Machine Learning Predicts ICU In-Hospital Mortality in Ards Patients Aged 80 and Above: A Multinational Multicenter
Xiaozhu Liu1, Xiangyu Sun2, Xinhe Zhou3
1Department of Critical Care Medicine, Clinical and Research Center on Acute Lung Injury, Emergency and Critical Care Center, Beijing Shijitan Hospital, Capital Medical University, Beijing, China.
Background:
The present study aims to develop and validate an interpretable machine learning (ML) model based on a multicenter cohort, which is intended for predicting the mortality of acute respiratory distress syndrome patients aged over 80 years admitted to the intensive care unit (ICU) and realizing risk stratification for this patient population.
Methods:
The research cohort drew from ICU clinical data from six medical institutions in China and the Medical Information Mart for Intensive Care (MIMIC-IV) database. In this study, eight distinct ML methods were employed to construct predictive models. The comprehensive performance of these models was evaluated using metrics such as the area under the receiver operating characteristic curve (AUC). The best-performing model was utilized for risk prediction and patient stratification, and its results were compared with those of the traditional Acute Physiology and Chronic Health Evaluation II scoring system and the oxygenation index-based risk classification. Furthermore, SHapley Additive exPlanations analysis was applied to interpret the model's intrinsic decision-making mechanisms at both global and local levels.
Results:
Among the eight ML models evaluated, the random forest (RF) model demonstrated the highest overall performance (area under the receiver operating characteristic curve = 0.835) and was selected as the final predictive model. Utilizing the RF model, patients were stratified into risk categories. The results indicate that the model accurately predicted patient mortality and effectively stratified patients based on risk. Furthermore, the risk prediction and stratification capabilities of the RF model significantly outperformed those of the Acute Physiology and Chronic Health Evaluation II scoring system and the oxygenation index-based risk classification.
Conclusion:
The ML model developed on the basis of a multicenter cohort demonstrated accurate prediction of mortality in ICU patients aged over 80 with acute respiratory distress syndrome. Integrated with SHapley Additive exPlanations analysis, the model enables precise interpretation of risk predictions and provides a scientific and effective basis for the clinical risk stratification management of these patients.
