Risk factors and construction of a prediction model for severe pain in patients with postherpetic neuralgia
10.3969/j.issn.1004-1648.2025.05.015
- VernacularTitle:带状疱疹后神经痛患者发生重度疼痛的危险因素及预测模型的构建
- Author:
Hui LUO
1
;
Shasha ZHANG
Author Information
1. 629000 遂宁市中心医院皮肤科
- Publication Type:Journal Article
- Keywords:
postherpetic neuralgia;
severe pain;
prediction model
- From:
Journal of Clinical Neurology
2025;38(5):374-379
- CountryChina
- Language:Chinese
-
Abstract:
Objective To analyze the risk factors for severe pain in patients with postherpetic neuralgia(PHN),then construct a Nomogram prediction model,and validate it.Methods A total of 480 PHN patients treated in our hospital from December 2023 to December 2024 were selected and divided into a modeling set(n=240)and a validation set(n=240)using a random number table.Two months after the onset of herpes zoster,the patients in the modeling set were further classified into a severe pain group(n=52)and a mild-to-moderate pain group(n=188)based on the degree of pain.The baseline data of the selected patients were analyzed.The predictive value of continuous variables was analyzed using the ROC test.Univariate and Logistic regression analyses were conducted to screen for risk factors for severe pain.The Nomogram prediction model was constructed using R software and tested for goodness of fit.Results Statistically significant differences were observed between the two groups in age,gender,presence of prodromal pain,presence of diabetes,skin lesion area,and initial treatment time(all P＜0.05).Logistic regression analysis revealed that gender,presence of prodromal pain,presence of diabetes,skin lesion area,and initial treatment time were independent risk factors for severe pain in PHN patients(all P＜0.05).Construct a Nomogram model with predictive indicators as gender,presence of prodromal pain,presence of diabetes,skin lesion area,and initial treatment time to predict the occurrence of severe pain in PHN patients.The Nomogram model demonstrated good predictive ability and accuracy,with a C-index of 0.974(0.957-0.992)and the Hosmer-Lemeshow goodness-of-fit testx2of 3.030(P=0.933).Conclusions Gender,presence of prodromal pain,presence of diabetes,skin lesion area,and initial treatment time are all influencing factors for severe pain in PHN patients.The constructed model exhibits good predictive ability and accuracy.
