title: Risk Prediction of One-Year Mortality in Patients with Cardiac Arrhythmias Using Random Survival Forest.
abstract: Existing models for predicting mortality based on traditional Cox proportional hazard approach (CPH) often have low prediction accuracy. This paper aims to develop a clinical risk model with good accuracy for predicting 1-year mortality in cardiac arrhythmias patients using random survival forest (RSF), a robust approach for survival analysis. 10,488 cardiac arrhythmias patients available in the public MIMIC II clinical database were investigated, with 3,452 deaths occurring within 1-year followups. Forty risk factors including demographics and clinical and laboratory information and antiarrhythmic agents were analyzed as potential predictors of all-cause mortality. RSF was adopted to build a comprehensive survival model and a simplified risk model composed of 14 top risk factors. The built comprehensive model achieved a prediction accuracy of 0.81 measured by c-statistic with 10-fold cross validation. The simplified risk model also achieved a good accuracy of 0.799. Both results outperformed traditional CPH (which achieved a c-statistic of 0.733 for the comprehensive model and 0.718 for the simplified model). Moreover, various factors are observed to have nonlinear impact on cardiac arrhythmias prognosis. As a result, RSF based model which took nonlinearity into account significantly outperformed traditional Cox proportional hazard model and has great potential to be a more effective approach for survival analysis.
authors.affiliation: Key Laboratory for Health Informatics of the Chinese Academy of Sciences (HICAS), Shenzhen Institutes of Advanced Technology, Shenzhen 518055, China ; Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Beijing 100049, China.
authors.forename: Fen
authors.initials: F
authors.lastname: Miao
authors.affiliation: Key Laboratory for Health Informatics of the Chinese Academy of Sciences (HICAS), Shenzhen Institutes of Advanced Technology, Shenzhen 518055, China.
authors.forename: Yun-Peng
authors.initials: YP
authors.lastname: Cai
authors.affiliation: Department of Senile Cardiovascular Medicine, The General Hospital of the People's Liberation Army, Beijing 100853, China.
authors.forename: Yu-Xiao
authors.initials: YX
authors.lastname: Zhang
authors.affiliation: Key Laboratory for Health Informatics of the Chinese Academy of Sciences (HICAS), Shenzhen Institutes of Advanced Technology, Shenzhen 518055, China.
authors.forename: Ye
authors.initials: Y
authors.lastname: Li
authors.affiliation: Key Laboratory for Health Informatics of the Chinese Academy of Sciences (HICAS), Shenzhen Institutes of Advanced Technology, Shenzhen 518055, China ; Joint Research Centre for Biomedical Engineering, Department of Electronic Engineering, Chinese University of Hong Kong, Shatin 00852, Hong Kong.
authors.forename: Yuan-Ting
authors.initials: YT
authors.lastname: Zhang
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D001145:Arrhythmias, Cardiac / Q000401:mortality*; D003198:Computer Simulation; D016208:Databases, Factual / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D011379:Prognosis; D016016:Proportional Hazards Models; D012307:Risk Factors; D016019:Survival Analysis