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Purpose: 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 χ 2=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.
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Purpose: To develop an individualized predictive model for postoperative recurrent lumbar disc herniation (PRLDH) in patients undergoing percutaneous endoscopic transforaminal discectomy (PETD) by considering postoperative activity factors. Patients and Methods: Retrospectively collected data from 612 LDH patients who underwent PETD in our institution from January 2017 to June 2023. They were divided into a training group (429 cases) and a validation group (183 cases). Lasso regression (Model 1) and random forest (Model 2) were applied for variable selection in the training group. The two models were compared in terms of discrimination (the area under curve, AUC), calibration (calibration curve), and clinical utility (decision curve analysis, DCA). Akaike information criterion (AIC) was used for model comparison, and internal validation employed 1000 times Bootstrap + 10-fold cross-validation. Finally, a Nomogram was constructed to display the results and uploaded to the web version. Results: Among 612 treated LDH patients, 66 (10.78%) developed PRLDH. Model 1, superior in AUC, calibration, DCA, and AIC over Model 2, was chosen as the predictive model. Logistic regression in the training group identified BMI, smoking, activity level score, time to first ambulation, diabetes, Modic change, and Pfirrmann grade as independent predictors of PRLDH. Model 1 exhibited a training group AUC of 0.813 (95% CI 0.753-0.872) and a validation group AUC of 0.868 (95% CI 0.773-0.962). At a Youden index of 0.50, sensitivity was 0.73, specificity was 0.77. Internal validation (1000 times Bootstrap + 10-fold cross-validation) for the training group showed accuracy of 0.889, kappa consistency of 0.112, and AUC of 0.757. The Hosmer-Lemeshow goodness-of-fit tests indicated good discriminative ability for Model 1 in both the training (χ2=2.895, P=0.941) and validation groups (χ2=8.197, P=0.414). The DCA and Nomogram are accessible at https://sofarnomogram.shinyapps.io/PRLDHNom/. Conclusion: The Nomogram predictive model, developed based on postoperative activity factors in this study, demonstrates excellent predictive capability, facilitating risk assessment for the occurrence of PRLDH after PETD.
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INTRODUCTION: Lower limb venous anomalies, including duplicated veins, are common and have significant impacts on the outcomes and efficacy of venous surgery. Digital subtraction angiography (DSA) guided venography, serving as the tertiary diagnostic option for venous disorders, offers valuable informations to clinical practitioners. PATIENTS AND METHODS: A retrospective study was conducted on 195 patients with suspected venous disease, evaluating 259 limbs with venography imaging. Two experienced interventional vascularists evaluated the images to determine the incidence and characteristics of variances in the femoral, popliteal, great saphenous, and small saphenous veins. Moreover, blood samples were collected to assess the safety of the venography procedure by monitoring changes in renal function. RESULT: Duplication variations were found in the lower limb veins, with the highest prevalence in the femoral vein (11.28%, 22/195), followed by the great saphenous vein (4.1%, 8/195), and the popliteal vein (1.54%, 3/195). No severe contrast agent allergies or postoperative complications were reported. No statistically significant differences were found in creatinine and urea levels pre- and post-operation for patients without duplication variations, those with duplication of the great saphenous, femoral, or popliteal vein (P < .05). CONCLUSION: DSA-guided venography is effective in identifying venous variations in lower limb disease. DFV is the most common recurrent vein, while DPV is the least. Adequate preparation ensures safety, high spatial resolution, dynamic imaging, and low tissue interference.
