Risk factors for poor prognosis following interventional treatment in patients with postherpetic neuralgia and construction of a predictive model
10.3760/cma.j.cn131073.20231129.00412
- VernacularTitle:带状疱疹后神经痛患者介入治疗预后不良的危险因素和预测模型构建
- Author:
Youjia YU
1
;
Junpeng YUAN
;
Huichan XU
;
Yan LI
;
Shaoyong SONG
;
Xiaohong JIN
Author Information
1. 苏州大学附属第四医院疼痛科，苏州 215125
- Keywords:
Neuralgia, postherpetic;
Prognosis;
Risk factors;
Prediction;
Interventional treatment
- From:
Chinese Journal of Anesthesiology
2024;44(4):442-446
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To identify the risk factors for poor prognosis following interventional treatment in the patients with postherpetic neuralgia (PHN) and construct a predictive model.Methods:The medical records from patients with PHN undergoing interventional therapy at the First Affiliated Hospital of Soochow University from March 2020 to August 2023 were retrospectively collected, including basic characteristics, past medical and surgical history, symptoms, medication therapy, clinical pain score, neutrophil/lymphocyte ratio (NLR) before interventional treatment and interventional treatment methods. Logistic regression analysis was used to identify the risk factors associated with poor prognosis following interventional treatment in PHN patients, and a nomogram predictive model for poor prognosis was constructed. The discrimination and calibration of the nomogram predictive model were evaluated using the C-index and Hosmer-Lemeshow test. Calibration curves and clinical decision curves were drawn to further verify the accuracy of the predictive model.Results:The results of the multivariate logistic regression analysis show that increasing age, prolonged disease duration, elevated NLR, use of immunosuppressants and use of pulsed radiofrequency were independent risk factors for poor prognosis following intervention treatment in PHN patients ( P<0.05). The nomogram predictive model for poor prognosis following PHN interventional treatment constructed based on these factors had a C-index of 0.844. Calibration curves showed good consistency between predicted probability of poor prognosis and actual incidence of poor prognosis. Clinical decision curves indicated that the predictive model provided good accuracy and net benefit. Conclusions:Increasing age, prolonged disease course, elevated NLR, use of immunosuppressants and use of pulsed radiofrequency are independent risk factors for poor prognosis following interventional treatment in the patients with PHN. The nomogram predictive model based on these factors can effectively predict the occurrence of poor prognosis in PHN patients undergoing interventional treatment.
