Validation of Adult Spinal Deformity Surgical Outcome Prediction Tools in Adult Symptomatic Lumbar Scoliosis

作者：James P. Wondra, Michael P. Kelly, Jacob K. Greenberg, Elizabeth L. Yanik, Christopher P. Ames, Ferrán Pellisé, Alba Vila-Casademunt, Justin S. Smith, Shay Bess, Christopher I. Shaffrey, Lawrence G. Lenke, Miquel Serra‐Burriel, Keith H. Bridwell · 发表于：Spine · 年份：2022 · DOI：10.1097/brs.0000000000004416 · 被引用次数：17 · 研究领域：Scoliosis diagnosis and treatment、Spine and Intervertebral Disc Pathology、Medical Imaging and Analysis

STUDY DESIGN: A post hoc analysis. OBJECTIVE: Advances in machine learning (ML) have led to tools offering individualized outcome predictions for adult spinal deformity (ASD). Our objective is to examine the properties of these ASD models in a cohort of adult symptomatic lumbar scoliosis (ASLS) patients. SUMMARY OF BACKGROUND DATA: ML algorithms produce patient-specific probabilities of outcomes, including major complication (MC), reoperation (RO), and readmission (RA) in ASD. External validation of these models is needed. METHODS: Thirty-nine predictive factors (12 demographic, 9 radiographic, 4 health-related quality of life, 14 surgical) were retrieved and entered into web-based prediction models for MC, unplanned RO, and hospital RA. Calculated probabilities were compared with actual event rates. Discrimination and calibration were analyzed using receiver operative characteristic area under the curve (where 0.5=chance, 1=perfect) and calibration curves (Brier scores, where 0.25=chance, 0=perfect). Ninety-five percent confidence intervals are reported. RESULTS: A total of 169 of 187 (90%) surgical patients completed 2-year follow up. The observed rate of MCs was 41.4% with model predictions ranging from 13% to 68% (mean: 38.7%). RO was 20.7% with model predictions ranging from 9% to 54% (mean: 30.1%). Hospital RA was 17.2% with model predictions ranging from 13% to 50% (mean: 28.5%). Model classification for all three outcome measures was better than chance for all [area u...