Early Diagnosis of Herpes Zoster Neuralgia: A Narrative Review

Clinical efficacy of high-voltage pulsed radiofrequency combined with stellate ganglion block in the acute phase of thoracic and dorsal herpes zoster neuralgia under dual guidance of ultrasound and C-arm

OBJECTIVES

A single therapeutic approach is not always successful in the treatment of herpes zoster neuralgia, and the appropriate combination of different treatments deserves further exploration. In this study, we investigated the clinical efficacy of high-voltage long-duration pulsed radiofrequency (PRF) combined with stellate ganglion block (SGB) in the acute phase of thoracic and dorsal herpes zoster neuralgia under dual guidance of ultrasound and C-arm.

METHODS

79 cases of acute zoster neuralgia were grouped premised upon differing therapeutic approaches: standard voltage PRF (group S, the temperature, duration, pulse width, frequency and voltage were set to 42 °C, 300 s, 20 ms, 2 Hz, and 45 V), high-voltage long-duration PRF (group H, parameters of PRF were set to 42 °C, 900 s, 20 ms, 2 Hz, and 90 V, respectively), and high-voltage long-duration PRF combined with SGB (group C, parameter settings for PRF are the same as those for group H). The therapeutic outcomes were assessed utilizing the numeric rating scale (NRS), Pittsburgh sleep quality index (PSQI), and Hamilton anxiety rating scale (HAMA). The incidence of clinically significant postherpetic neuralgia post-treatment had been documented.

RESULTS

Compared to baseline, scores of NRS, PSQI, and HAMA at each time point post-treatment decreased across all groups, and the decrease was more significant in the C group than in the S group. At the later stage of treatment, the consumption of pregabalin and tramadol and the plasma levels of interleukin-6 and galectin-3 in the C group were significantly lower than those in the S group. The incidence of PHN in the C group was significantly lower than in the S group.

CONCLUSIONS

The combination of high-voltage long-duration PRF combined with SGB under dual guidance of ultrasound and C-arm represents a safe, effective, environmentally friendly, and cost-efficient method for treating AZN, significantly improving sleep quality, alleviating anxiety, and reducing the risk of PHN occurrence.

Development of a Prediction Model and Corresponding Scoring Table for Postherpetic Neuralgia Using Six Machine Learning Algorithms: A Retrospective Study

INTRODUCTION

Postherpetic neuralgia (PHN), a complication of herpes zoster, significantly impacts the quality of life of affected patients. Research indicates that early intervention for pain can reduce the occurrence or severity of PHN. This study aims to develop a predictive model and scoring table to identify patients at risk of developing PHN following acute herpetic neuralgia, facilitating informed clinical decision-making.

METHODS

We conducted a retrospective review of 524 hospitalized patients with herpes zoster at The First Affiliated Hospital of Zhejiang Chinese Medical University from December 2020 to December 2023 and classified them according to whether they had PHN, collecting a comprehensive set of 30 patient characteristics and disease-related indicators, 5 comorbidity indicators, 2 disease score values, and 10 serological indicators. Relevant features associated with PHN were identified using the least absolute shrinkage and selection operator (LASSO). Then, the patients were divided into a training set and a test set in a 4:1 ratio, with comparability tested using univariate analysis. Six models were established in the training set using machine learning methods: support vector machines, logistic regression, random forest, k-nearest neighbor, gradient boosting, and neural network. The performance of these models was evaluated in the test set, and a nomogram based on logistic regression was used to create a PHN prediction score table.

RESULTS

Eight non-zero characteristic variables selected from the LASSO regression results were included in the model, including age [area under the curve (AUC) = 0.812, p < 0.001], Numerical Rating Scale (NRS) (AUC = 0.792, p < 0.001), receiving treatment time (AUC = 0.612, p < 0.001), rash recovery time (AUC = 0.680, p < 0.001), history of malignant tumor (AUC = 0.539, p < 0.001), history of diabetes (AUC = 0.638, p < 0.001), varicella-zoster virus immunoglobulin M (AUC = 0.620, p < 0.001), and serum nerve-specific enolase (AUC = 0.659, p < 0,001). The gradient boosting model outperformed other classifier models on the test set with an AUC of 0.931, 95% confidence interval (CI) (0.882-0.980), accuracy of 0.886 (95% CI 0.809-0.940). In the test set, our predictive scoring table achieved an AUC of 0.820 (95% CI 0.869-0.970) with accuracy of 0.790 (95% CI 0.700-0.864).

CONCLUSION

This study presents a methodology for predicting the development of postherpetic neuralgia in shingles patients by analyzing historical case data, employing various machine learning techniques, and selecting the optimal model through comparative analysis. In addition, a logistic regression model has been used to create a scoring table for predicting the postherpetic neuralgia.