Using machine learning algorithms to predict 28-day mortality in critically ill elderly patients with colorectal cancer

Abstract

Objective: To predict the 28-day mortality of critically ill, elderly patients with colorectal cancer (CRC) using five machine learning approaches. Methods: Data were extracted from the eICU Collaborative Research Database (eICU-CRD) (version 2.0) for a training cohort and from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) and Wuhan Union hospital for validation cohorts. Clinical information (i.e., demographics; initial laboratory tests; vital signs; outcomes) were collected. Five machine learning algorithms (LightGBM, decision tree, XGBoost, random forest, and ensemble model) and a logistic regression were applied for the prediction of 28-day mortality. Results: Overall, 693 patients were included from the eICU cohort, 181 patients from the MIMIC-IV cohort and 95 from the Wuhan Union cohort. Among the six machine learning models, the ensemble model exhibited the best predictive ability (AUC, 0.86), followed by random forest (AUC, 0.83) and LightGBM (AUC, 0.82) in the training cohort. The models also obtained the good predictive performance for the 28-day mortality in the validation cohorts. Conclusions: We showed that machine learning algorithms can be used for the 28-day mortality prediction in critically ill, elderly patients with CRC.