Personalized Anesthetic Planning Using Predictive Clinical Algorithms

Abstract

Predictive clinical algorithms are increasingly being used to individualize anesthetic care, drawing on preoperative, intraoperative, and genomic data to forecast risk and guide drug selection at the level of the individual patient. This review surveys machine-learning models for preoperative and intraoperative risk stratification, algorithms targeting specific physiologic events such as post-induction hypotension, pharmacogenomic approaches to individualized opioid and anesthetic dosing, and closed-loop systems that couple prediction to automated drug delivery. The strongest current evidence supports narrowly scoped intraoperative event prediction and closed-loop control of anesthetic depth, both of which have short and well-characterized causal pathways from measurable inputs to outcome. Broader complication-risk models and genotype-guided dosing protocols remain conceptually promising but are limited by insufficient external validation, workflow integration challenges, and a paucity of prospective outcome trials. We conclude that these tools are best understood, at their current stage of development, as a calibrated adjunct to clinical judgment rather than a replacement for it, and identify the validation and integration work still needed before routine adoption.