Predicting Survival in Heart Failure
Predicting Survival in Heart Failure
Aims Using a large international database from multiple cohort studies, the aim is to create a generalizable easily used risk score for mortality in patients with heart failure (HF).
Methods and results The MAGGIC meta-analysis includes individual data on 39 372 patients with HF, both reduced and preserved left-ventricular ejection fraction (EF), from 30 cohort studies, six of which were clinical trials. 40.2% of patients died during a median follow-up of 2.5 years. Using multivariable piecewise Poisson regression methods with stepwise variable selection, a final model included 13 highly significant independent predictors of mortality in the following order of predictive strength: age, lower EF, NYHA class, serum creatinine, diabetes, not prescribed beta-blocker, lower systolic BP, lower body mass, time since diagnosis, current smoker, chronic obstructive pulmonary disease, male gender, and not prescribed ACE-inhibitor or angiotensin-receptor blockers. In preserved EF, age was more predictive and systolic BP was less predictive of mortality than in reduced EF. Conversion into an easy-to-use integer risk score identified a very marked gradient in risk, with 3-year mortality rates of 10 and 70% in the bottom quintile and top decile of risk, respectively.
Conclusion In patients with HF of both reduced and preserved EF, the influences of readily available predictors of mortality can be quantified in an integer score accessible by an easy-to-use website www.heartfailurerisk.org. The score has the potential for widespread implementation in a clinical setting.
Heart failure (HF) is a major cause of death, but prognosis in individual patients is highly variable. Quantifying a patient's survival prospects based on their overall risk profile will help identify those patients in need of more intensive monitoring and therapy, and also help target appropriate populations for trials of new therapies.
There exist previous risk models for patients with HF. Each uses a single cohort of patients and hence their generalizability to other populations is questionable. Each model's development is from a limited cohort size, compromising the ability to truly quantify the best risk prediction model. Also most models are restricted to patients with reduced left-ventricular ejection fraction (EF), thus excluding many HF patients with preserved EF.
The Meta-analysis Global Group in Chronic Heart Failure (MAGGIC) provides a comprehensive opportunity to develop a prognostic model in HF patients, both with reduced and preserved EF. We use readily available risk factors based on 39 372 patients from 30 studies to provide a user-friendly score that readily quantifies individual patient mortality risk.
Abstract and Introduction
Abstract
Aims Using a large international database from multiple cohort studies, the aim is to create a generalizable easily used risk score for mortality in patients with heart failure (HF).
Methods and results The MAGGIC meta-analysis includes individual data on 39 372 patients with HF, both reduced and preserved left-ventricular ejection fraction (EF), from 30 cohort studies, six of which were clinical trials. 40.2% of patients died during a median follow-up of 2.5 years. Using multivariable piecewise Poisson regression methods with stepwise variable selection, a final model included 13 highly significant independent predictors of mortality in the following order of predictive strength: age, lower EF, NYHA class, serum creatinine, diabetes, not prescribed beta-blocker, lower systolic BP, lower body mass, time since diagnosis, current smoker, chronic obstructive pulmonary disease, male gender, and not prescribed ACE-inhibitor or angiotensin-receptor blockers. In preserved EF, age was more predictive and systolic BP was less predictive of mortality than in reduced EF. Conversion into an easy-to-use integer risk score identified a very marked gradient in risk, with 3-year mortality rates of 10 and 70% in the bottom quintile and top decile of risk, respectively.
Conclusion In patients with HF of both reduced and preserved EF, the influences of readily available predictors of mortality can be quantified in an integer score accessible by an easy-to-use website www.heartfailurerisk.org. The score has the potential for widespread implementation in a clinical setting.
Introduction
Heart failure (HF) is a major cause of death, but prognosis in individual patients is highly variable. Quantifying a patient's survival prospects based on their overall risk profile will help identify those patients in need of more intensive monitoring and therapy, and also help target appropriate populations for trials of new therapies.
There exist previous risk models for patients with HF. Each uses a single cohort of patients and hence their generalizability to other populations is questionable. Each model's development is from a limited cohort size, compromising the ability to truly quantify the best risk prediction model. Also most models are restricted to patients with reduced left-ventricular ejection fraction (EF), thus excluding many HF patients with preserved EF.
The Meta-analysis Global Group in Chronic Heart Failure (MAGGIC) provides a comprehensive opportunity to develop a prognostic model in HF patients, both with reduced and preserved EF. We use readily available risk factors based on 39 372 patients from 30 studies to provide a user-friendly score that readily quantifies individual patient mortality risk.
