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Updated: Aug 6, 2026
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Development and External Validation of a Nomogram for Predicting Postherpetic Neuralgia Risk in Patients with Herpes
Lulu Yan1, Renyuan Deng2, Hongguang Lu1
1Department of Dermatology, Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, People's Republic of China.
Background:
Postherpetic neuralgia (PHN) is a common and often debilitating complication of herpes zoster (HZ), particularly among older adults and immunocompromised individuals. Characterized by persistent neuropathic pain, PHN is associated with substantial impairment in quality of life and increased healthcare burden. Early identification of patients at high risk for PHN remains challenging in clinical practice.
Methods:
This retrospective cohort study included 516 patients with acute HZ from the Affiliated Hospital of Guizhou Medical University between October 2018 and September 2021. Patients were randomly divided into a development cohort (n=362) and an internal validation cohort (n=154), while an independent external validation cohort (n=200) was obtained from another tertiary center. Candidate variables were evaluated using univariable analysis and multivariable logistic regression with backward stepwise selection. A nomogram was developed based on the final model. Model performance was assessed using AUC, calibration plots with Spiegelhalter's Z-test, DCA, and CIC.
Results:
PHN occurred in 28.7% of patients in the development cohort. Five predictors were retained in the final model. The nomogram demonstrated acceptable discrimination, with AUCs of 0.758 (95% CI: 0.701-0.815) and 0.746 (95% CI: 0.652-0.841) in the development and external validation cohorts, respectively. Calibration analysis demonstrated satisfactory agreement between predicted and observed outcomes (p=0.761 and p=0.933, respectively). DCA demonstrated a net clinical benefit across threshold probabilities ranging from 18% to 80%, while CIC analysis demonstrated reasonable agreement between predicted high-risk individuals and observed PHN cases across clinically relevant threshold probabilities.
Conclusion:
A nomogram for predicting PHN risk was developed and externally validated using readily available clinical variables. The model demonstrated acceptable discrimination, satisfactory calibration, and potential clinical utility, which may support early risk stratification in patients with HZ.