Construction and Validation of a Preoperative Surgical Difficulty Prediction and Risk Stratification System for Posterior Spinal Deformity Correction Surgery Based on Machine Learning ‐ Multicenter Cohort Study

ABSTRACT Operative duration reflects surgical complexity and is valuable for perioperative planning. To predict prolonged operative time in posterior spinal deformity correction, we developed a preoperative machine‐learning model. Data from 162 patients across 11 hospitals were analyzed. Ten algorithms were trained and compared, and the best‐performing model was implemented as a web calculator. Boruta identified seven key predictors: interval from illness to surgery, fibrinogen, white blood cell count, activated partial thromboplastin time, American Society of Anesthesiologists class, albumin, and age. LightGBM showed the most consistent performance, achieving an AUC of approximately 0.70. SHAP analyses improved interpretability, and calibration and decision‐curve analyses indicated good calibration and net clinical benefit. We developed an interpretable, internally validated model to estimate operative duration in posterior spinal deformity surgery. The web calculator may support individualized planning and more efficient operating‐room resource allocation. Clinical Trial Registration: NCT05867732