Biomarker-Based Risk Model to Predict Cardiovascular Mortality in Patients With Stable Coronary Disease.

Daniel Lindholm, Johan Lindbäck, Paul W Armstrong, Andrzej Budaj, Christopher P Cannon, Christopher B Granger, Emil Hagström, Claes Held, Wolfgang Koenig, Ollie Östlund, Ralph A H Stewart, Joseph Soffer, Harvey D White, Robbert J de Winter, Philippe Gabriel Steg, Agneta Siegbahn, Marcus E Kleber, Alexander Dressel, Tanja B Grammer, Winfried März, Lars Wallentin

Journal: Journal of the American College of Cardiology 2017;70(7):813-826

PMID: 28797349

Free Full Text on Elsevier Science View on PubMed

Abstract

BACKGROUND

Currently, there is no generally accepted model to predict outcomes in stable coronary heart disease (CHD).

OBJECTIVES

This study evaluated and compared the prognostic value of biomarkers and clinical variables to develop a biomarker-based prediction model in patients with stable CHD.

METHODS

In a prospective, randomized trial cohort of 13,164 patients with stable CHD, we analyzed several candidate biomarkers and clinical variables and used multivariable Cox regression to develop a clinical prediction model based on the most important markers. The primary outcome was cardiovascular (CV) death, but model performance was also explored for other key outcomes. It was internally bootstrap validated, and externally validated in 1,547 patients in another study.

RESULTS

During a median follow-up of 3.7 years, there were 591 cases of CV death. The 3 most important biomarkers were N-terminal pro-B-type natriuretic peptide (NT-proBNP), high-sensitivity cardiac troponin T (hs-cTnT), and low-density lipoprotein cholesterol, where NT-proBNP and hs-cTnT had greater prognostic value than any other biomarker or clinical variable. The final prediction model included age (A), biomarkers (B) (NT-proBNP, hs-cTnT, and low-density lipoprotein cholesterol), and clinical variables (C) (smoking, diabetes mellitus, and peripheral arterial disease). This "ABC-CHD" model had high discriminatory ability for CV death (c-index 0.81 in derivation cohort, 0.78 in validation cohort), with adequate calibration in both cohorts.

CONCLUSIONS

This model provided a robust tool for the prediction of CV death in patients with stable CHD. As it is based on a small number of readily available biomarkers and clinical factors, it can be widely employed to complement clinical assessment and guide management based on CV risk. (The Stabilization of Atherosclerotic Plaque by Initiation of Darapladib Therapy Trial [STABILITY]; NCT00799903).

Address: Department of Medical Sciences, Cardiology, Uppsala University, Uppsala, Sweden; Uppsala Clinical Research Center, Uppsala University, Uppsala, Sweden. Electronic address: [email protected].; Uppsala Clinical Research Center, Uppsala University, Uppsala, Sweden.; Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada.; Postgraduate Medical School, Grochowski Hospital, Warsaw, Poland.; Cardiovascular Division, Brigham and Women's Hospital, Boston, Massachusetts; Baim Institute of Clinical Research, Boston, Massachusetts.; Duke Clinical Research Institute, Durham, North Carolina.; Department of Medical Sciences, Cardiology, Uppsala University, Uppsala, Sweden; Uppsala Clinical Research Center, Uppsala University, Uppsala, Sweden.; Department of Internal Medicine II-Cardiology, University of Ulm Medical Center, Ulm, Germany; Deutsches Herzzentrum München, Technische Universität München, Munich, Germany; DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany.; Green Lane Cardiovascular Service, Auckland City Hospital, Auckland, New Zealand; University of Auckland, Auckland, New Zealand.; Metabolic Pathways and Cardiovascular Therapeutic Area, GlaxoSmithKline, Collegeville, Pennsylvania.; Department of Cardiology, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands.; Département Hospitalo-Universitaire Fibrosis, Inflammation, and Remodeling, Assistance Publique-Hôpitaux de Paris, Hôpital Bichat, Paris, France; Paris Diderot University, Sorbonne Paris Cité, Paris, France; National Heart and Lung Institute, Imperial College, Institute of Cardiovascular Medicine and Sciences, Royal Brompton Hospital, London, United Kingdom; FACT (French Alliance for Cardiovascular Trials), an F-CRIN network, INSERM U1148, Paris, France.; Uppsala Clinical Research Center, Uppsala University, Uppsala, Sweden; Department of Medical Sciences, Clinical Chemistry, Uppsala University, Uppsala, Sweden.; Medical Clinic V (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany; Institute of Nutrition, Friedrich Schiller University, Jena, Germany.; DACH Society for Prevention of Cardiovascular Disease e.V., Hamburg, Germany.; Mannheim Institute of Public Health, Social and Preventive Medicine, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.; Medical Clinic V (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany; Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria; Synlab Academy, Synlab Holding Deutschland GmbH, Mannheim and Augsburg, Germany.

DOI: 10.1016/j.jacc.2017.06.030

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MeSH Terms: Aged, Benzaldehydes, Biomarkers, Coronary Disease, Female, Follow-Up Studies, Global Health, Humans, Male, Middle Aged, Natriuretic Peptide, Brain, Oximes, Peptide Fragments, Phospholipase A2 Inhibitors, Prognosis, Prospective Studies, Risk Assessment, Risk Factors, Secondary Prevention, Survival Rate, Troponin T