Construction of influencing factors and nomogram prediction model for post-herpetic neuralgia based on T cell function and inflammatory factors.

OBJECTIVE

To explore the feasibility and clinical value of establishing a prediction model of post-herpetic neuralgia (PHN) based on T cell functional indicators (CD4+/CD8+ ratio, Treg cell ratio) and inflammatory factors (IL-6, TNF-, and IL-10).

METHODS

A total of 260 patients with herpes zoster who were admitted to our hospital from June 2022 to November 2024 were included in the study. The 7:3 score was used as the training set ( = 182) and verification set ( = 78). The clinical data were collected and the peripheral blood T cell subsets and inflammatory factor levels were detected. Risk factors were screened by univariate and multivariate Logistic regression, and a nomogram model was constructed for efficacy evaluation and verification.

RESULTS

The incidence of PHN in the training set was 29.67%(54/182) and the verification set was 30.77%(24/78). Multivariate regression analysis showed that age, CD4+/CD8+ ratio, Treg cell ratio, IL-6, TNF-, and IL-10 were the independent risk factors ( < 0.05). The C-index values for the nomogram models in the training and validation sets were 0.804 and 0.789, respectively, the AUC values were 0.802 (95% CI: 0.722-0.882) and 0.790 (95% CI: 0.642-0.938), and the sensitivity and specificity values were 0.634, 0.875, and 0.462 and 0.875, respectively. The calibration curve showed good agreement between the predicted and actual values with mean absolute errors of 0.164 and 0.146, respectively, which was good by the Hosmer-Lemeshow test.

CONCLUSION

The nomogram model based on T cell function and inflammatory factors can effectively predict the risk of PHN and provide the basis for early clinical intervention.