A comparison of random survival forest and Cox regression for prediction of mortality in patients with hemorrhagic stroke

BMC Medical Informatics and Decision Making

Research

Yong Jiang¹  Yuxin Wang²  Meihong Zhou²  Yuhan Deng²  Yinliang Tan²  Baohua Liu² 

[1] Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China;China National Clinical Research Center for Neurological Diseases, Beijing, China;Department of Social Medicine and Health Education, School of Public Health, Peking University, Beijing, China;

关键词: Hemorrhagic stroke;    Random survival forest;    Cox regression;    Intensive care unit;   

DOI  :  10.1186/s12911-023-02293-2

 received in 2023-06-18, accepted in 2023-09-11,  发布年份 2023

来源: Springer

【 摘 要 】

ObjectiveTo evaluate RSF and Cox models for mortality prediction of hemorrhagic stroke (HS) patients in intensive care unit (ICU).MethodsIn the training set, the optimal models were selected using five-fold cross-validation and grid search method. In the test set, the bootstrap method was used to validate. The area under the curve(AUC) was used for discrimination, Brier Score (BS) was used for calibration, positive predictive value(PPV), negative predictive value(NPV), and F1 score were combined to compare.ResultsA total of 2,990 HS patients were included. For predicting the 7-day mortality, the mean AUCs for RSF and Cox regression were 0.875 and 0.761, while the mean BS were 0.083 and 0.108. For predicting the 28-day mortality, the mean AUCs for RSF and Cox regression were 0.794 and 0.649, while the mean BS were 0.129 and 0.174. The mean AUCs of RSF and Cox versus conventional scores for predicting patients’ 7-day mortality were 0.875 (RSF), 0.761 (COX), 0.736 (SAPS II), 0.723 (OASIS), 0.632 (SIRS), and 0.596 (SOFA), respectively.ConclusionsRSF provided a better clinical reference than Cox. Creatine, temperature, anion gap and sodium were important variables in both models.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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