Derivation of an electronic frailty index for short-term mortality in heart failure: a machine learning approach
Derivation of an electronic frailty index for short-term mortality in heart failure: a machine learning approach
Abstract ObjectiveFrailty may be found in heart failure patients especially in the elderly and is associated with a poor prognosis. However, assessment of frailty status is time-consuming and the electronic frailty indices developed using health records have served as useful surrogates. We hypothesized that an electronic frailty index developed using machine learning can improve short-term mortality prediction in patients with heart failure. MethodsThis was a retrospective observational study included patients admitted to nine public hospitals for heart failure from Hong Kong between 2013 and 2017. Age, sex, variables in the modified frailty index, Deyo’s Charlson comorbidity index (≥2), neutrophil-to-lymphocyte ratio (NLR) and prognostic nutritional index (PNI) were analyzed. Gradient boosting, which is a supervised sequential ensemble learning algorithm with weak prediction submodels (typically decision trees), was applied to predict mortality. Comparisons were made with decision tree and multivariate logistic regression. ResultsA total of 8893 patients (median: age 81, Q1-Q3: 71-87 years old) were included, in whom 9% had 30-day mortality and 17% had 90-day mortality. PNI, age and NLR were the most important variables predicting 30-day mortality (importance score: 37.4, 32.1, 20.5, respectively) and 90-day mortality (importance score: 35.3, 36.3, 14.6, respectively). Gradient boosting significantly outperformed decision tree and multivariate logistic regression (area under the curve: 0.90, 0.86 and 0.86 for 30-day mortality; 0.92, 0.89 and 0.86 for 90-day mortality). ConclusionsThe electronic frailty index based on comorbidities, inflammation and nutrition information can readily predict mortality outcomes. Their predictive performances were significantly improved by gradient boosting techniques.
Zhou Jiandong、Lee Sharen、Kei Wong Ian Chi、Zhang Yuhui、Liu Tong、Zhang Qingpeng、Wei Li、Liu Ying、Chan Esther WY、Tse Gary、Ju Chengsheng、Tan Martin Sebastian
School of Data Science, City University of Hong KongLaboratory of Cardiovascular Physiology, LKS Institute of Health Sciences, Chinese University of Hong KongResearch Department of Practice and Policy, School of Pharmacy, University College London||Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, The University of Hong KongHeart Failure Center, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences & Peking Union Medical CollegeTianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical UniversitySchool of Data Science, City University of Hong KongResearch Department of Practice and Policy, School of Pharmacy, University College LondonHeart Failure and Structural Cardiology Division, Department of Cardiology, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning ProvinceCentre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, The University of Hong KongHeart Failure and Structural Cardiology Division, Department of Cardiology, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning Province||Faculty of Health and Medical Sciences, University of SurreyResearch Department of Practice and Policy, School of Pharmacy, University College LondonFaculty of Arts and Science, University of Toronto
医学研究方法内科学
Frailty indexheart failuremortalityinflammationnutritionmachine learning
Zhou Jiandong,Lee Sharen,Kei Wong Ian Chi,Zhang Yuhui,Liu Tong,Zhang Qingpeng,Wei Li,Liu Ying,Chan Esther WY,Tse Gary,Ju Chengsheng,Tan Martin Sebastian.Derivation of an electronic frailty index for short-term mortality in heart failure: a machine learning approach[EB/OL].(2025-03-28)[2025-05-24].https://www.medrxiv.org/content/10.1101/2020.12.26.20248867.点此复制
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