Development of an Electronic Frailty Index for Predicting Mortality in Patients Undergoing Transcatheter Aortic Valve Replacement using Machine Learning.

Autor:	Yiyi Chen, Jiandong Zhou, Shi Kai Chan, Jeffrey, Tong Liu, Hothi, Sandeep S., Roever, Leonardo, Rajan, Rajesh, Ian Chi Kei Wong, Qingpeng Zhang, Tse, Gary, Yan Wang
Předmět:	RESEARCH STATISTICS NONPARAMETRIC statistics HEART valve prosthesis implantation FRAIL elderly SCIENTIFIC observation CONFIDENCE intervals MULTIVARIATE analysis RESEARCH methodology MACHINE learning RETROSPECTIVE studies ACQUISITION of data MANN Whitney U Test REGRESSION analysis RANDOM forest algorithms PEARSON correlation (Statistics) MEDICAL records DESCRIPTIVE statistics SURVIVAL analysis (Biometry) CHI-squared test STATISTICAL hypothesis testing KAPLAN-Meier estimator PREDICTION models DATA analysis DATA analysis software LONGITUDINAL method ALGORITHMS PROPORTIONAL hazards models EVALUATION
Zdroj:	Annals of Clinical Cardiology; Jan-Jun2023, Vol. 5 Issue 1, p17-26, 13p
Abstrakt:	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. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
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