Predicting heart failure outcome from cardiac and comorbid conditions: The 3C-HF score
Prognostic stratification in heart failure (HF) is crucial to guide clinical management and treatment decision-making. Currently available models to predict HF outcome have multiple limitations. We developed a simple risk stratification model, based on routinely available clinical information includ...
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Published in | International journal of cardiology Vol. 163; no. 2; pp. 206 - 211 |
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Main Authors | , , , , , , , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Shannon
Elsevier Ireland Ltd
20.02.2013
Elsevier |
Subjects | |
Online Access | Get full text |
ISSN | 0167-5273 1874-1754 1874-1754 |
DOI | 10.1016/j.ijcard.2011.10.071 |
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Abstract | Prognostic stratification in heart failure (HF) is crucial to guide clinical management and treatment decision-making. Currently available models to predict HF outcome have multiple limitations. We developed a simple risk stratification model, based on routinely available clinical information including comorbidities, the Cardiac and Comorbid Conditions HF (3C-HF) Score, to predict all-cause 1-year mortality in HF patients.
We recruited in a cohort study 6274 consecutive HF patients at 24 Cardiology and Internal Medicine Units in Europe. 2016 subjects formed the derivation cohort and 4258 the validation cohort. We entered information on cardiac and comorbid candidate prognostic predictors in a multivariable model to predict 1-year outcome.
Median age was 69years, 35.8% were female, 20.6% had a normal ejection fraction, and 65% had at least one comorbidity. During 5861 person-years follow-up, 12.1% of the patients met the study end-point of all-cause death (n=750) or urgent transplantation (n=9). The variables that contributed to outcome prediction, listed in decreasing discriminating ability, were: New York Heart Association class III–IV, left ventricular ejection fraction <20%, no beta-blocker, no renin–angiotensin system inhibitor, severe valve heart disease, atrial fibrillation, diabetes with micro or macroangiopathy, renal dysfunction, anemia, hypertension and older age. The C statistic for 1-year all-cause mortality was 0.87 for the derivation and 0.82 for the validation cohort.
The 3C-HF score, based on easy-to-obtain cardiac and comorbid conditions and applicable to the 1-year time span, represents a simple and valuable tool to improve the prognostic stratification of HF patients in daily practice. |
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AbstractList | Prognostic stratification in heart failure (HF) is crucial to guide clinical management and treatment decision-making. Currently available models to predict HF outcome have multiple limitations. We developed a simple risk stratification model, based on routinely available clinical information including comorbidities, the Cardiac and Comorbid Conditions HF (3C-HF) Score, to predict all-cause 1-year mortality in HF patients.
We recruited in a cohort study 6274 consecutive HF patients at 24 Cardiology and Internal Medicine Units in Europe. 2016 subjects formed the derivation cohort and 4258 the validation cohort. We entered information on cardiac and comorbid candidate prognostic predictors in a multivariable model to predict 1-year outcome.
Median age was 69 years, 35.8% were female, 20.6% had a normal ejection fraction, and 65% had at least one comorbidity. During 5861 person-years follow-up, 12.1% of the patients met the study end-point of all-cause death (n=750) or urgent transplantation (n=9). The variables that contributed to outcome prediction, listed in decreasing discriminating ability, were: New York Heart Association class III-IV, left ventricular ejection fraction <20%, no beta-blocker, no renin-angiotensin system inhibitor, severe valve heart disease, atrial fibrillation, diabetes with micro or macroangiopathy, renal dysfunction, anemia, hypertension and older age. The C statistic for 1-year all-cause mortality was 0.87 for the derivation and 0.82 for the validation cohort.
The 3C-HF score, based on easy-to-obtain cardiac and comorbid conditions and applicable to the 1-year time span, represents a simple and valuable tool to improve the prognostic stratification of HF patients in daily practice. Prognostic stratification in heart failure (HF) is crucial to guide clinical management and treatment decision-making. Currently available models to predict HF outcome have multiple limitations. We developed a simple risk stratification model, based on routinely available clinical information including comorbidities, the Cardiac and Comorbid Conditions HF (3C-HF) Score, to predict all-cause 1-year mortality in HF patients. We recruited in a cohort study 6274 consecutive HF patients at 24 Cardiology and Internal Medicine Units in Europe. 2016 subjects formed the derivation cohort and 4258 the validation cohort. We entered information on cardiac and comorbid candidate prognostic predictors in a multivariable model to predict 1-year outcome. Median age was 69years, 35.8% were female, 20.6% had a normal ejection fraction, and 65% had at least one comorbidity. During 5861 person-years follow-up, 12.1% of the patients met the study end-point of all-cause death (n=750) or urgent transplantation (n=9). The variables that contributed to outcome prediction, listed in decreasing discriminating ability, were: New York Heart Association class III–IV, left ventricular ejection fraction <20%, no beta-blocker, no renin–angiotensin system inhibitor, severe valve heart disease, atrial fibrillation, diabetes with micro or macroangiopathy, renal dysfunction, anemia, hypertension and older age. The C statistic for 1-year all-cause mortality was 0.87 for the derivation and 0.82 for the validation cohort. The 3C-HF score, based on easy-to-obtain cardiac and comorbid conditions and applicable to the 1-year time span, represents a simple and valuable tool to improve the prognostic stratification of HF patients in daily practice. Abstract Background Prognostic stratification in heart failure (HF) is crucial to guide clinical management and treatment decision-making. Currently available models to predict HF outcome have multiple limitations. We developed a simple risk stratification model, based on routinely available clinical information including comorbidities, the Cardiac and Comorbid Conditions HF (3C-HF) Score, to predict all-cause 1-year mortality in HF patients. Methods We recruited in a cohort study 6274 consecutive HF patients at 24 Cardiology and Internal Medicine Units in Europe. 2016 subjects formed the derivation cohort and 4258 the validation cohort. We entered information on cardiac and comorbid candidate prognostic predictors in a multivariable model to predict 1-year outcome. Results Median age was 69 years, 35.8% were female, 20.6% had a normal ejection fraction, and 65% had at least one comorbidity. During 5861 person-years follow-up, 12.1% of the patients met the study end-point of all-cause death (n = 750) or urgent transplantation (n = 9). The variables that contributed to outcome prediction, listed in decreasing discriminating ability, were: New York Heart Association class III–IV, left ventricular ejection fraction < 20%, no beta-blocker, no renin–angiotensin system inhibitor, severe valve heart disease, atrial fibrillation, diabetes with micro or macroangiopathy, renal dysfunction, anemia, hypertension and older age. The C statistic for 1-year all-cause mortality was 0.87 for the derivation and 0.82 for the validation cohort. Conclusions The 3C-HF score, based on easy-to-obtain cardiac and comorbid conditions and applicable to the 1-year time span, represents a simple and valuable tool to improve the prognostic stratification of HF patients in daily practice. Prognostic stratification in heart failure (HF) is crucial to guide clinical management and treatment decision-making. Currently available models to predict HF outcome have multiple limitations. We developed a simple risk stratification model, based on routinely available clinical information including comorbidities, the Cardiac and Comorbid Conditions HF (3C-HF) Score, to predict all-cause 1-year mortality in HF patients.BACKGROUNDPrognostic stratification in heart failure (HF) is crucial to guide clinical management and treatment decision-making. Currently available models to predict HF outcome have multiple limitations. We developed a simple risk stratification model, based on routinely available clinical information including comorbidities, the Cardiac and Comorbid Conditions HF (3C-HF) Score, to predict all-cause 1-year mortality in HF patients.We recruited in a cohort study 6274 consecutive HF patients at 24 Cardiology and Internal Medicine Units in Europe. 2016 subjects formed the derivation cohort and 4258 the validation cohort. We entered information on cardiac and comorbid candidate prognostic predictors in a multivariable model to predict 1-year outcome.METHODSWe recruited in a cohort study 6274 consecutive HF patients at 24 Cardiology and Internal Medicine Units in Europe. 2016 subjects formed the derivation cohort and 4258 the validation cohort. We entered information on cardiac and comorbid candidate prognostic predictors in a multivariable model to predict 1-year outcome.Median age was 69 years, 35.8% were female, 20.6% had a normal ejection fraction, and 65% had at least one comorbidity. During 5861 person-years follow-up, 12.1% of the patients met the study end-point of all-cause death (n=750) or urgent transplantation (n=9). The variables that contributed to outcome prediction, listed in decreasing discriminating ability, were: New York Heart Association class III-IV, left ventricular ejection fraction <20%, no beta-blocker, no renin-angiotensin system inhibitor, severe valve heart disease, atrial fibrillation, diabetes with micro or macroangiopathy, renal dysfunction, anemia, hypertension and older age. The C statistic for 1-year all-cause mortality was 0.87 for the derivation and 0.82 for the validation cohort.RESULTSMedian age was 69 years, 35.8% were female, 20.6% had a normal ejection fraction, and 65% had at least one comorbidity. During 5861 person-years follow-up, 12.1% of the patients met the study end-point of all-cause death (n=750) or urgent transplantation (n=9). The variables that contributed to outcome prediction, listed in decreasing discriminating ability, were: New York Heart Association class III-IV, left ventricular ejection fraction <20%, no beta-blocker, no renin-angiotensin system inhibitor, severe valve heart disease, atrial fibrillation, diabetes with micro or macroangiopathy, renal dysfunction, anemia, hypertension and older age. The C statistic for 1-year all-cause mortality was 0.87 for the derivation and 0.82 for the validation cohort.The 3C-HF score, based on easy-to-obtain cardiac and comorbid conditions and applicable to the 1-year time span, represents a simple and valuable tool to improve the prognostic stratification of HF patients in daily practice.CONCLUSIONSThe 3C-HF score, based on easy-to-obtain cardiac and comorbid conditions and applicable to the 1-year time span, represents a simple and valuable tool to improve the prognostic stratification of HF patients in daily practice. |
Author | Cicoira, Mariantonietta Cioffi, Gianni Parodi, Oberdan Parrella, Piervirgilio Filippatos, Gerasimos De Maria, Renata Ponikowski, Piotr Oliva, Fabrizio Ferrazzi, Paolo Porcu, Maurizio Di Lenarda, Andrea Gavazzi, Antonello Pulignano, Giovanni Böhm, Michael Tribouilloy, Christophe Cottini, Ciro Nodari, Savina Gabrielli, Domenico Senni, Michele |
Author_xml | – sequence: 1 givenname: Michele surname: Senni fullname: Senni, Michele email: msenni@ospedaliriuniti.bergamo.it organization: Dipartimento Cardiovascolare – Dipartimento Medicina Interna, Ospedali Riuniti, Bergamo, Italy – sequence: 2 givenname: Piervirgilio surname: Parrella fullname: Parrella, Piervirgilio organization: Dipartimento Cardiovascolare – Dipartimento Medicina Interna, Ospedali Riuniti, Bergamo, Italy – sequence: 3 givenname: Renata surname: De Maria fullname: De Maria, Renata organization: Istituto Fisiologia Clinica CNR, Milano, Dipartimento Cardiologico Ospedale Niguarda Ca’ Granda, Milano, Italy – sequence: 4 givenname: Ciro surname: Cottini fullname: Cottini, Ciro organization: Dipartimento Cardiovascolare – Dipartimento Medicina Interna, Ospedali Riuniti, Bergamo, Italy – sequence: 5 givenname: Michael surname: Böhm fullname: Böhm, Michael organization: Klinik für Innere Medizin III, Kardiologie, Angiologie und Internistische Intensivmedizin Universitätsklinikum es Saarlandes, Homburg/Saar, Germany – sequence: 6 givenname: Piotr surname: Ponikowski fullname: Ponikowski, Piotr organization: Klinika Kardiologii, Wojskowy Szpital Kliniczny z Polikliniką, Wroclaw, Poland – sequence: 7 givenname: Gerasimos surname: Filippatos fullname: Filippatos, Gerasimos organization: Attikon University Hospital, Athens, Greece – sequence: 8 givenname: Christophe surname: Tribouilloy fullname: Tribouilloy, Christophe organization: Service de Cardiologie, Hopital Sud, University of Amiens, France – sequence: 9 givenname: Andrea surname: Di Lenarda fullname: Di Lenarda, Andrea organization: Divisione di Cardiologia, Azienda Ospedale Università Ospedali Riuniti Trieste, Italy – sequence: 10 givenname: Fabrizio surname: Oliva fullname: Oliva, Fabrizio organization: Dipartimento Cardiologico Ospedale Niguarda Ca’ Granda, Milano, Italy – sequence: 11 givenname: Giovanni surname: Pulignano fullname: Pulignano, Giovanni organization: Dipartimento Cardiovascolare, Ospedale San Camillo, Roma, Italy – sequence: 12 givenname: Mariantonietta surname: Cicoira fullname: Cicoira, Mariantonietta organization: Divisione di Cardiologia, Dipartimento di Scienze Biomediche e Chirurgiche, Università di Verona, Italy – sequence: 13 givenname: Savina surname: Nodari fullname: Nodari, Savina organization: Istituto di Cardiologia, Spedali Civili, Università di Brescia, Italy – sequence: 14 givenname: Maurizio surname: Porcu fullname: Porcu, Maurizio organization: Dipartimento Cardiovascolare, Ospedale Brotzu, Cagliari, Italy – sequence: 15 givenname: Gianni surname: Cioffi fullname: Cioffi, Gianni organization: Struttura Complessa di Cardiologia, Ospedale Villa Bianca, Trento, Italy – sequence: 16 givenname: Domenico surname: Gabrielli fullname: Gabrielli, Domenico organization: Dipartimento Cardiovascolare, Ospedali Riuniti, Ancona, Italy – sequence: 17 givenname: Oberdan surname: Parodi fullname: Parodi, Oberdan organization: Istituto Fisiologia Clinica CNR, Milano, Dipartimento Cardiologico Ospedale Niguarda Ca’ Granda, Milano, Italy – sequence: 18 givenname: Paolo surname: Ferrazzi fullname: Ferrazzi, Paolo organization: Dipartimento Cardiovascolare, Ospedali Riuniti, Bergamo, Italy – sequence: 19 givenname: Antonello surname: Gavazzi fullname: Gavazzi, Antonello organization: Dipartimento Cardiovascolare, Ospedali Riuniti, Bergamo, Italy |
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Snippet | Prognostic stratification in heart failure (HF) is crucial to guide clinical management and treatment decision-making. Currently available models to predict HF... Abstract Background Prognostic stratification in heart failure (HF) is crucial to guide clinical management and treatment decision-making. Currently available... |
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SubjectTerms | Aged Biological and medical sciences Cardiology. Vascular system Cardiovascular Comorbidities Female Heart Heart failure Heart Failure - complications Heart failure, cardiogenic pulmonary edema, cardiac enlargement Humans Male Medical sciences Middle Aged Prognosis Prospective Studies Retrospective Studies Risk models Severity of Illness Index |
Title | Predicting heart failure outcome from cardiac and comorbid conditions: The 3C-HF score |
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