The efficacy and prognosis analysis of short-term spinal cord stimulation in the treatment of zoster-associated pain: a retrospective study.

Background: Zoster-associated pain (ZAP) is the most common and intractable complication in the clinical setting when patients with herpes zoster (HZ) seek medical treatment. Short-term spinal cord stimulation (stSCS) has been validated as an effective means to relieve ZAP. However, in the existing literature, there is a paucity of comprehensive reports elaborating on the risk factors that impact its treatment efficacy. Objective: This study aimed to evaluate the clinical efficacy of stSCS in the treatment of ZAP, and to analyze the risk factors that influence the treatment efficacy. Methods: Clinical data of patients diagnosed with ZAP and who underwent stSCS surgery in the Pain Department of Shanxi Bethune Hospital from January 2020 to December 2023 were collected. A retrospective analysis was performed to evaluate their clinical efficacy. Principal component analysis (PCA) was utilized to screen potential factors influencing the efficacy, and Logistic regression was employed to establish a predictive model. The accuracy of the model was assessed through the Receiver Operating Characteristic (ROC) curve and the C-index. Results: A total of 98 patients were enrolled in this study. After stSCS treatment, the visual analogue scale (VAS) scores of pain were significantly reduced, the sleep quality of patients was improved, and the dosage of analgesic drugs was markedly decreased compared with that before treatment. The results of multivariate Logistic regression analysis indicated that age [odds ratio (OR): 1.175; 95% confidence interval (CI): 1.864-2.584; Conclusion: Our findings suggest that, stSCS effectively relieves ZAP patients' pain, reduces analgesics consumption, improves sleep quality, with low complication rate and high safety. Notably, age, disease course, diabetes mellitus, and pain area size are independent risk factors for its efficacy. The C-index and ROC area, both 0.824, show the prediction model has good accuracy and discriminative power.