학술논문

Development and validation of a nomogram to predict the risk of residual low back pain after tubular microdiskectomy of lumbar disk herniation
Document Type
Original Paper
Source
European Spine Journal. :1-11
Subject
Lumbar disk herniation
Residual low back pain
Risk factor
Tubular
Microdiskectomy
Visual analog scale
Facet orientation
Multifidus fatty atrophy
Facet joint degeneration
Nomogram model
Language
English
ISSN
0940-6719
1432-0932
Abstract
Objective: Tubular microdiskectomy (tMD) is one of the most commonly used for treating lumbar disk herniation. However, there still patients still complain of persistent postoperative residual low back pain (rLBP) postoperatively. This study attempts to develop a nomogram to predict the risk of rLBP after tMD.Methods: The patients were divided into non-rLBP (LBP VAS score < 2) and rLBP (LBP VAS score ≥ 2) group. The correlation between rLBP and these factors were analyzed by multivariate logistic analysis. Then, a nomogram prediction model of rLBP was developed based on the risk factors screened by multivariate analysis. The samples in the model are randomly divided into training and validation sets in a 7:3 ratio. The Receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the diskrimination, calibration and clinical value of the model, respectively.Results: A total of 14.3% (47/329) of patients have persistent rLBP. The multivariate analysis suggests that higher preoperative LBP visual analog scale (VAS) score, lower facet orientation (FO), grade 2–3 facet joint degeneration (FJD) and moderate-severe multifidus fat atrophy (MFA) are risk factors for postoperative rLBP. In the training and validation sets, the ROC curves, calibration curves, and DCAs suggested the good diskrimination, predictive accuracy between the predicted probability and actual probability, and clinical value of the model, respectively.Conclusion: This nomogram including preoperative LBP VAS score, FO, FJD and MFA can serve a promising prediction model, which will provide a reference for clinicians to predict the rLBP after tMD.