• Hai-Yang Qiu, Chang-Bo Lu, Da-Ming Liu, Wei-Chen Dong, Chao Han, Jiao-Jiao Dai, Zi-Xiang Wu, Wei Lei, and Yang Zhang.
• Department of Orthopaedics, the First Affiliated Hospital of Air Force Medical University, Xi'an, Shanxi Province, 710032, China.
• World Neurosurg. 2024 Oct 19.

BackgroundUnplanned reoperation post-spinal surgery (URPS) leads to prolonged hospital stays, higher costs, decreased patient satisfaction, and adversely affects postoperative rehabilitation. This study aimed to develop and validate prediction models (nomograms) for early URPS risk factors using machine learning (ML) methods, aiding spine surgeons in designing prevention strategies, promoting early recovery, reducing complications and improving patient satisfaction.MethodsMedical records of 639 patients who underwent reoperation post-spinal surgery (RPS) from the First Affiliated Hospital of Air Force Medical University (2018 - 2022) were collected, including baseline indicators, perioperative indicators, and laboratory indicators. After applying inclusion and exclusion criteria, 122 URPS and 155 non-URPS patients were identified and randomly divided into training (82 URPS, 111 non-URPS) and validation (40 URPS, 44 non-URPS) cohorts. Three ML methods (LASSO regression, Random Forest, and SVM-RFE) were used to select feature variables, and their intersection was used to develop the prediction model, tested on the validation cohort.ResultsSix factors-implant, postoperative suction drainage (PSD), gelatin sponge (GS), anticoagulants (ATG), antibiotics (ATB), and disease type (DT)-were identified to construct a nomogram diagnostic model. The area under the curve (AUC) of this nomogram was 0.829 (95% CI 0.771-0.886) in the training cohort and 0.854 (95% CI 0.775-0.933) in the validation cohort. Calibration curves demonstrated satisfactory agreement between predictions and actual probabilities. The decision curve indicated clinical usefulness with a threshold between 1% and 90%.ConclusionsThe established model can effectively predict URPS in patients and can assist spine surgeons in devising personalized and rational clinical prevention strategies.Copyright © 2024. Published by Elsevier Inc.

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