João Pedro Ferreira1, Wouter Ouwerkerk2,3, Jasper Tromp2,4, Leong Ng5, Kenneth Dickstein6, Stefan Anker7, Gerasimos Filippatos8,9, John G Cleland10,11, Marco Metra12, Dirk J van Veldhuisen4, Adriaan A Voors4, Faiez Zannad1. 1. Université de Lorraine, Inserm, Centre d'Investigations Cliniques- Plurithématique 14-33, and Inserm U1116, CHRU, F-CRIN INI-CRCT (Cardiovascular and Renal Clinical Trialists), Nancy, France. 2. National Heart Centre Singapore, Hospital Drive, Singapore. 3. Department of Dermatology, Amsterdam UMC, Amsterdam Infection & Immunity Institute, University of Amsterdam, Amsterdam, The Netherlands. 4. Department of Cardiology, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands. 5. Department of Cardiovascular Sciences, NIHR Leicester Biomedical Research Centre, Glenfield Hospital, University of Leicester, Leicester, UK. 6. Stavanger University Hospital, University of Bergen, Bergen, Norway. 7. Department of Cardiology (CVK), and Berlin-Brandenburg Center for Regenerative Therapies (BCRT), German Centre for Cardiovascular Research (DZHK) partner site Berlin, Charité Universitätsmedizin, Berlin, Germany. 8. National and Kapodistrian University of Athens, Attikon General Hospital, Athens, Greece. 9. University of Cyprus, School of Medicine, Nicosia, Cyprus. 10. Robertson Centre for Biostatistics and Clinical Trials, University of Glasgow, Glasgow, UK. 11. National Heart and Lung Institute, Imperial College London, London, UK. 12. Institute of Cardiology, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy.
Abstract
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 CV event enrichment.
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 HFpatients. 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 CV event enrichment.
Authors: Karola S Jering; Claudio Campagnari; Brian Claggett; Eric Adler; Liviu Klein; Faraz S Ahmad; Adriaan A Voors; Scott Solomon; Avi Yagil; Barry Greenberg Journal: Eur J Heart Fail Date: 2022-05-22 Impact factor: 17.349
Authors: Milton Packer; James L Januzzi; Joao Pedro Ferreira; Stefan D Anker; Javed Butler; Gerasimos Filippatos; Stuart J Pocock; Martina Brueckmann; Waheed Jamal; Daniel Cotton; Tomoko Iwata; Faiez Zannad Journal: Eur J Heart Fail Date: 2021-06-21 Impact factor: 17.349
Authors: Thor Ueland; Lars Gullestad; Lei Kou; James B Young; Marc A Pfeffer; Dirk Jan van Veldhuisen; Karl Swedberg; John J V Mcmurray; Akshay S Desai; Inderjit S Anand; Pål Aukrust Journal: Clin Res Cardiol Date: 2021-10-05 Impact factor: 5.460
Authors: Mintu Nath; Simon P R Romaine; Andrea Koekemoer; Stephen Hamby; Thomas R Webb; Christopher P Nelson; Marcos Castellanos-Uribe; Manolo Papakonstantinou; Stefan D Anker; Chim C Lang; Marco Metra; Faiez Zannad; Gerasimos Filippatos; Dirk J van Veldhuisen; John G Cleland; Leong L Ng; Sean T May; Federica Marelli-Berg; Adriaan A Voors; James A Timmons; Nilesh J Samani Journal: Eur J Heart Fail Date: 2022-06-03 Impact factor: 17.349