ArticleJournal of pain research2024
Analysis of Risk Factors and Development and Validation of a Dynamic Nomogram for Postherpetic Neuralgia: A Retrospective Study.
Article in Journal of pain research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 3 of them syntheses that pooled it.
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Who cites it
8 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Construction and validation of a prediction system for postherpetic neuralgia: based on meta-analysis.Scientific reports · 2026Pooled it
- Evaluating the efficacy of machine learning in predicting postherpetic neuralgia: a systematic review and meta-analysis.Frontiers in neurology · 2025Pooled it
- Risk prediction models for postherpetic neuralgia: a systematic review and meta-analysis.Frontiers in neurologyPooled it
- Nomogram-Based Prediction Model for Postherpetic Neuralgia in Immunosuppressive Patients.Journal of clinical medicine · 2026Article
- Prediction of Postherpetic Neuralgia in Patients with Acute and Subacute Herpetic Neuralgia Using Structural Magnetic Resonance Imaging: A Retrospective Study.Pain and therapy · 2026Article
- Development and External Validation of a Nomogram for Predicting Postherpetic Neuralgia Risk in Patients with Herpes Zoster: A Retrospective Cohort Study.Journal of pain research · 2026Article
- Risk Prediction Model for Arteriovenous Fistula Maturation Failure in Elderly Diabetic Patients: Limitations and Future Directions [Response to Letter].Diabetes, metabolic syndrome and obesity : targets and therapy · 2025Article
- Development and validation of a machine learning model for predicting postherpetic neuralgia risk.Frontiers in neurologyArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Postherpetic Neuralgia (PHN), recognized as the most common complication of Herpes Zoster, is experiencing an increasing trend in its occurrence. The goal of this study was to identify the independent risk factors for PHN and create a dynamic nomogram using routine clinical characteristics to predict PHN in patients with herpes zoster, for early identification and prevention of PHN. Patients and Methods: A total of 2420 patients were retrospectively reviewed and divided into training (n=1696) and validation (n=724) cohort using a 7:3 random allocation. Univariable, LASSO and multivariable logistic regression analysis was performed to identified independent risk factors for PHN. A dynamic nomogram was assessed through the area under the receiver operating characteristic curve (AUC), calibration curves and Hosmer-Lemeshow test. The decision curve analysis (DCA) was used to evaluate its clinical validity. Results: Multivariable logistic regression identified several independent risk factors for PHN, including age, female, diabetes mellitus, malignant tumors, and connective tissue diseases. The area under the curve was 0.698 (95% CI, 0.666-0.730) for training cohort and 0.713 (95% CI, 0.663-0.763) for the validation cohort. Calibration curve revealed a moderate consistency between actual observation and prediction. Decision curve analysis showed a risk threshold of 16% and demonstrated a clinically effective predictive model. Conclusion: We have developed a user-friendly dynamic nomogram to predict PHN in patients with herpes zoster, which can assist in early identification and prevention of PHN.
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