Development and validation of a machine learning-based nomogram for prediction of unplanned reoperation post-spinal surgery within 30 days

• Published in: World neurosurgery
• Main Authors: Qiu, Hai-Yang, Lu, Chang-Bo, Liu, Da-Ming, Dong, Wei-Chen, Han, Chao, Dai, Jiao-Jiao, Wu, Zi-Xiang, Lei, Wei, Zhang, Yang
• Format: Journal Article
• Language: English
• Published: United States 19.10.2024
• Subjects: Machine learning algorithms; Diagnosis; Unplanned reoperation; Nomogram; Prediction model; Spine surgery
• ISSN: 1878-8769; 1878-8769
• DOI: 10.1016/j.wneu.2024.10.038

Unplanned 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. Medical 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. Six 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%. The established model can effectively predict URPS in patients and can assist spine surgeons in devising personalized and rational clinical prevention strategies.