Development of a nomogram model for predicting incomplete clinical improvement after percutaneous endoscopic lumbar discectomy.

BACKGROUND

Percutaneous endoscopic lumbar discectomy (PELD) is an effective treatment for lumbar disc herniation (LDH). However, some patients experience incomplete clinical improvement following surgery. This study aimed to develop a predictive nomogram to identify risk factors associated with incomplete clinical improvement after PELD.

METHODS

A total of 705 patients who underwent PELD at our institution between January 2013 and September 2020 were retrospectively analyzed and randomly divided into training and internal validation at a ratio of 7:3. Multivariate logistic regression analysis was performed on the training set to identify significant risk factors and construct the nomogram. Model performance was assessed using receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Additionally, a temporal validation was conducted using data from 80 patients treated at our institution between January 2021 and December 2021.

RESULTS

Incomplete clinical improvement was observed in 9.4 % (66/705) of patients. Multivariate logistic regression analysis identified high body mass index (BMI) (P < 0.001, OR = 1.46), higher preoperative visual analog scale for back pain (VAS-B) scores (P < 0.001, OR = 3.48), a history of previous discectomy (P = 0.043, OR = 2.71), and moderate-to-severe multifidus fat atrophy (MFA) (P = 0.030, OR = 2.32) as independent risk factors. The nomogram demonstrated good discrimination and clinical utility in all datasets. The area under the ROC curve (AUC) was 0.86 (95 % CI: 0.81-0.91) in the training set, 0.86 (95 % CI: 0.79-0.93) in the internal validation set, and 0.93 (95 % CI: 0.87-0.99) in the temporal validation set.

CONCLUSIONS

This nomogram provides a reliable tool for predicting incomplete clinical improvement after PELD. High BMI, elevated preoperative VAS-B scores, previous discectomy, and MFA are significant predictors and should be considered when evaluating patient prognosis.