• Hui-Min Hu, Peng Mao, Xing Liu, Yuan-Jing Zhang, Chen Li, Yi Zhang, Yi-Fan Li, and Bi-Fa Fan.
• Graduate School of Beijing University of Chinese Medicine, Beijing, People's Republic of China.
• Pain Physician. 2024 Nov 1; 27 (8): E843E850E843-E850.

BackgroundHerpes zoster (HZ) and postherpetic neuralgia (PHN) have a negative effect on patients. A simple and practical PHN prediction model is lacking.ObjectiveWe aimed to investigate risk factors associated with PHN in patients with HZ and develop a predictive model.Study DesignA prospective observational study.SettingThis study was conducted at the Department of Pain Management, China-Japan Friendship Hospital in Beijing, People's Republic of China, spanning from August 2020 through March 2022.MethodsClinical data of 174 patients with HZ were recorded using a case report form. The patients underwent a 3-month follow-up, which included both in-person visits and telephone follow-ups. Patients were categorized into either a PHN or non-PHN group based on the diagnosis  of PHN. Multiple logistic regression analysis was used to identify the predictors of PHN occuring in patients with HZ. Subsequently, a nomogram model was developed to estimate the likelihood of PHN. To validate the prediction model's accuracy, calibration curves, the C-index, and receiver operating characteristic (ROC) curves were utilized.ResultsIn this study, a total of 174 patients were divided into 2 groups: the PHN Group, consisting of 52 patients, and the non-PHN Group, consisting of 122 patients based on the follow-up results. Multiple logistic regression analysis revealed 5 significant risk factors for PHN, including being a woman, being more than 50 years old, having prodromal phase pain, having a large rash area, and having great pain severity during the acute phase. The model's performance was excellent, with an area under the ROC curve of 0.81 and a close alignment between the calibration curve and the actual data, signifying high accuracy. The model's accuracy and net benefit were maximized when predicting a prevalence between 6% and 92%.LimitationsOur study was conducted at a single center and had a limited sample size.ConclusionsThe incidence of PHN is influenced by factors such as being a woman, being more than 50 years old, having prodromal phase pain, having a large rash area, and having great pain severity during the acute stage. The prediction model developed in this study effectively forecasts the occurrence of PHN using these 5 risk factors, making it a valuable tool for clinical practice.

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