High-sensitivity Troponin T Predicts Recurrent Cardiovascular Events in Patients with Stable Coronary Heart Disease: KAROLA Study 8 Year FU

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ESC Congress 2011 Paris, France, August 27-31 KAROLA Session: Prevention: Are biomarkers worth their money? Abstract # 84698 High-sensitivity Troponin T Predicts Recurrent Cardiovascular Events in Patients with Stable Coronary Heart Disease: KAROLA Study 8 Year FU W. Koenig 1, L. Breitling 2, H. Brenner 2, D. Rothenbacher 2,3 1 Department of Internal Medicine II - Cardiology, University of Ulm Medical Centre, Ulm, Germany 2 Division of Clinical Epidemiology and Aging Research, German Cancer Research Centre, Heidelberg, Germany 3 Institute of Epidemiology and Medical Biometry, Ulm University, Ulm, Germany

Presenter Disclosure Information High-sensitivity Troponin T Predicts Recurrent Cardiovascular Events in Patients with Stable Coronary Heart Disease: KAROLA Study 8 Year FU The following relationships exist related to this presentation: W. Koenig Honoraria for lectures/consultancy to Roche L. Breitling nothing to disclose H. Brenner nothing to disclose D. Rothenbacher nothing to disclose

Background Troponins (Tn) are established biomarkers for cardiomyocyte necrosis in acute coronary syndromes (ACS) - even slight elevations are associated with increased adverse events. High-sensitivity (hs) assays have been developed that enable earlier diagnosis with increased sensitivity and only slight decreases in specificity. A cut-off for hstnt has been defined as 14 ng/l which represents the 99 th percentile in a healthy population. So far two studies (Omland et al NEJM 2009, Kavsak et al Clin Chem 2011) have investigated the clinical relevance of slightly elevated TnT levels in selected patients with stable CHD from the PEACE and HOPE trials.

Aims of the Study To assess determinants of hstnt in a large cohort of wellcharacterized patients from routine clinical care in Germany (KAROLA cohort). To evaluate the predictive value of elevated levels of TnT for cardiovascular outcomes after adjustment for potential confounders, including potent emerging risk markers, like cystatin C and NT-proBNP.

KAROLA HsTnT and Risk of Future CV Events: The KAROLA Cohort Study 1999-2008 1,050 men aged 30-70 years with manifest CHD/ 3 weeks after ACS (inpatient rehabilitation program) Standardized assessment of cardiovascular risk factors Hs TnT and NT-proBNP (Elecsys 170, Roche, Mannheim, Germany, inter-assay CVs 1.9-3.1% and 3.1-7.2%. Lp-PLA 2 (diadexus PLAC ); spla2 (ELISA, Cayman Co, USA). CRP (latex enhanced hs immunonephelometry), cystatin C (immunonephelometry, Siemens, Marburg, Germany, inter-assay CVs 6-7%, 16.5%, 4.7-5.2%, and 2.6-2.8%, respectively). Qx sent to patients 1, 3, 4 and 8 years after rehab. Endpoint assessment by Qx to GPs (cardiovascular death, non-fatal MI and stroke/tia)

KAROLA Statistical Methods The relation of hstnt with CVD events during FU assessed by the Kaplan-Meier and life table method, quantified by means of the log-rank test. The Cox proportional hazards model to assess the independent association of hstnt with risk of secondary CVD events. Measures of model accuracy: Model fit: Likelihood ratios, AIC, BIC Discrimination: C statistics (AUC, 95%) Reclassification: IDI and NRI Calibration: Observed versus expected

KAROLA Baseline Characteristics in 1,050 Patients With Clinically Manifest CHD (1) Characteristics at Baseline Age (years) (, SD) 58.9 8.0 Men, n (%) 893 (85.1%) History of myocardial infarction, n (%) 611 (58.2%) History of heart failure, n (%) 129 (12.3%) Clinical score (angiographic evaluation) 1 vessel disease 256 (25.6%) 2 vessel disease 281 (28.1%) 3 vessel disease 448 (44.8%) unknown 50 (4.8%) School education <10 yr, n (%) 627 (59.7%)

KAROLA Baseline Characteristics in 1,050 Patients With Clinically Manifest CHD (2) Characteristics at Baseline Body mass index (kg/m 2 ), (, SD) 27.1 3.5 History of diabetes, n (%) 179 (17.1%) Total cholesterol (mg/dl) (, SD) 169.0 (32.8) LDL-cholesterol (mg/dl) (, SD) 100.5 (29.1) HDL-cholesterol (mg/dl) (, SD) 39.4 (10.5) C-reactive protein (mg/l) * 3.48 (1.25; 8.40) Creatinine Clearance (ml/min) * 93.2 (78.2; 114.7) Cystatin C (mg/l)* 1.03 (0.93; 1.19) NT-proBNP (pg/ml)* 568.9 (277.9; 1101.0) * median, 25 th and 75 th quantile cut-point

HsTnT According to Various Sociodemographic Characteristics, CV Risk Factors, and ECG Findings (1) Sociodemographic characteristics N hstnt, ng/l Median All 1050 10.90 IQR (5.09-18.90) Above detection limit Above 99 th percentile Gender Female 157 8.43 Age (years) 30-39 23 3.26 * Kruskall-Wallis-Test > 3 ng/l = 83.1% >14 ng/l = 37.1% p-value* Male 893 11.10 0.002 40-49 130 6.29 50-59 304 8.07 60-70 593 13.40 <0.0001

HsTnT According to Various Sociodemographic Characteristics, CV Risk Factors, and ECG Findings (2) Sociodemographic Characteristics N hstnt, ng/l Median Family status Married 874 10.90 p-value* other 176 10.20 0.55 Body mass index (kg/m 2 ) < 25 298 11.50 25-30 564 10.20 > 30 187 11.00 0.04 Smoking status Never 333 11.10 Ex 666 10.65 Current 51 10.30 0.48 History of diabetes Yes 179 13.00 No 871 10.00 < 0.0001 History of MI Yes 611 10.70 * Kruskall-Wallis-Test No 439 11.00 0.80

HsTnT According to Various Sociodemographic Characteristics, CV Risk Factors, and ECG Findings (3) * Kruskall-Wallis-Test Clin. Characteristics N hstnt, ng/l, Median p-value* History of hypertension Yes 584 11.75 No 466 9.39 0.0006 History of heart failure Yes 129 12.00 No 888 10.65 0.06 Angiographic score Zero/one 271 6.82 (number of affected vessels) Two 281 10.60 Three 448 13.00 < 0.0001 Initial management of CHD Conservative 190 9.22 PCI 360 7.67 CABG 500 13.10 < 0.0001 Left ventricular function* No/only little 748 9.39 (degree of impairment) - Modest/severe 215 17.40 - Unknown 87 10.70 < 0.0001

HsTnT According to Various Sociodemographic Characteristics, CV Risk Factors, and ECG Findings (4) ECG Characteristics N hstnt, ng/l, Median p-value* Sinus tachycardia Yes 48 15.15 No 1002 10.60 0.049 Atrial flutter / fibrillation Yes 29 18.70 No 1021 10.80 0.01 Left ventricular hypertrophy Yes 29 13.60 No 1002 10.75 0.21 Anterior wall infarction Yes 210 13.95 No 840 10.00 < 0.0001 Posterior wall infarction Yes 224 11.30 No 826 10.75 0.04 * Kruskall-Wallis-Test

Partial Spearman Rank Correlation Coefficients (R) (1) hstnt, ng/l R p-value Total cholesterol [mmol/l] 0.08 0.01 HDL cholesterol [mmol/l] -0.03 0.30 LDL cholesterol [mmol/l] 0.09 0.003 Leukocytes 0.10 0.0009 C-reactive protein [mg/l] 0.19 < 0.0001 Interleukin-6 [pg/ml] 0.15 < 0.0001 Adiponectin [µg/ml] 0.17 < 0.0001

Partial Spearman Rank Correlation Coefficients (R) (2) hstnt, ng/l R p-value Creatinine clearance [ml/min] -0.19 < 0.0001 Cystatin C [mg/l] 0.32 < 0.0001 NT-proBNP [pg/ml] 0.61 < 0.0001 Lp-PLA 2 mass [ng/ml] 0.18 < 0.0001 Lp-PLA 2 activity [nmol/min/ml] 0.11 0.0004 spla 2 mass [ng/ml] 0.16 < 0.0001 spla 2 activity [nmol/ml/min] 0.13 < 0.0001

Proportion of Event-free Survivors KAROLA 1.0 Kaplan-Meier Estimates of Secondary Fatal and Non-Fatal CVD Events Q1 Q2 Q3 Q4 0.9 0.8 0.7 0.5 0 500 1000 1500 2000 2500 3000 3500 HsTnT (ng/l) N Secondary CVD Event During FU; N (Incidence*) p-value** < 5.03 (bottom quartile 263 21 (11.4) >5.3 10.8 268 28 (15.1) >10.8 18.06 257 37 (21.3) >18.6 (top quartile) 262 64 (37.5) < 0.0001 1050 150 (21.0) * per 1000 patient years, ** Log Rank Test FU [days]

Association of hstnt Concentration at Baseline With Fatal and Non-Fatal CV Events During Follow-Up HsTnT [ng/l] HR (95% CI) Adjusted for age and gender Results of Multivariate Analysis HR (95% CI) *Adjusted for multiple covariates Bottom Quartile 1 referent 1 referent 1 referent HR (95% CI) Adjusted for multiple covariates Second 1.21 (0.68 2.16) 1.25 (0.70 2.22) 1.26 (0.71 2.24) Third 1.60 (0.91 2.80) 1.63 (0.92 2.86) 1.54 (0.87 2.72) Top Quartile 2.88 (1.72 4.82) 2.83 (1.68 4.79) 2.27 (1.31 3.95) p for trend < 0.0001 p for trend < 0.0001 p for trend < 0.0001 Per unit increase** 1.48 (1.25 1.74) 1.45 (1.23 1.72) 1.30 (1.08 1.57) * adjusted for age, gender, smoking status, history of diabetes mellitus, initial management of CHD (conservative, PCI, CABG), rehabilitation clinic, HDL-cholesterol, LDL-cholesterol, treatment with lipid-lowering drugs (according to variable selection criteria) adjusted for above listed variables (*) and NT-proBNP, CRP and Cystatin C ** after log-transformation

KAROLA Measures of Model Accuracy Without and With hstnt (1) Basic Model* Basic Model plus hstnt Model fit LR 53.84 (df = 11, p < 0.0001) 72.83 (df = 12, p < 0.0001) AIC 1984.9 1967.8 BIC 2018.0 2003.9 Discrimination C-statistic (AUC, 95% CI) 0.6707 (0.6271-0.7143) 0.6898 (0.6468-0.7327) Reclassification IDI NRI 0.009 (p<0.001) 17.2% (p<0.029) IDI=Integrated Discrimination Improvement, NRI=Net reclassification index LR=Likehood ratio; AIC=Akaike`s Information Criterion; BIC=Bayesian Information Criterion

KAROLA Measures of Model Accuracy Without and With hstnt (2) Basic Model* Calibration**: Observed vs. Expected events (p) Basic Model plus hstnt Bottom quintile 15 / 12.1 (0.400) 12 /10.3 (0.593) 2 nd quintile 14 / 20.1 (0.176) 15 / 17.2 (0.601) 3 rd quintile 30 / 25.5 (0.375) 24 / 24.9 (0.864) 4 th quintile 34 / 34.7 (0.906) 38 / 33.6 (0.449) Top quintile 57 / 57.6 (0.933) 61 / 64.1 (0.701) *adjusted for age, gender, smoking status, history of diabetes mellitus, initial management of CHD (conservative, PCI, CABG), rehabilitation clinic, HDL-cholesterol, LDL-cholesterol, treatment with lipid-lowering drugs (according to variable selection criteria) ** May-Hosmer`s simplification of the Gronnesby-Borgan test: patients were divided into quintiles according to their rank in an estimated risk score (p> 0.05 for comparison of observed with expected cases indicate good model calibration.

KAROLA Summary and Conclusions In this routine setting of patients with stable manifest CHD, mostly after an ACS, hstnt is a strong predictor of long-term prognosis even in extensively adjusted models. However, before hstnt is routinely measured in these patients, more data are needed to further prove its clinical utility. Prediction might be further improved by the combination of various biomarkers reflecting different pathophysiological pathways.

Department of Cardiology University of Ulm Medical Center The New York Times, Sunday May 8, 2005 University of Ulm Medical Center