Predicting Postherpetic Neuralgia in Patients with Herpes Zoster by Machine Learning: A Retrospective Study
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ORIGINAL RESEARCH
Predicting Postherpetic Neuralgia in Patients with Herpes Zoster by Machine Learning: A Retrospective Study Xin-Xing Wang . Yi Zhang . Bi-Fa Fan
Received: July 21, 2020 The Author(s) 2020
ABSTRACT Introduction: Postherpetic neuralgia (PHN) is a neuropathic pain secondary to shingles. Studies have shown that early pain intervention can reduce the incidence or intensity of PHN. The aim of this study was to predict whether a patient with acute herpetic neuralgia will develop PHN and to help clinicians make better decisions. Method: Five hundred two patients with shingles were reviewed and classified according to whether they had PHN. The risk factors associated with PHN were determined by univariate analysis. Logistic regression and random forest algorithms were used to do machine learning, and then the prediction accuracies of the two algorithms were compared, choosing the superior one to predict the next 60 new cases.
Digital Features To view digital features for this article go to https://doi.org/10.6084/m9.figshare.12866024. X.-X. Wang Graduate School of Beijing University of Chinese Medicine, Beijing 100029, People’s Republic of China X.-X. Wang Y. Zhang B.-F. Fan (&) Department of Pain, China-Japan Friendship Hospital, Beijing 100029, People’s Republic of China e-mail: [email protected]
Results: Age, NRS score, rash site, Charlson comorbidity index (CCI) score, antiviral therapy and immunosuppression were found related to the occurrence of PHN. The NRS score was the most closely related factor with an importance of 0.31. As for accuracy, the random forest was 96.24%, better than that of logistic regression in which the accuracy was 92.83%. Then, the random forest model was used to predict 60 newly diagnosed patients with herpes zoster, and the accuracy rate was 88.33% with a 95% confidence interval (CI) of 77.43–95.18%. Conclusions: This study provides an idea and a method in which, by analyzing the data of previous cases, we can develop a predictive model to predict whether patients with shingles will develop PHN. Keywords: Herpes zoster; Logistic regression machine learning; Postherpetic neuralgia; Probability; Random forest
Pain Ther
Key Summary Points Postherpetic neuralgia (PHN) is a kind of intractable pain. Studies have shown that early pain intervention can reduce the incidence and severity of PHN. There are clear risk factors associated with PHN. Can we predict the probability of PHN in a patient with shingles by analyzing risk factors? A statistical model for predicting PHN was obtained through machine learning by logistic regression and random forest analysis. For patients at high risk of PHN, we can advise them to undergo pain intervention as soon as possible.
DIGITAL FEATURES This article is published with digital features to facilitate understanding of the article. To view digital features for this article go to https://doi. org/10.6084/m9.figshare.12866024.
INTRODUCTION Postherpetic neuralgia (PHN) is a kind of neuropathic pain secondary to herpes zoster infection.
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