Risk prediction models for postherpetic neuralgia: a systematic review and meta-analysis
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By
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Qian Li
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Hui Li
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Zhejin Yuan
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Dongmei Yuan
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July 14, 2026
Clinical Scorecard: Systematic Review and Meta-Analysis of Risk Assessment Models for Postherpetic Neuralgia
At a Glance
| Category | Detail |
|---|
| Condition | Postherpetic Neuralgia (PHN) |
| Key Mechanisms | Involves sensitization of peripheral neurons and central nervous system due to varicella-zoster virus activation. |
| Target Population | Patients with herpes zoster, particularly older individuals. |
| Care Setting | Clinical settings focusing on pain management and risk assessment. |
Key Highlights
- A total of 25 studies included with sample sizes from 90 to 8,878 cases.
- Pooled AUC for predictive models was 0.86, indicating good predictive performance.
- Common predictive factors include age, VAS, rash site, prodromal pain, and extent of rash.
- High risk of bias noted in included studies, with good applicability.
- Need for high-quality risk prediction models based on machine learning and large-sample studies.
Guideline-Based Recommendations
Diagnosis
- Utilize risk prediction models to assess likelihood of PHN in patients with herpes zoster.
Management
- Implement early interventions for high-risk populations to reduce PHN incidence.
Monitoring & Follow-up
- Regularly evaluate patients for the development of PHN symptoms post-herpes zoster.
Risks
- Patients with severe acute pain, extensive rash, and comorbidities are at increased risk for PHN.
Patient & Prescribing Data
Individuals diagnosed with herpes zoster.
Current treatments include antiviral drugs and neuromodulatory agents, though pain relief is often inadequate.
Clinical Best Practices
- Conduct comprehensive evaluations of risk factors for PHN.
- Integrate multiple variables in risk prediction models for improved accuracy.
- Encourage early identification and intervention in high-risk patients.
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