An explainable machine learning framework for computable physiologic risk representation in preanesthetic assessment: Development and external validation | JoVE Visualize

Area of Science:

Anesthesiology and Perioperative Medicine
Artificial Intelligence in Healthcare
Clinical Risk Assessment

Background:

Preanesthetic evaluation synthesizes complex clinical data, but current risk communication often uses subjective, categorical methods like the American Society of Anesthesiologists Physical Status (ASA-PS) classification.
The ASA-PS classification exhibits inter-rater and institutional variability, limiting its precision in capturing true physiologic risk.
There is a need for more objective and data-driven approaches to represent physiologic severity in preanesthetic assessments.

Purpose of the Study:

To develop and externally validate an explainable machine learning (ML) framework for representing physiologic severity in preanesthetic risk assessment.
To create a computable tool that complements existing subjective risk classification systems.
To enhance the consistency and accuracy of perioperative risk communication.

Main Methods:

A retrospective study utilized data from two Taiwanese institutions for model development (n=1,200) and external validation (n=113).
Supervised learning employed a physiologic severity label derived from postoperative Acute Physiology and Chronic Health Evaluation II (APACHE II) scores (≥12).
Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for feature selection, and EasyEnsemble-Light Gradient Boosting Machine (LightGBM) addressed class imbalance. Model performance was assessed using AUROC, calibration, and DCA.

Main Results:

The ML model incorporated six key variables: surgical site, age, white blood cell count, heart rate, mean arterial pressure, and smoking status.
Internal validation showed an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.94, significantly outperforming ASA-PS (0.71).
External validation achieved an AUROC of 0.88, also superior to ASA-PS (0.76), with Decision Curve Analysis (DCA) favoring the ML approach for clinical utility.

Conclusions:

The developed ML framework provides an externally validated, computable representation of physiologic severity.
This explainable AI tool complements the traditional ASA-PS classification, offering more nuanced risk stratification.
The framework has the potential to support more consistent perioperative risk communication and improve preanesthetic decision-making.