Key result
A random forest machine learning model effectively predicted in-hospital mortality among critically ill liver cancer patients in the ICU, achieving an AUROC of 0.857 in the external validation cohort.
Why the study?
Liver cancer has high mortality especially among ICU patients, but reliable tools for early prediction of in-hospital mortality in these patients are lacking.
Observational (n=1,554)
No
Effect estimate: AUROC 0.857 (95% CI 0.826-0.889)
Machine learning models, particularly random forest, can effectively predict in-hospital mortality for critically ill liver cancer patients in the ICU using routine clinical data and severity scores.
May inform ICU mortality risk stratification in liver cancer; leaves open prospective validation before clinical adoption.
Liver cancer has a high incidence and mortality rate globally, particularly in patients requiring intensive care unit (ICU) admission. Early prediction of in-hospital mortality for these patients is crucial, yet lacking reliable tools. This study aims to develop and evaluate machine learning (ML) models for predicting in-hospital mortality in critically ill liver cancer patients admitted to the ICU. This retrospective study used data from the MIMIC-III and MIMIC-IV databases, including 862 patients from MIMIC-III (training cohort) and 692 patients from MIMIC-IV (validation cohort). The study focused on patients diagnosed with liver cancer, identified by specific ICD codes. Four ML algorithms, namely logistic regression, random forest, XGBoost, and LightGBM, were used to predict in-hospital mortality based on clinical characteristics, laboratory results, and severity scores. Performance was evaluated using accuracy, AUROC, AUPRC, F1 score, and Kaplan-Meier curves. A total of 1,554 patients were included. The random forest model demonstrated the best performance, with an AUROC of 0.911 (95% CI: 0.855-0.956) and an AUPRC of 0.823 (95% CI: 0.718-0.905) in the internal test set, and an AUROC of 0.857 (95% CI: 0.826-0.889) and an accuracy of 0.828 (95% CI: 0.802-0.857) in the external validation set. Kaplan-Meier curves showed that all four models effectively stratified high-risk and low-risk groups. Key features influencing the prediction included APSIII, SAPSII, LODS, OASIS, and vital signs such as heart rate, temperature, and oxygen saturation. Feature importance analysis revealed that clinical severity scores played a major role in predicting mortality. This study demonstrates the potential of machine learning algorithms, particularly random forest, in predicting in-hospital mortality for critically ill liver cancer patients in the ICU. The identified clinical features provide valuable insights for clinicians in assessing patient risk and making timely interventions.
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Zheng et al. (2025) conducted an observational in Liver cancer requiring ICU admission (n=1,554). Machine learning predictive models (Random Forest) was evaluated on In-hospital mortality prediction (AUROC in external validation) (AUROC 0.857, 95% CI 0.826-0.889). A random forest machine learning model effectively predicted in-hospital mortality among critically ill liver cancer patients in the ICU, achieving an AUROC of 0.857 in the external validation cohort.
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