Analysis of treatment-related factors of postherpetic neuralgia and construction of XGBoost clinical prediction model
YANG Bo
TANG Xuemiao
SONG Futing
SHI Xiaohan
WANG Qing
XU Ya'nan
QI Jing
LYU Yan
WANG Yingfeng
GU Nan
Abstract：Objective To analyze the risk factors affecting the occurrence of postherpetic neuralgia(PHN),especially the treatment-related factors,and select the optimal machine learning algorithm to construct the PHN clinical prediction model.Methods The medical records of 434 patients with herpes zoster treated in the outpatient clinic of Pain Medicine Center of Xijing Hospital from May to October 2023 were selected.Demographic factors,herpes-related factors,treatment-related factors,and co-morbidities were collected.After 3 months of disease course,patients were divided into PHN group(n=197)and non-PHN group(n=237)according to the pain VAS.Univariate analysis and logistic regression were used to select variables,and then LASSO regression was used to screen and dimensionally reduce the selected factors to determine the final variables to be included in the model.The differentiation performance of traditional logistic regression model and two machine learning models(XGBoost and SVM)was compared,and the optimal algorithm was selected for model construction and verification and evaluation.Results LASSO regression screened ganglion segment,age,acute phase VAS,herpes area,time to start nerve block therapy,and nature of pain as independent influencing factors for PHN.The mean ROC-AUC values of logistic regression model,XGBoost model,and SVM model in the training set were 0.82,0.95,and 0.77,respectively,and those in the validation set were 0.81,0.81,and 0.76,respectively,suggesting that the XGBoost model had the best prediction performance.Using XGBoost to construct the final prediction model,the mean ROC-AUC values and 95％CI of the training set and validation set were 0.94(0.92-0.97)and 0.86(0.79-0.94),respectively,indicating a better model differentiation.Hosmer-Lemeshow goodness-of-fit test(P＞0.05)showed that the calibration curve was close to the ideal curve,indicating that the model has good prediction performance.The decision curve analysis showed that the model had an excellent net clinical benefit.Conclusion The two treatment-related factors,the affected nerve segment and the time to start nerve block therapy,are critical independent influencing factors for the occurrence of PHN.The XGBoost clinical prediction model constructed by ganglion segment,age,acute phase VAS,herpes area,time to start nerve block therapy,and nature of pain performs well with good differentiation and calibration.For early identification of PHN high-risk patients,timely targeted treatment has important clinical significance.
Keywords：postherpetic neuralgiaherpes zostertreatment-related factorsclinical prediction modelmachine learning
Publication Date：2024-04-28
Online Publishing Date：2026-08-26(First online date of this platform, not the publication date of the document)
Pages：9( 380-388 )