table 1. Patients presenting with high suPARconcentrations were characterized by high-risk fea-tures such as increasing age, a higher prevalence ofcardiovascular risk factors exemplified by increasedconcentrations of LDL cholesterol (LDL-C), blood glu-cose, creatinine, TnT; higher rate of smoking; and pre-vious CVD. The relations of suPAR to these risk factors

analyzed by univariate, and age- and sex-adjusted Pear-son correlation analyses are presented in Table A in theData Supplement that accompanies the online versionof this article at http://www.clinchem.org/content/vol59/issue11. By multiple linear regression analysis withlog(suPAR) as the dependent variable, the explanatoryvariables age, sex,diabetes, hypertension, smoking status,previous MI, and CHF and baseline concentrations ofcreatinine, LDL-C, HDL-C, CRP,and TnT accounted forless than half of the variability in suPAR concentrations(R2 0.48).

DIAGNOSIS ON ADMISSION AND DURING FOLLOW-UP

The final diagnosis at discharge was high-riskNSTEACS in 77 (17.2%) and low-risk NSTEACS in257 (57.2%) of patients,whereas 58 (12.9%) of patientshad another cardiovascular diagnosis such as arrhyth-

mia, CHF, or significant valve disease. In total, 57(12.7%) had a noncardiac-related diagnosis (e.g.,pneumonia, gastrointestinal causes, or musculoskele-tal disorders). The distribution of suPAR tertiles ac-cording to the index diagnosis is shown in Fig. 1.

During a median follow-up of 5.7 years (range0.016.6 years), there were 162 (36.1%) deaths, 46(10.2%) readmissions due to HF, and 89 (19.8%) casesof fatal or nonfatal MI. Of patients who died duringfollow-up, 96 deaths were related to cardiovascular dis-eases (ICD I00-I99), which included 40 fatal MIs (ICDI21-I22) and 4 deaths due to HF (ICD I50-I50.9) as theprimary diagnoses. Nineteen deaths were registered as

cancer (ACD10 C00-D48) and 17 fatalities were due toinfection or pulmonary diseases (ICD A40-A40.5, J10-J18.9, and J40-J46.9) as the primary diagnosis.

BLOOD SAMPLING

The median (interquartile range) time from onset ofsymptoms to admission was 6.5 h (2.518.9 h), and thetime from symptom onset to first blood sampling was7.7 h (3.720.8 h), whereas the time from admission tobaseline blood sample was 0.8 h (0.22.0 h). The num-ber of consecutive blood samples taken during admis-sion varied as follows: 1 blood sample (8.2% of entirecohort), 2 samples (28.5%), 3 samples (62.4%), 4

samples (0.9%).

suPAR AND ALL-CAUSE MORTALITY

Patients who died in the follow-up period had signifi-cantly higher suPAR concentrations at baseline thansurviving patients (median 5.99 vs 4.11 mg/L; P fordifference0.0001) (see online Supplemental Fig. A).As illustrated in Fig. 2A, all-cause mortality increasedsignificantly during follow-up from 17.1% to 51.8%across increasing tertiles of age- and sex-adjusted su-PAR concentrations, P 0.0001. In a univariatemodel, suPAR concentrations were significantly asso-

suPAR and Outcomes in Patients with Chest Pain

Clinical Chemistry59:11 (2013) 3
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ciated with all-cause mortality [hazard ratio (HR) 2.73(95% CI, 2.323.22) per SD increment in log(suPAR);P 0.0001]. In a model adjusted for age and sex,suPAR remained strongly associated with all-cause mor-

tality [HR 2.21 (1.842.65);P 0.0001], and furtheradjustments for the final index diagnosis, diabetes, hy-pertension, smoking, CHF, previous MI, and admis-sion concentrations of log-transformed plasma creati-nine, CRP, and TnT in the fully adjusted model did notconsiderably attenuate this relationship [HR 1.93(1.482.51);P 0.0001)] (Table 2).

The predictive capability of age- and sex-adjustedsuPARconcentrations for all-cause mortalitywas iden-tical whether the first blood sample [HR per SD incre-ment in log(suPAR) 2.21 (1.84 2.65)] or peak concen-tration of suPAR [HR 2.14 (1.802.55)] was used.

Finally, when suPAR was included in the same modelas CRP, adjusted for age and sex, CRP was only weaklyrelated to outcomes [HR 1.21 (1.011.45) per SD in-crement in log(CRP); P 0.042 vs HR 2.08 (1.69

2.56) per SD increment in suPAR;P 0.0001], and inthe fully adjusted model,CRP did not predict mortality(P 0.16).

suPAR, HF, AND FATAL OR NONFATAL MI

Patients readmitted for HF or with fatal or nonfatal MIduring follow-up also had significantly higher suPARconcentrations at baseline than event-free individuals(median concentrations for HF, 5.99 vs 4.46 mg/L, andfor fatal or nonfatal MI, 5.80 vs 4.44 mg/L; both Pfordifference0.0001) (see online Supplemental Fig. A).There was a significant increase in event rates during

Table 1. Baseline characteristics for the entire cohort and divided in tertiles of the concentration of the first

available suPAR sample.a

Variable

Total

suPAR (127.3

ng/mL)

First suPAR tertile

(1.04.0 ng/mL)

Second suPAR tertile

(4.05.5 ng/mL)

Third suPAR tertile

(5.527.3 ng/mL)

Pn Value n Value n Value n Value

Demographics

Age, years 449 67 (5778) 149 58 (4867) 150 71 (6178) 150 76 (6583) 0.0001

Male sex, % 449 56.8 149 67.1 150 52.0 150 51.3 0.008

Hemodynamic parameters

Systolic BP, mmHg 443 150 (130172) 146 150 (132170) 150 149 (131172) 147 150 (126178) 0.79

Diastolic BP, mmHg 442 83 (7293) 146 84 (7598) 150 82 (7091) 145 81 (7092) 0.008

LVEF, % 168 50 (3560) 44 55 (4360) 58 60 (4060) 66 40 (3050) 0.0005

Cardiovascular risk factors

Diabetes, % 436 17.7 146 10.3 144 13.9 146 28.8 0.0001

Hypertension, % 413 54.2 143 47.6 142 56.3 128 59.4 0.12

Current smoking, % 433 38.8 145 35.9 144 41.0 144 39.6 0.51

Prior MI, % 443 28.9 147 18.4 149 28.2 147 40.1 0.0002

Prior CHF, % 435 25.3 146 15.1 146 24.0 143 37.1 0.0001

Laboratory values

First suPAR value, ng/mL 449 4.6 (3.596.06) 149 3.18 (2.683.59) 150 4.54 (4.224.97) 150 7.02 (6.068.40) 0.0001

Peak suPAR value, ng/mL 449 4.8 (3.816.34) 149 3.39 (2.913.79) 150 4.73 (4.335.21) 150 7.21 (6.259.02) 0.0001

CRP, mg/L 430 6.17 (1.9420.6) 143 2.49 (1.016.49) 145 6.05 (2.1714.4) 142 18.8 (6.0951.6) 0.0001

Total cholesterol, mmol/L 406 4.8 (3.95.7) 144 4.9 (4.25.9) 134 5.0 (4.05.6) 128 4.7 (3.55.6) 0.03

LDL-C, mmol/L 403 2.8 (2.03.6) 144 3.0 (2.43.8) 133 2.8 (2.03.6) 126 2.6 (1.73.4) 0.002

HDL-C, mmol/L 404 1.3 (1.01.7) 144 1.3 (1.11.7) 133 1.4 (1.11.8) 127 1.3 (0.91.7) 0.27

Triglycerides, mmol/L 403 1.3 (0.91.9) 142 1.3 (0.92.1) 133 1.4 (1.01.9) 128 1.3 (0.91.8) 0.83Creatinine, mol/L 443 77 (6494) 148 72 (6183) 149 76 (6487) 146 91 (70125) 0.0001

Glucose 424 5.8 (5.27.1) 146 5.5 (5.16.2) 138 5.9 (5.26.8) 140 6.5 (5.48.4) 0.0001

Peak TnT value, ng/L 446 10 (1080) 149 10 (1010) 149 10 (1050) 148 40 (10250) 0.0007

a Data are presented as median (interquartile range) for continues variables and proportions in percent for categorical variables. The Pvalues were calculated on

the basis of linear trends for all continuous variables and nonparametric testing for categorical variables.


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risk individuals, as assessed by the ECG and peak TnTconcentrations during admission. Thus,cstatistics forall-cause mortality increased from 0.751 (95% CI,0.713 0.789) to 0.805 (0.772 0.837) (Pfor difference

0.0001) with the addition of plasma suPAR concen-trations to a model consisting of age, sex, and presenceof abnormal ECGand increased(100 ng/L) peak TnTconcentrations.

Table 2. Association between suPAR and outcome.a

Outcome

Univariate model Multivariate model 1 Multivariate model 2

HR (95% CI) P

HR (95% CI) P

HR (95% CI) P

All-cause mortality 2.73 (2.323.22) 0.0001 2.21 (1.842.65) 0.0001 1.93 (1.482.51) 0.0001

Readmission for HF 2.69 (1.983.67) 0.0001 2.33 (1.673.27) 0.0001 2.00 (1.263.18) 0.004

Fatal or nonfatal MI 1.98 (1.592.46) 0.0001 1.81 (1.432.29) 0.0001 1.14 (0.831.56) 0.44

a Standardized HRs for log(suPAR) for all-cause mortality and fatal or nonfatal MI in 3 (1 univariate and 2 multivariate) Cox proportional hazard models. Multivariate

model 1, adjusted for age and sex; multivariate model 2, adjusted for model 1 final index diagnosis, diabetes, hypertension, smoking, previous MI, CHF, and

log transformed plasma creatinine, CRP, and peak concentrations of TnT.

Fig. 3. suPAR and outcome in patients according to ECG and TnT findings.

Outcome according to the presence of ECG abnormalities (A) or peak TnT concentrations (B) with first available suPARconcentrations below or above the median. The number of events/total number of individuals is shown in each bar.


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EFFECTS OF suPAR ON MODEL DISCRIMINATION AND

RECLASSIFICATION

The addition of suPAR to a model including age and

sex improved the cstatistics for the outcome of all-cause mortality from 0.744 (95% CI, 0.706 0.783) to0.799 (0.7650.832) (P 0.0001). The same was ob-served for continuous NRI, which improved by 0.628(P 0.0001) and IDI by 0.100 (P 0.0001), respec-tively (see online Supplemental Table B). When suPARwas added to the fully expanded model with traditionalcardiovascular risk factors, the c statistics improvedsignificantly, from 0.814 (0.776 0.852) to 0.830(0.795 0.865) (Pfor difference 0.047). Addition ofsuPAR to the fully expanded model also led to signifi-cant improvements for NRI, by 0.287 (P 0.029), andIDI, by 0.026 (P 0.027). The corresponding numbersfor patients readmitted with HF and with the com-bined outcome of fatal or nonfatal MI is shown in on-line Supplemental Table B.

PREDICTIVE CAPABILITIES OF suPAR WITH INDEX DIAGNOSIS

AND TIME

We found no interaction between the adjudicated di-agnoses at discharge and the predictive capability ofsuPAR during follow-up for the outcome of all-causemortality. To determine trends in the predictive capa-bility of suPAR at different time points during follow-up, we calculated the HR for the outcome after 6

months and 1, 3, and 5 years adjusted for age and sex. Itis evident from Table 3 that for all 3 outcomes the pre-dictive capabilities of suPAR did not vary substantiallyover time, although there wasa trend toward a strongerpredictive capability of suPAR for the outcome of all-cause mortality early in the follow-up period. With re-spect to the combined outcome of fatal and nonfatalMI, suPAR had significant predictive capabilities onlyin the model adjusted for age and sex (Table 3). WhenTnT was omitted from the fully expanded model,suPAR was a significant predictor of fatal and nonfatalMI after 1 year (data not shown).

Discussion

This is the first study to demonstrate that suPAR con-

centrations measured in the first blood sample uponadmission in patients with chest pain and suspectedNSTEACS is a strong and independent mid- and long-term predictor of all-cause mortality and to a lesserextent MI. Importantly, this effect was independent ofthe final discharge diagnosis. Even after adjustmentsfor age and sex, patients with a suPAR concentration inthe lowesttertile had a relatively good prognosis, with amortality of 2.6% after 6 months and 17.5% after 6

years of follow-up, whereas the corresponding num-bers for patients in the highest suPAR tertile were con-siderably less favorable, with mortality rates of 11.2%and 42.7%, respectively. suPAR was also a strong pre-

dictor of future readmissions due to HF and to a lesserdegree also predicted future MI, although this effectdisappeared when TnT and final index diagnosis wereincluded in the fully expanded model.

The predictive capability of suPAR for all-causemortality, HF, and MI was preserved whether firstbloodsample concentrations or peak concentrations ofsuPAR were used. Thus, the exact timing of blood sam-pling for clinical use of suPAR for risk prediction inthese acute chest pain patients does not seem to beimportant. Therefore, these biomarker characteristicscould make suPAR a strong and suitable candidate inmanagement decisions regarding acute chest pain pa-

tients with suspected NSTEACS.The current practice for the evaluation of acute

chest pain patients with suspected NSTEACS is basedon serial ECG analyses and troponin measurements.Although the prognosis in NSTEACS patients is clearlydependent on ECG abnormalities and increased plasmatroponin concentrations (23), many acute chest pain pa-tients haveunderlyingpathologies thatdo notinvolvesig-nificant coronary artery disease. Under such circum-stances an unremarkable clinical assessment and testresults could often result in swift discharge of patients,with potentially serious adverse outcomes. Thus, addi-

Table 3. Association between suPAR and outcome during follow-up.a

Model Outcome

6 months 1 year 3 years 5 years

HR (95% CI) P HR (95% CI) P HR (95% CI) P HR (95% CI) P

Multivariatemodel 1

All-cause mortality 2.88 (2.024.11) 0.0001 2.98 (2.263.92) 0.0001 2.55 (2.053.17) 0.0001 2.23 (1.852.69) 0.0001

HF readmission 1.87 (1.362.57) 0.0001 2.14 (1.542.99) 0.0001 2.27 (1.633.16) 0.0001 2.35 (1.683.29) 0.0001

Fatal or nonfatal MI 2.05 (1.462.88) 0.0001 2.00 (1.482.72) 0.0001 1.96 (1.522.54) 0.0001 1.84 (1.452.34) 0.0001

a Standardized HRs for log(suPAR) for all-cause mortality, HF, and fatal or nonfatal MI in a Cox proportional hazard model adjusted for age and sex (multivariate

model 1) from 6 months until 5 years of follow-up.



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tional variables are needed for optimal treatment of acutechest pain patients with suspected NSTEACS, especiallywhen MI has been ruled out. In our cohort only about25% ultimately had high-risk NSTEACS, which mayhave suggested that the remaining population had alow risk. Measurements of suPAR and subsequent cor-relation with long-term mortality on admission never-theless showedthat this wasnot thecase.Applicationofa strategy in which suPAR concentrations were consid-ered together with ECG abnormalities and increasedconcentrations of TnT resulted in improved risk strat-ification for all-cause mortality and MI both in low-risk patients (with or without NSTEACS) with normalECG findings and TnT concentrations within referenceintervals, and in high-risk individuals with abnormalECGs and TnT concentrations above reference inter-vals, respectively. This suggests that suPAR may con-

tribute to clinical decision-making, particularly at thetime of discharge of apparently low-risk acute chestpain patients,in whom lowsuPARvalues mayadd con-siderable weight to expected low probabilities long-term morbidity and mortality. The clinical implica-tions of increased suPAR concentrations (apart fromworsened prognosis), however, remain unknown; at aminimum this finding may warrant a thorough follow-up, with additional clinical and paraclinical workups inpursuit of significant pathology together with modifi-cations of risk factors such as smoking, physical inac-tivity, and obesity. Prospective studies are warranted tostudy the results of a suPAR-dependent clinical strat-

egy in patients with acute chest pain and suspectedNSTEACS. Such studies would be important to estab-lish appropriate cutoff values for suPAR and to exam-ine the effects of diagnostic and therapeutic interven-tions in such high-risk individuals.

Patients with high concentrations of suPAR on ad-mission were characterized by high-risk features suchas higher prevalence of CHF, prior MI, diabetes, andhigher plasma concentrations of creatinine and CRP.Notwithstanding these factors, a multiple-variable lin-ear model including age, sex, diabetes, hypertension,smoking, previous MI, CHF, and baseline concentra-tions of creatinine, LDL-C, HDL-C, CRP, and TnT ex-

plained less than half of the variation of suPAR concen-trations. This result suggests that additional and as yetnot defined mechanisms contribute to plasma suPARconcentrations. As opposed to CRP, suPAR was ahighly significant predictor of adverse outcomes,which indicates that these 2 biomarkers probably re-flect different aspects of the inflammatory pathologythat may be manifested during follow-up of acutechest pain patients (24). The origin of suPAR inplasma is unknown, but uPAR can be found in ath-erosclerotic plaques(25 )and is released from endo-thelial and inflammatory cells upon activation (26

28 ). In the future, it will be important to determinethe underlying mechanisms for release of suPAR tothe circulation and any potential pathophysiologicalrole of suPAR.

Potential study limitations merit consideration.This study wasbased on blood samples obtained from arather homogeneous white population in a single cen-ter with a limited samplesize,factors which might limitthe applicability of the results to other ethnic popula-tions. Patients were admitted at the discretion of theon-call physician when NSTEACS was suspected, i.e., aheterogeneous population which, however, is likely toreflect typical acute chest pain patients in the emer-gency department. Moreover, the clinical parameterson which this study relied were established on the basisof information gathered from the hospital charts,which, like the timing of blood sampling, was not stan-

dardized. Variablesthat were available only in a minor-ity of patients, e.g., left ventricular ejection fraction(LVEF), were not included in the models and an im-portant biomarker such as NT-proBNP (N-terminalpro-B-type natriuretic peptide) was not measured inthis cohort. Nevertheless, suPAR still predicted all-cause mortality in the 168 patients with measurementsof LVEF in the fully adjusted model [HR 1.96 (95% CI,1.223.14);P 0.006]. Also, we had no informationabout heart rate or blood pressure at admission pre-cluding theuse of GRACE or TIMI risk scores. Further-more, the follow-up of the study patients relied on theaccuracy of the diagnoses listed in the Danish Central

Civil Register, although the diagnosis of MI in this set-ting has been validated(29 ). The use of national regis-ters for follow-up did not allow for detailed scrutiny ofthe causes of death. Finally, the blood samples usedwere part of a biobank endeavor. Although suPAR hasbeen found to be stable in frozen blood samples (14 ),the samples were stored for several years before analy-sis, potentially leading to biomarker degradation, butthis would be expected to attenuate the prognostic ca-pability of the biomarker.

In conclusion, increased circulating concentra-tions of soluble urokinase plasminogen activator re-ceptor in patients with acute chest pain and sus-pected NSTEACS are associated with poor prognosisand provide independent prognostic informationbeyond established cardiovascular risk factors.These findings require independent verification in amulticenter prospective cohort.

Author Contributions:All authors confirmed they have contributed to

the intellectual content of this paper and have met the following 3 re-

quirements: (a) significant contributions to the conception and design,acquisition of data, or analysis and interpretation of data; (b) drafting


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or revising the article for intellectual content; and (c) final approval ofthe published article.

Authors Disclosures or Potential Conflicts of Interest:Upon man-uscript submission, all authors completed the author disclosure form.Disclosures and/or potential conflicts of interest:

Employment or Leadership:J. Eugen-Olsen, ViroGates, Denmark,and Copenhagen University Hospital Hvidovre.Consultant or Advisory Role:P.M.Clemmensen, Abbot, AstraZeneca,

Bayer, BoehringerIngelheim, Bristol MyersSquibb,DaiichiSankyo, Eli-Lilly, Evolva, Fibrex, Merck, Myogen, Medtronic, Mitsubishi Pharma,The Medicines Company, Nycomed, Organon, Pfizer, Pharmacia,Sanofi, Searle, and Servier; J. Eugen-Olsen, ViroGates, Denmark, and

Copenhagen University Hospital Hvidovre.

Stock Ownership: J. Eugen-Olsen, ViroGates, Denmark, and Co-

penhagen University Hospital Hvidovre.

Honoraria: None declared.

Research Funding:J.L. Jeppesen, Novo Nordisk Foundation.

Expert Testimony: None declared.

Patents: J. Eugen-Olsen, inventor on patents held by ViroGates,Denmark, and Copenhagen University Hospital Hvidovre on the

use of suPARin prognostics (US12/520,718, PCT EP2007/064497,

PA200601709 (WO2008/077958), PCT/DK 02/00341, and PCT/

DK00/00651).

Role of Sponsor:The funding organizations played no role in the

designof study, choiceof enrolled patients, reviewand interpretation

of data, or preparation or approval of manuscript.

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