AI-Guided Surgical Blood Readiness: Overcoming Real-World Challenges in Prospective Validation for Safer, More Efficient Blood Preparation.

Abstract

Current preoperative blood ordering relies on outdated, procedure-based maximum surgical blood order schedule (MSBOS) that overlook individual patient risk. Smart Match, a machine-learning (ML) tool that predicts patient-specific transfusion needs, was developed and silently validated. Integrated into real-time workflows, Smart Match demonstrates the potential of ML to replace MSBOS with personalized, data-driven approaches to perioperative blood management. Using 82 variables and 1921 features from the electronic medical record of a tertiary academic medical center, a custom extreme gradient boosting (XGBoost) model was optimized to predict perioperative red blood cell (RBC) transfusions before performing adult elective surgeries, aligning sensitivity with native MSBOS thresholds. The retrospective cohort was split into training, validation, and test sets. Silent prospective validation was conducted in real-world elective cases, comparing Smart Match predictions with both MSBOS and clinician RBC preordering behaviors at multiple time points preceding surgery. Model discrimination, calibration, and clinical utility were assessed. In addition, a hybrid model to estimate daily blood bank needs was developed. Retrospective data consisted of 235,054 cases with a 3.04% (95% confidence interval [CI], 2.97 to 3.11) transfusion rate. The model predictors included medical history, laboratory results, demographics, surgery information, medications, transfusion history, and MSBOS recommendations. The test data achieved an area under the receiver-operating-characteristic curve (AUROC) of 0.94 (95% CI, 0.93 to 0.95) and an area under the precision-recall curve (AUPRC) of 0.57 (95% CI, 0.53 to 0.61). Silent prospective validation (n=24,003, transfusion rate 2.18%, 95% CI, 2.07 to 2.46) maintained a 0.94 (95% CI, 0.92 to 0.95) AUROC and a 0.55 (95% CI, 0.51 to 0.60) AUPRC. Prospectively, the model's sensitivity was 0.72 (95% CI, 0.67 to 0.75) and its positive predictive value was 0.34 (95% CI, 0.31 to 0.37), surpassing both MSBOS and clinician behavior. Our hybrid model outperformed MSBOS for daily RBC needs with a mean absolute error of 12.86 (95% CI, 11.86 to 13.84) versus 13.34 (95% CI, 12.14 to 14.51) on the test set. Smart Match reliably outperforms MSBOS and clinician orders, works in real time, and will next be assessed through a randomized control trial to gauge its usability and clinical impact. (Funded by the National Institute of General Medical Sciences and others.).