• Hang He, Jun Ma, Chengjie Xiong, Tanjun Wei, Aolin Tang, Yongkang Chen, and Feng Xu.
• Departments of Orthopaedics, General Hospital of Central Theater Command of PLA, Wuhan, China.
• World Neurosurg. 2023 Apr 1; 172: e349e356e349-e356.

ObjectiveTo develop and validate a nomogram for predicting recurrent lumbar disk herniation (LDH) within 2 years after percutaneous endoscopic lumbar discectomy.MethodsInformation on patients' LDH was collected from 1 medical center between January 2015 and September 2020. The LASSO (least absolute shrinkage and selection operator) method was applied to select the most significant risk factors. A multivariate logistic regression analysis was used to develop a predictive model incorporating the possible factors selected by the LASSO regression model. The discriminant, corrected, and clinically useful prediction models were evaluated using consistency index (C-index), receiver operating characteristic curve, calibration curves, and decision curve analysis. Internal validation of clinical predictive power was also assessed by bootstrap validation.ResultsA total of 690 patients with LDH were included in this study. Sixty-three patients experienced recurrence within 2 years whereas 627 experienced no recurrence. The nomogram predictors included age, body mass index, Modic change, Pfirrmann grade, and sagittal range of motion. The model had good discrimination power, with a reliable C-index of 0.868 (95% confidence interval, 0.822-0.913) and interval validation confirmed a higher C-index value of 0.846. The area under the receiver operating characteristic curve was 0.868, indicating a good predictive value. The decision curve analysis indicated that it was clinically feasible to use the predictive recurrence nomogram model.ConclusionsWe developed and validated a new accurate and effective nomogram for predicting recurrent LDH within 2 years after percutaneous endoscopic lumbar discectomy. Age, body mass index, Modic change, Pfirrmann grade, and sagittal range of motion were significant features for predicting rLDH.Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.

15 keywords...

hide…