Prospective Validation of the EAC+ Algorithm for Surgical Timing in Multi-System Trauma: A Multicentre Pragmatic Trial

The optimal timing of definitive surgical intervention in multi-system trauma remains a decisive factor in patient outcomes, yet existing protocols often fail to reflect real-time physiological readiness. This prospective multicentre study evaluates the Early Appropriate Care Plus (EAC+) algorithm, a next-generation, physiology-driven decision-support system designed to guide surgical timing. EAC + integrates continuous monitoring of arterial pH (≥ 7.25), lactate (≤ 4.0 mmol/L), and base excess (≥ − 5.5 mEq/L) with explainable artificial intelligence to generate readiness alerts that are both evidence-based and interpretable to clinicians. Unlike traditional Early Appropriate Care (EAC) protocols, which apply static thresholds at isolated time points, and previous machine learning–based models, which are often retrospective and non-interoperable, EAC + delivers dynamic, real-time, and multi-centre–validated guidance embedded within standard trauma workflows. Involving 3,512 patients across five Level 1 trauma centres, the algorithm was benchmarked against standard practice, original EAC thresholds, logistic regression, and decision tree models. EAC + achieved the highest accuracy (91.4%), AUROC (0.94), precision (0.89), and recall (0.92), while reducing unnecessary surgical delays, shortening ICU stay by an average of 1.6 days, and lowering 30-day mortality. SHAP-based feature attribution confirmed the algorithm’s physiological validity, with pH, lactate, and base excess emerging as the most influential predictors. This study provides the first prospective, real-time, multicentre validation of a physiology-based surgical readiness algorithm that unifies clinical evidence, continuous monitoring, and transparent AI. Its adoption could standardize surgical timing decisions, improve workflow efficiency, and inform national trauma care guidelines.