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A Multicenter Prospective Study to Develop a Prediction Model for Postherpetic Neuralgia Using Clinical and
Jiaqi Wang1,2, Yanbing Yao3, Honggeng Wang4
1The Clinical Laboratory Center, The Second Affiliated Hospital of Fujian Medical University, No. 34 Zhongshan North Road, Licheng District, Quanzhou, 362000, Fujian, China.
Pain and Therapy
|April 22, 2026
Summary
A new logistic regression model effectively predicts postherpetic neuralgia (PHN) risk in herpes zoster (HZ) patients using routine clinical and lab data. This tool aids early identification and prevention of PHN complications.
Area of Science:
- Neurology
- Infectious Diseases
- Biostatistics
Background:
- Postherpetic neuralgia (PHN) is a debilitating complication of herpes zoster (HZ), causing significant patient suffering and economic burden.
- Current PHN risk assessment relies on subjective clinical features, lacking objective quantitative tools for early identification.
- Timely identification of high-risk patients is crucial for initiating preventive interventions against PHN.
Purpose of the Study:
- To develop and validate an efficient, objective, and clinically accessible prediction model for PHN risk.
- To integrate routine clinical and laboratory indicators for enhanced PHN risk prediction.
- To provide a practical tool for early PHN risk stratification in acute herpes zoster patients.
Main Methods:
- Prospective, multicenter study enrolling 722 patients with acute HZ.
- Collection of clinical data and centralized laboratory testing for multiple indicators.
- Univariate/multivariate logistic regression and machine learning algorithms to identify predictors and build a predictive model, validated on an independent test set.
Main Results:
- The incidence of PHN was 18.84% in the study cohort.
- Seven independent predictors for PHN were identified: age, disease duration, prodromal pain, VZV-IgM, RDW-CV, urea, and serum sodium.
- The logistic regression model achieved an AUC of 0.733 and accuracy of 0.765, demonstrating good predictive performance.
Conclusions:
- This study reports the first PHN incidence data for HZ patients in Quanzhou.
- A validated logistic regression model integrating clinical and laboratory data accurately predicts PHN risk.
- The developed model serves as a practical clinical tool for early PHN risk identification and guiding preventive strategies.
