Nomogram Development and Validation for Predicting Postoperative Recurrent Lumbar Disc Herniation Based on Paraspinal Muscle Parameters

Abstract

Ming Tang,1,2,* Siyuan Wang,1,2,* Yiwen Wang,1 Fanyi Zeng,1 Mianpeng Chen,1 Xindong Chang,1 Mingfei He,1 Qingqing Fang,1 Shiwu Yin1 1Department of Interventional Vascular Medicine, Hefei Hospital Affiliated to Anhui Medical University, The Second People’s Hospital of Hefei, Hefei City, Anhui Province, People’s Republic of China; 2The Fifth Clinical College of Medicine, Anhui Medical University, Hefei City, Anhui Province, People’s Republic of China*These authors contributed equally to this workCorrespondence: Shiwu Yin, Department of Interventional Vascular Medicine, Hefei Hospital Affiliated to Anhui Medical University, The Second People’s Hospital of Hefei, 574 Changjiang East Road, Yaohai District, Hefei City, Anhui Province, 230011, People’s Republic of China, Email [email protected]: Previous studies highlight paraspinal muscles’ significance in spinal stability. This study aims to assess paraspinal muscle predictiveness for postoperative recurrent lumbar disc herniation (PRLDH) after lumbar disc herniation patients undergo percutaneous endoscopic transforaminal discectomy (PETD).Patients and Methods: Retrospectively collected data from 232 patients undergoing PETD treatment at our institution between January 2020 and January 2023, randomly allocated into training (60%) and validation (40%) groups. Utilizing Lasso regression and multivariable logistic regression, independent risk factors were identified in the training set to construct a Nomogram model. Internal validation employed Enhanced Bootstrap, with Area Under the ROC Curve (AUC) assessing accuracy. Calibration was evaluated through calibration curves and the Hosmer-Lemeshow goodness-of-fit test. Decision curve analysis (DCA) and clinical impact curve (CIC) were employed for clinical utility analysis.Results: Diabetes, Modic changes, and ipsilesional multifidus muscle skeletal muscle index (SMI) were independent predictive factors for PRLDH following PETD (P< 0.05). Developed Nomogram model based on selected predictors, uploaded to a web page. AUC for training: 0.921 (95% CI 0.872– 0.970), validation: 0.900 (95% CI 0.828– 0.972), respectively. The Hosmer-Lemeshow test yielded χ2=5.638/6.259, P=0.688/0.618, and calibration curves exhibited good fit between observed and predicted values. DCA and CIC demonstrate clinical net benefit for both models at risk thresholds of 0.02– 1.00 and 0.02– 0.80.Conclusion: The Nomogram predictive model developed based on paraspinal muscle parameters in this study demonstrates excellent predictive capability and aids in personalized risk assessment for PRLDH following PETD.Keywords: lumbar disc herniation, percutaneous endoscopic transforaminal discectomy, postoperative recurrent lumbar disc herniation, predictive model

Keywords

• lumbar disc herniation
• percutaneous endoscopic transforaminal discectomy
• postoperative recurrent lumbar disc herniation
• predictive model