An explainable machine learning framework for computable physiologic risk representation in preanesthetic assessment:...

Preanesthetic evaluation requires clinicians to synthesize heterogeneous clinical information, yet perioperative risk communication often relies on categorical representations that do not fully capture physiologic risk. The American Society of Anesthesiologists Physical Status (ASA-PS) classification is widely used but remains subjective, with inter-rater and institutional variability. This study aimed to develop and externally validate an explainable machine learning-based framework for physiologic severity representation in preanesthetic risk assessment.

A retrospective study used data from two institutions in Taiwan: a regional hospital (n = 1,200) for model development and internal validation, and a tertiary medical center (n = 113) for external validation. The target was a physiologic severity label derived from postoperative Acute Physiology and Chronic Health Evaluation II (APACHE II) scores ≥ 12, used as a standardized reference for supervised learning. Feature selection used least absolute shrinkage and selection operator (LASSO) regression, and class imbalance was addressed using an EasyEnsemble-Light Gradient Boosting Machine (LightGBM) approach. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC), calibration analysis, and decision curve analysis (DCA).

The model incorporated six variables: surgical site, age, white blood cell count, heart rate, mean arterial pressure, and smoking status. In internal validation, AUROC was 0.94 (95 % CI 0.90-0.97) versus ASA-PS 0.71 (P < 0.001). In external validation, AUROC was 0.88 (95 % CI 0.81-0.94) versus ASA-PS 0.76. DCA demonstrated favorable net benefit for the APACHE II-derived physiologic severity reference compared with ASA-PS across clinically relevant thresholds. SHapley Additive exPlanations (SHAP) analysis identified cases with discordant risk patterns compared with ASA-PS, illustrating individualized physiologic severity estimates beyond categorical classification.

The proposed framework provides an externally validated, computable physiologic severity representation that complements ASA-PS-based assessment and may support consistent perioperative risk communication and preanesthetic decision support.