Analysis of risk factors for venous thromboembolism in hospitalized patients with acute herpetic neuralgia and construction of a predictive nomogram model
- 1Pain Department, Hengshui People's Hospital
Abstract
Objectives: To explore the risk factors for venous thromboembolism (VTE) in hospitalized patients with acute herpetic neuralgia (AHN) and to construct a VTE prediction model based on clinical indicators to help clinicians better identify high-risk patients and develop prevention and treatment strategies.
Materials and methods: This study retrospectively included 327 patients diagnosed with AHN who were hospitalized in the Pain Department of Hengshui People's Hospital between December 2020 and December 2023, of whom 26 developed VTE during hospitalization. The basic clinical data of the patients were collected, and the independent risk factors for VTE were screened by LASSO regression and multifactorial Logistic regression analysis, and the VTE prediction model was constructed. Model performance was evaluated by ROC curves, calibration curves, and decision curves, and internal validation was performed using ten-fold cross-validation and bootstrap resampling.
Results: The incidence of VTE in AHN patients was 7.95%. LASSO regression screened for 10 relevant variables, and final multifactor logistic regression analysis identified FIB (fibrinogen), FDP (fibrin degradation products), NRS (Numerical Rating Scale) score, and thrombotic risk score as independent predictors. Among them, FIB was a protective factor for VTE (OR = 0.477, P = 0.006), while FDP (OR = 1.48, P < 0.001), NRS score (OR = 1.584, P = 0.039), and thrombotic risk score (OR = 2.05, P < 0.001) were risk factors. The AUC of the prediction model was 0.853 (95% CI: 0.764–0.941), and the bootstrap optimism-corrected C-index was 0.834. At the optimal cutoff, the sensitivity and specificity were 0.731 and 0.877, respectively, with good calibration and clinical applicability.
Conclusions: In this study, a VTE prediction model was constructed for patients with AHN. The model can accurately predict the risk of VTE in patients with AHN and provide a reference for clinical prevention and intervention; however, external validation in larger, multicenter cohorts is warranted before clinical implementation.
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BibTeX
@article{wang2026analysis,
title = {{Analysis of risk factors for venous thromboembolism in hospitalized patients with acute herpetic neuralgia and construction of a predictive nomogram model}},
author = {Chunman Wang and Guiling Wang and Bing Liu and Qian Gao and Chuansui Du and Lin Li},
journal = {Frontiers in Neurology},
year = {2026},
month = oct,
doi = {10.3389/fneur.2026.1877838},
url = {https://www.frontiersin.org/articles/10.3389/fneur.2026.1877838/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Analysis of risk factors for venous thromboembolism in hospitalized patients with acute herpetic neuralgia and construction of a predictive nomogram model
AU - Chunman Wang
AU - Guiling Wang
AU - Bing Liu
AU - Qian Gao
AU - Chuansui Du
AU - Lin Li
PY - 2026
DA - 2026/10/20
JO - Frontiers in Neurology
DO - 10.3389/fneur.2026.1877838
UR - https://www.frontiersin.org/articles/10.3389/fneur.2026.1877838/full
AB - Objectives: To explore the risk factors for venous thromboembolism (VTE) in hospitalized patients with acute herpetic neuralgia (AHN) and to construct a VTE prediction model based on clinical indicators to help clinicians better identify high-risk patients and develop prevention and treatment strategies. Materials and methods: This study retrospectively included 327 patients diagnosed with AHN who were hospitalized in the Pain Department of Hengshui People's Hospital between December 2020 and December 2023, of whom 26 developed VTE during hospitalization. The basic clinical data of the patients were collected, and the independent risk factors for VTE were screened by LASSO regression and multifactorial Logistic regression analysis, and the VTE prediction model was constructed. Model performance was evaluated by ROC curves, calibration curves, and decision curves, and internal validation was performed using ten-fold cross-validation and bootstrap resampling. Results: The incidence of VTE in AHN patients was 7.95%. LASSO regression screened for 10 relevant variables, and final multifactor logistic regression analysis identified FIB (fibrinogen), FDP (fibrin degradation products), NRS (Numerical Rating Scale) score, and thrombotic risk score as independent predictors. Among them, FIB was a protective factor for VTE (OR = 0.477, P = 0.006), while FDP (OR = 1.48, P < 0.001), NRS score (OR = 1.584, P = 0.039), and thrombotic risk score (OR = 2.05, P < 0.001) were risk factors. The AUC of the prediction model was 0.853 (95% CI: 0.764–0.941), and the bootstrap optimism-corrected C-index was 0.834. At the optimal cutoff, the sensitivity and specificity were 0.731 and 0.877, respectively, with good calibration and clinical applicability. Conclusions: In this study, a VTE prediction model was constructed for patients with AHN. The model can accurately predict the risk of VTE in patients with AHN and provide a reference for clinical prevention and intervention; however, external validation in larger, multicenter cohorts is warranted before clinical implementation.
SN - 1664-2295
ER -