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Development and multicenter validation of machine learning models for 28-day mortality in critically ill patients
Jinbin Yang1,2, Tianci Huang1, Hongsheng Lu1
1Intensive Care Unit, The Second Affiliated Hospital of Guangxi Medical University, No 166 Daxuedong Road, Nanning, 530007, China.
Background:
Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is a common critical illness in the intensive care unit (ICU) and is associated with poor outcomes. Marked clinical heterogeneity limits the ability of conventional scoring systems to achieve accurate early mortality risk stratification. This study aimed to develop and validate machine learning (ML) models for early prediction of 28-day mortality in critically ill patients with AECOPD.
Methods:
In this multicentre retrospective study, ML models were developed using the Medical Information Mart for Intensive Care IV database and externally validated in the eICU Collaborative Research Database. Adult patients with AECOPD undergoing first ICU admission were included, and routinely available clinical variables within the first 24 h after ICU admission were extracted. Least absolute shrinkage and selection operator regression was used for feature selection, and nine ML models were developed. Model performance was assessed in terms of discrimination, precision-recall performance, calibration, and clinical utility, and compared with conventional clinical scores. Class weighting was used in the primary analysis, while SMOTE and additional sensitivity analyses were performed to assess robustness.
Results:
A total of 715 patients were included, of whom 84 (11.7%) died within 28 days. LASSO selected 15 predictive variables. In the internal test set, support vector machine achieved the highest AUC of 0.722. In the external validation cohort, LightGBM achieved the highest AUC of 0.764 and F1 score, GBM showed the highest sensitivity, XGBoost achieved the highest negative predictive value and balanced accuracy, and random forest achieved the highest PR-AUC. Calibration performance varied across models and datasets. Compared with conventional clinical scores, several ML models showed more favourable numerical performance, although no single model consistently outperformed the others across all evaluation dimensions. Sensitivity analyses suggested that the main conclusions were broadly robust.
Conclusions:
ML models based on routinely available ICU data showed exploratory potential for 28-day mortality risk stratification in critically ill patients with AECOPD. Given the limited number of death events, class imbalance, and risk of overfitting, these findings should be interpreted cautiously. The models are not ready for clinical implementation and require further prospective validation, local recalibration, and evaluation within real-world clinical workflows.
