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Development of an electronic frailty index for predicting mortality in patients undergoing transcatheter aortic valve replacement using machine learning
Yiyi Chen1, Jiandong Zhou2, Jeffrey Shi Kai Chan3, Tong Liu4, Sandeep S Hothi5, Leonardo Roever6, Rajesh Rajan7, Ian Chi Kei Wong8, Qingpeng Zhang2, Gary Tse9, Yan Wang10
1 Department of Practice and Policy, School of Pharmacy, University College London, London, UK
2 School of Data Science, City University of Hong Kong, Hong Kong, China
3 Department of Medicine and Therapeutics, Prince of Wales Hospital, Hospital Authority; Heart Failure and Structural Heart Disease Unit, Cardiovascular Analytics Group, Hong Kong, China-UK Collaboration
4 Department of Cardiology, Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular disease, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China
5 Heart and Lung Centre, New Cross Hospital, Wolverhampton, UK
6 Department of Clinical Research, Federal University of Uberlândia, Uberlândia, Brazil
7 Department of Cardiology, Sabah Al Ahmed Cardiac Centre, Al Amiri Hospital, Kuwait City, Kuwait
8 Department of Pharmacology and Pharmacy, University of Hong Kong, Pokfulam, Hong Kong, China
9 Department of Cardiology, Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular disease, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China; Kent and Medway Medical School, Canterbury CT2 7FS, United Kingdom; Xiamen Cardiovascular Hospital, Xiamen University, Xiamen, China
10 Xiamen Cardiovascular Hospital, Xiamen University, Xiamen, China
Correspondence Address:
Gary Tse,
Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin 300 211, Xiamenv Cardiovascular Hospital, Xiamen University, Xiamen
Qingpeng Zhang,
School of Data Science, City University of Hong Kong, Hong Kong
China
 Source of Support: None, Conflict of Interest: None
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Background: Electronic frailty indices can be useful surrogate measures of frailty. Objective: This study is to develop an electronic frailty index that incorporates patient demographics, baseline comorbidities, health-care utilization characteristics, electrocardiographic measurements, and laboratory examinations for predicting all-cause mortality in patients undergoing transcatheter aortic valve replacement (TAVR). Methods: This was a multicenter retrospective observational study of patients undergoing for TAVR. Significant univariate and multivariate predictors of all-cause mortality were identified using Cox regression. Importance ranking of variables was obtained with a gradient boosting survival tree (GBST) model, a supervised sequential ensemble learning algorithm, and used to build the frailty models. Comparisons were made between multivariate Cox, GBST, and random survival forest models. Results: A total of 450 patients (49% of females; median age at procedure, 82.3 [interquartile range, 79.0–86.0]) were included, of which 22 died during follow-up. A machine learning survival analysis model found that the most important predictors of mortality were activated partial thromboplastin time, followed by INR, severity of tricuspid regurgitation, cumulative hospital stays, cumulative number of readmissions, creatinine, urate, alkaline phosphatase, and QTc/QT intervals. GBST significantly outperformed random survival forests and multivariate Cox regression (precision: 0.91, recall: 0.89, AUC: 0.93, C-index: 0.96, and KS-index: 0.50) for mortality prediction. Conclusions: An electronic frailty index incorporating multidomain data can efficiently predict all-cause mortality in patients undergoing TAVR. A machine learning survival learning model significantly improves the risk prediction performance of the frailty models.
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