Development and Validation of a Novel Clinical Prediction Model for Postherpetic Neuralgia: Integrating Inflammatory and Coagulation Biomarkers.

Abstract

Postherpetic neuralgia (PHN) is the most common and debilitating complication of herpes zoster, yet predicting PHN occuring remains challenging. Our study aimed to develop and validate a clinical prediction model for PHN based on key demographic, serological, and clinical parameters. Retrospective cohort study. This study included 202 patients with herpes zoster treated at the China-Japan Friendship Hospital from December 2023 through November 2024. Eligible patients were classified into PHN (n = 89) and non-PHN (n = 113) groups. Clinical and laboratory data were extracted from electronic medical records. Univariate logistic regression (P < 0.20) identified candidate predictors, which were further refined using least absolute shrinkage and selection operator regression. A multivariate logistic regression model was constructed, and a predictive nomogram was developed. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis. Four independent predictors were identified: age ≥ 60 years (odds ratio [OR] = 8.45; 95% CI. 3.94-18.12; P < 0.001), higher pain intensity measured by the Numeric Rating Scale (OR = 1.31; 95% CI, 1.07-1.61; P = 0.009), elevated D-dimer levels (D-dimer > 0.5 mg/L) (OR = 2.19; 95% CI, 1.10-4.36; P = 0.026), and lower neutrophil-to-lymphocyte ratio (≤ 3) (OR = 0.22; 95% CI, 0.10-0.49; P < 0.001). The final model demonstrated robust predictive accuracy (AUC = 0.83; 95% CI, 0.78-0.89) and good calibration (Hosmer-Lemeshow test, P = 0.326). A decision curve analysis confirmed the model's clinical utility within a risk threshold range of 8%-85%. Our study is limited by its single-center design and small sample size. Our study developed a reliable and clinically applicable nomogram for PHN prediction in patients with herpes zoster. The model incorporates age, pain intensity, D-dimer level, and neutrophil-to-lymphocyte ratio,, enabling early risk stratification and optimized patient management. Future prospective studies are warranted to validate this model's utility across diverse populations.