# A scoping review of models for predicting the risk of postherpetic neuralgia

## TL;DR

This scoping review summarizes risk prediction models for postherpetic neuralgia, highlighting their performance and areas needing improvement.

## Contribution

The study provides a comprehensive overview of existing PHN risk models and identifies gaps in validation and model optimization.

## Key findings

- 23 studies were included, with PHN prevalence ranging from 6.20 to 48.00%.
- Age, rash area, and pain severity score were the most common predictive factors.
- Most models used logistic regression, and many lacked validation or external testing.

## Abstract

To conduct a scoping review of risk prediction models for postherpetic neuralgia (PHN), providing insights for clinical identification of patients at high risk and future research.

China National Knowledge Infrastructure, Wanfang, VIP Database, Chinese Biomedical Literature Service System (SinoMed), PubMed, Embase, Web of Science and the Cochrane Library databases were systematically searched from database establishment to 25 October 2024, and data on the prevalence of PHN, model construction, predictors and model performance were extracted for summary analysis.

A total of 23 studies were included, with a high overall risk of bias. The prevalence of PHN ranged from 6.20 to 48.00%, with traditional logistic regression being the predominant model construction method. The three most frequently identified predictive factors were age, rash area and pain severity score. Additionally, 43.48% of the studies did not validate their models, and 52.17% used visualization methods to present their models. The area under the receiver operator characteristic curve of the studies was 0.714–0.980. Two studies performed external validation; 14 studies evaluated the model’s calibration, and the calibration curve coincided well with the actual curve; and eight studies assessed the clinical benefit.

Risk prediction models for PHN all showed good predictive performance, but the risk of bias was high, and further clinical validation is needed. In the future, research could refine variable selection and model performance evaluation to optimize predictive models continuously, aiming to develop models with excellent predictive performance and strong clinical utility.

DOl: https://doi.org/10.17605/0SF.IO/SUR2C.