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Published on: January 27, 2010
Risk prediction models for postherpetic neuralgia: a systematic review and meta-analysis
Qian Li1,2, Hui Li1,2, Zhejin Yuan1,2
1Department of Pain Management, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Frontiers in Neurology
|July 29, 2026
Summary
This systematic review found that current risk prediction models for postherpetic neuralgia (PHN) show good predictive performance, with a pooled AUC of 0.86. However, research quality is limited, necessitating development of higher-quality models.
Area of Science:
- Medical Informatics
- Epidemiology
- Biostatistics
Background:
- Postherpetic neuralgia (PHN) is a common complication of herpes zoster.
- Accurate risk prediction models are crucial for identifying individuals at high risk of developing PHN.
- Existing models require systematic evaluation to guide future development.
Purpose of the Study:
- To conduct a systematic review and meta-analysis of risk prediction models for PHN.
- To assess the quality and performance of existing PHN risk prediction models.
- To provide a reference for developing higher-quality PHN risk prediction models in China.
Main Methods:
- Systematic literature search across multiple databases (CNKI, Wanfang, VIP, CBM, PubMed, Web of Science, Embase, Cochrane Library) up to March 1, 2026.
- Independent screening and data extraction by two researchers.
- Risk of bias and applicability assessment using the Prediction Model Risk of Bias Assessment Tool (PROBAST).
- Meta-analysis of Area Under the Curve (AUC) values and predictive factors using R 4.5.1 software.
Main Results:
- 25 studies were included, with sample sizes ranging from 90 to 8,878 cases.
- Models demonstrated good predictive performance, with a pooled AUC of 0.86 (95% CI: 0.82-0.90).
- Commonly identified predictive factors included Age, VAS, rash site, prodromal pain, and extent of rash.
Conclusions:
- Research on PHN risk prediction models is in its early stages, characterized by a high risk of bias and limited clinical application.
- Future efforts should focus on developing high-quality models with strong generalizability using machine learning and multicenter prospective studies.
