To conduct a systematic review and meta-analysis of risk prediction models for postherpetic neuralgia (PHN) to provide a reference for developing higher-quality models.
Approach:
Literature Search: Systematic search of multiple databases including CNKI, Wanfang Data, VIP, CBM, PubMed, Web of Science, Embase, and Cochrane Library for studies on PHN risk prediction models.
Data Extraction: Two researchers independently screened literature and extracted relevant information.
Bias Assessment: Used the Prediction Model Risk of Bias Assessment Tool (PROBAST) to assess risk of bias and applicability.
Meta-Analysis: Performed meta-analyses of area under the curve (AUC) values and predictive factors using R 4.5.1 software.
Key Findings:
25 studies included, with sample sizes from 90 to 8,878 and PHN incidence rates from 6.2% to 52.9%.
18 studies performed internal validation; 4 studies performed external validation.
Pooled AUC was 0.86 (0.82–0.90), indicating good predictive performance.
Common predictive factors included Age, VAS, rash site, Prodromal pain, and Extent of Rash.
Interpretation:
Research on risk prediction models for PHN is at an early stage, with high risk of bias and limited clinical application.
Limitations:
Overall high risk of bias in included studies.
Lack of clinical applicability of existing models.
Conclusion:
Future development of high-quality risk prediction models with high accuracy and strong generalizability is needed, potentially utilizing machine learning and multicenter, large-sample prospective studies.
