Performance of Prognostic Risk Scores in Chronic Heart Failure Patients Enrolled in the European Society of Cardiology Heart Failure Long-Term Registry
Marco Canepa,Candida Fonseca,Ovidiu Chioncel,Cécile Laroche,María G. Crespo-Leiro,Andrew J.S. Coats,Alexandre Mebazaa,Massimo F Piepoli,Luigi Tavazzi,Aldo P. Maggioni +9 more
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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.read more
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Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone
Davide Chicco,Giuseppe Jurman +1 more
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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Effects of Sacubitril/Valsartan on Biomarkers of Extracellular Matrix Regulation in Patients With HFrEF
Michael R. Zile,Eileen O'Meara,Brian Claggett,Margaret F. Prescott,Scott D. Solomon,Karl Swedberg,Milton Packer,John J.V. McMurray,Victor Shi,Martin Lefkowitz,Jean L. Rouleau +10 more
TL;DR: Biomarkers reflecting extracellular matrix homeostasis are elevated in heart failure with preserved ejection fraction, favorably altered by sacubitril/valsartan, and have important prognostic value.
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Heart Failure Association/European Society of Cardiology position paper on frailty in patients with heart failure
Cristiana Vitale,Ewa A. Jankowska,Loreena Hill,Massimo F Piepoli,Wolfram Doehner,Stefan D. Anker,Mitja Lainscak,Tiny Jaarsma,Piotr Ponikowski,Giuseppe M.C. Rosano,Petar M. Seferović,Andrew J.S. Coats +11 more
TL;DR: A new definition of frailty for use by healthcare professionals in the setting of HF is proposed and a foundation for the design of a tailored and validated score for this common condition is created.
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Improving risk prediction in heart failure using machine learning
Eric Adler,Adriaan A. Voors,Liviu Klein,Fima Macheret,Oscar Ö. Braun,Marcus A. Urey,Wenhong Zhu,Iziah E Sama,Matevz Tadel,Claudio Campagnari,Barry H. Greenberg,A. Yagil +11 more
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.
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Outcomes and Effect of Treatment According to Etiology in HFrEF: An Analysis of PARADIGM-HF
Craig Balmforth,Joanne Simpson,Li Shen,Pardeep S. Jhund,Martin Lefkowitz,Adel R. Rizkala,Jean L. Rouleau,Victor Shi,Scott D. Solomon,Karl Swedberg,Michael R. Zile,Milton Packer,John J.V. McMurray +12 more
TL;DR: Just under one-half of patients in this global trial had nonischemic HF with reduced ejection fraction, with idiopathic and hypertensive the most commonly ascribed etiologies.
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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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