Journal of Pain Research (Nov 2024)
Analysis of Risk Factors and Development and Validation of a Dynamic Nomogram for Postherpetic Neuralgia: A Retrospective Study
Abstract
Cunjin Wang,1– 4,* Xiaowei Song,1,2,* Jing Liu,1,2 Yinghao Song,1,3,4 Ju Gao1– 4 1Northern Jiangsu People’s Hospital Affiliated to Yangzhou University, Yangzhou, 225001, People’s Republic of China; 2Department of Anesthesiology, Northern Jiangsu People’s Hospital, Yangzhou, 225001, People’s Republic of China; 3Department of Pain Treatment, Northern Jiangsu People’s Hospital, Yangzhou, 225001, People’s Republic of China; 4The Yangzhou Clinical Medical College of Xuzhou Medical University, Yangzhou, 225001, People’s Republic of China*These authors contributed equally to this workCorrespondence: Cunjin Wang, Department of Anesthesiology, Northern Jiangsu People’s Hospital, No. 98 Nan Tong Western Road, Yangzhou, Jiangsu, 225001, People’s Republic of China, Tel +86- 18051061453, Email [email protected]: 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.Keywords: postherpetic neuralgia, herpes zoster, risk factors, dynamic nomogram
