Machine learning-based early warning system for hemodynamic deterioration in cardiovascular ICU patients: a bidirectional cross-validation study

Background: Early identification of hemodynamic deterioration in cardiovascular intensive care unit (ICU) patients is critical for improving clinical outcomes. Traditional monitoring approaches and scoring systems often fail to capture dynamic multidimensional physiological changes, and existing machine learning models frequently lack robust external validation across diverse healthcare systems. Methods: We employed a retrospective multi-center cohort design to develop machine learning prediction models using the MIMIC-IV database (46,007 admissions) and the eICU database (50,949 admissions). To rigorously assess model robustness and generalizability, a novel bidirectional cross-validation framework was implemented: models were trained on MIMIC data and validated on eICU data, and conversely, trained on eICU data and validated on MIMIC data. The study defined a strict composite outcome comprising hemodynamic instability, tissue hypoperfusion, and confirmed cardiac etiology. Multiple machine learning algorithms were evaluated to identify the optimal classifier. Results: The Random Forest model was selected as the optimal classifier. Bidirectional validation demonstrated exceptional cross-database generalizability: the MIMIC-trained model achieved an Area Under the Receiver Operating Characteristic (AUROC) of 0.841 on the eICU cohort, while the eICU-trained model achieved an AUROC of 0.852 on the MIMIC cohort, with performance degradation controlled within a minimal range (