Interpretable machine learning models using early dynamic clinical features to predict early hemodynamic...

Early hemodynamic stabilization after vasopressor initiation in septic shock is heterogeneous and difficult to predict. We aimed to develop and externally validate interpretable machine learning models for predicting early hemodynamic stabilization.

This retrospective study used MIMIC-IV for model development and internal validation and eICU for external validation. Adult ICU patients with septic shock receiving vasopressors were included. Dynamic clinical features during the first 6 h after vasopressor initiation were used to predict stabilization during 6-24 h, defined by sustained mean arterial pressure (MAP) control without vasopressor escalation or increased norepinephrine-equivalent dose. Seven machine learning models were compared. Performance was assessed using discrimination, calibration, Brier score, decision curve analysis, external validation, and SHAP.

The MIMIC-IV and eICU cohorts included 3,445 and 453 patients, with stabilization rates of 41.77% and 32.67%, respectively. Gradient boosting machine (GBM) showed the best overall performance, with an AUC of 0.720 and Brier score of 0.209 in internal validation and an AUC of 0.730 and Brier score of 0.192 in external validation. At the training-derived threshold, external sensitivity and specificity were 0.466 and 0.797. SHAP identified early MAP stability, lactate clearance, platelet count, renal function, inflammatory burden, and vasopressor intensity as major predictors. The stabilization endpoint was associated with ICU and hospital mortality and remained robust to alternative MAP definitions. However, predictive performance was substantially driven by proximal hemodynamic features, particularly MAP trajectories, and the full GBM did not consistently outperform simpler hemodynamic benchmarks externally.

Early dynamic clinical features provided moderate discrimination for predicting hemodynamic stabilization after vasopressor initiation, with similar external performance. The model should be interpreted as a structured risk-stratification tool rather than a measure of pharmacological vasopressor responsiveness. Prospective validation is required before clinical implementation.