Contributors: Centre d'investigation clinique plurithématique Pierre Drouin Nancy (CIC-P); Centre d'investigation clinique Nancy (CIC); Université de Lorraine (UL)-Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL)-Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM); Défaillance Cardiovasculaire Aiguë et Chronique (DCAC); Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL); Cardiovascular and Renal Clinical Trialists Vandoeuvre-les-Nancy (INI-CRCT); Institut Lorrain du Coeur et des Vaisseaux Louis Mathieu Nancy; French-Clinical Research Infrastructure Network - F-CRIN Paris (Cardiovascular & Renal Clinical Trialists - CRCT ); National Heart Centre Singapore (NHCS); University of Amsterdam Amsterdam (UvA); University Medical Center Groningen Groningen (UMCG); University of Leicester; Stavanger University Hospital; Berlin-Brandenburg Center for Regenerative Therapies Berlin, Germany; Charité - UniversitätsMedizin = Charité - University Hospital Berlin; University General Hospital “ Attikon ” Athens, Greece; University of Cyprus (UCY); Robertson Centre for Biostatistics and Clinical Trials, University of Glasgow and National Heart & Lung Institute; National Heart and Lung Institute London (NHLI); Imperial College London-Royal Brompton and Harefield NHS Foundation Trust; Università degli Studi di Brescia Brescia; Contrat de Plan Etat Région Lorraine and FEDER IT2MP; IMPACT GEENAGE; ANR-15-RHUS-0004,FIGHT-HF,Combattre l'insuffisance cardiaque(2015); ANR-15-IDEX-0004,LUE,Isite LUE(2015); European Project: 242209,EC:FP7:HEALTH,FP7-HEALTH-2009-single-stage,BIOSTAT-CHF(2010)
نبذة مختصرة : International audience ; AIMS:Heart failure (HF) patients are at high-risk of cardiovascular (CV) events, including CV death. Nonetheless, a substantial proportion of these patients die from non-CV causes. Identifying patients at higher risk for each individual event may help selecting patients for clinical trials and tailoring cardiovascular therapies. The aims of the present study are to: (i) characterize patients according to CV vs. non-CV death; (ii) develop models for the prediction of the respective events; (iii) assess the models' performance to differentiate CV from non-CV death.METHODS AND RESULTS:This study included 2309 patients with HF from the BIOSTAT-CHF (a systems BIOlogy Study to TAilored Treatment in Chronic Heart Failure) study. Competing-risk models were used to assess the best combination of variables associated with each cause-specific death. Results were validated in an independent cohort of 1738 HF patients. The best model to predict CV death included low blood pressure, estimated glomerular filtration rate ≤ 60 mL/min, peripheral oedema, previous HF hospitalization, ischaemic HF, chronic obstructive pulmonary disease, elevated N-terminal pro-B-type natriuretic peptide (NT-proBNP), and troponin (c-index = 0.73). The non-CV death model incorporated age > 75 years, anaemia and elevated NT-proBNP (c-index = 0.71). Both CV and non-CV death rose by quintiles of the risk scores; yet these models allowed the identification of patients in whom absolute CV death rates clearly outweigh non-CV death ones. These findings were externally replicated, but performed worse in a less severely diseased population.CONCLUSIONS:Risk models for predicting CV and non-CV death allowed the identification of patients at higher absolute risk of dying from CV causes (vs. non-CV ones). Troponin helped in predicting CV death only, whereas NT-proBNP helped in the prediction of both CV and non-CV death. These findings can be useful both for tailoring therapies and for patient selection in HF trials in order to attain ...
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