Predicting mechanical complications in adult spinal deformity patients with postoperative proportioned and moderately disproportioned alignment.

ABSTRACT: Objective: Mechanical complications are common after adult spinal deformity (ASD) surgery and can significantly impair outcomes. This study aimed to predict such complications in proportioned and moderately disproportioned patients using a machine learning approach, to inform preoperative planning and enable early preventive care. Methods: Prospectively collected clinical data, including preoperative, intraoperative, and postoperative variables, radiographic param- eters, technical details, and patient-reported outcomes, were obtained from a multi-center ASD surgery database. Parameter tuning of a random forest (RF) classifier was performed using 9-times 3-fold cross-validation over 3 rounds of grid search, with the F-score used as the primary optimization metric. The final RF model was us

SUBMITTER: Balaban B 

PROVIDER: S-EPMC12362533 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature