Beyond Static Thresholds: Oscillatory Hemodynamic Instability as a Prodromal Marker for Intraoperative Hypotension using Explainable Machine Learning

Background: Intraoperative hypotension (IOH) is strongly associated with postoperative myocardial injury, acute kidney injury, and mortality. Current monitoring relies on reactive threshold alarms, often alerting clinicians only after hemodynamic compromise has occurred. We hypothesized that a machine learning (ML) approach utilizing engineered hemodynamic volatility features could predict IOH five minutes before its occurrence. Methods: A retrospective observational study was conducted using high-resolution intraoperative monitoring data from the VitalDB registry. The cohort included 1,750 adult patients undergoing non-cardiac surgery. We developed and compared three ML algorithms Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) trained on physiological features including arterial pressure trends and rolling volatility indices. Performance was evaluated using the Area Under the Receiver Operating Characteristic Curve (AUROC) for discrimination and the Brier Score for calibration. Results: All models demonstrated robust predictive capability. The Random Forest model achieved the highest discrimination (AUROC 0.837), outperforming LR (0.824) and XGBoost (0.803). However, XGBoost demonstrated superior calibration with a Brier Score of 0.0825 (vs. 0.153 for RF), indicating more reliable probabilistic risk estimates. Feature importance analysis consistently identified hemodynamic volatility (rolling standard deviation of MAP) as the dominant predictor across all models. At the optimal threshold, the system demonstrated a sensitivity of 69.5% and specificity of 75.3%. Conclusions: We identified a trade-off between discrimination and calibration: Random Forest offers the best ranking for early warning, while XGBoost provides the most accurate risk probability. Crucially, hemodynamic instability was identified as a critical prodromal marker, suggesting that oscillatory variance precedes hypotension.