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Performance of Prognostic Risk Scores in Chronic Heart Failure Patients Enrolled in the European Society of Cardiology Heart Failure Long-Term Registry

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TLDR
Performance of prognostic risk scores is still limited and physicians are reluctant to use them in daily practice, so the need for contemporary, more precise prognostic tools should be considered.
Abstract
Objectives This study compared the performance of major heart failure (HF) risk models in predicting mortality and examined their utilization using data from a contemporary multinational registry. Background Several prognostic risk scores have been developed for ambulatory HF patients, but their precision is still inadequate and their use limited. Methods This registry enrolled patients with HF seen in participating European centers between May 2011 and April 2013. The following scores designed to estimate 1- to 2-year all-cause mortality were calculated in each participant: CHARM (Candesartan in Heart Failure-Assessment of Reduction in Mortality), GISSI-HF (Gruppo Italiano per lo Studio della Streptochinasi nell9Infarto Miocardico-Heart Failure), MAGGIC (Meta-analysis Global Group in Chronic Heart Failure), and SHFM (Seattle Heart Failure Model). Patients with hospitalized HF (n = 6,920) and ambulatory HF patients missing any variable needed to estimate each score (n = 3,267) were excluded, leaving a final sample of 6,161 patients. Results At 1-year follow-up, 5,653 of 6,161 patients (91.8%) were alive. The observed-to-predicted survival ratios (CHARM: 1.10, GISSI-HF: 1.08, MAGGIC: 1.03, and SHFM: 0.98) suggested some overestimation of mortality by all scores except the SHFM. Overprediction occurred steadily across levels of risk using both the CHARM and the GISSI-HF, whereas the SHFM underpredicted mortality in all risk groups except the highest. The MAGGIC showed the best overall accuracy (area under the curve [AUC] = 0.743), similar to the GISSI-HF (AUC = 0.739; p = 0.419) but better than the CHARM (AUC = 0.729; p = 0.068) and particularly better than the SHFM (AUC = 0.714; p = 0.018). Less than 1% of patients received a prognostic estimate from their enrolling physician. Conclusions Performance of prognostic risk scores is still limited and physicians are reluctant to use them in daily practice. The need for contemporary, more precise prognostic tools should be considered.

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Journal ArticleDOI

Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone

TL;DR: Analysis of a dataset of 299 patients with heart failure collected in 2015 shows that serum creatinine and ejection fraction are sufficient to predict survival of heart failure patients from medical records, and that using these two features alone can lead to more accurate predictions than using the original dataset features in its entirety.
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Improving risk prediction in heart failure using machine learning

TL;DR: Predicting mortality is important in patients with heart failure and current strategies for predicting risk are only modestly successful, likely because they are derived from statistical analysis methods that fail to capture prognostic information in large data sets containing multi‐dimensional interactions.
References
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Journal ArticleDOI

Effects of candesartan on mortality and morbidity in patients with chronic heart failure: the CHARM-Overall programme

TL;DR: Codesartan was generally well tolerated and significantly reduced cardiovascular deaths and hospital admissions for heart failure and there was no significant heterogeneity for candesartan results across the component trials.
Journal ArticleDOI

Prognosis and prognostic research: what, why, and how?

TL;DR: Why research into prognosis is important and how to design such research is explained are explained.
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