支持向量机与多项逻辑回归在预测带状疱疹后神经痛中的比较：门诊带状疱疹患者。

Support Vector Machine versus Multiple Logistic Regression for Prediction of Postherpetic Neuralgia in Outpatients with Herpes Zoster.

机构信息

Department of Anesthesiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.

Department of Anesthesiology, Beijing Ditan Hospital, Capital Medical University, Beijing, China.

出版信息

Pain Physician. 2022 May;25(3):E481-E488.

PMID:35652777

Abstract

BACKGROUND

Postherpetic neuralgia (PHN), as the most common complication of herpes zoster (HZ), is very refractory to current therapies. Studies of HZ have indicated that early aggressive pain interventions can effectively prevent PHN; therefore, accurately predicting PHN in outpatients with HZ and treating HZ promptly, would be of great benefit to patients. Multiple logistic regression (MLR) has often been used to predict PHN. However, support vector machine (SVM) has been poorly studied in predicting PHN in outpatients with HZ.

OBJECTIVE

The aim of our retrospective study was to analyze the data of outpatients with HZ to evaluate the use of SVM for predicting PHN by comparing it with MLR.

STUDY DESIGN

A retrospective study.

SETTING

Department of Anesthesiology in China.

METHODS

The data of 732 outpatients with HZ from January 1, 2015 to May 31, 2020 were reviewed. Risk factors for having PHN in outpatients with HZ were screened using least absolute shrinkage and selection operator (LASSO) algorithm. Then, SVM and MLR were used to predict PHN in outpatients with HZ based on screened risk factors. The data from 600 patients were used for training set and another 132 patients for test set. The receiver operating characteristic (ROC) curve was drawn from the 132 test set of patients. The prediction accuracy of the models was assessed using the area under curve (AUC).

RESULTS

The incidence of having PHN in outpatients with HZ was 19.4%. The risk factors selected by LASSO algorithm were gender, age, VAS scores, skin lesion area, initial treatment time, anxiety, sites of HZ (multiple skin lesions), types of HZ (bullous) and types of pain (knife cutting). The AUC for the SVM and MLR in test set were 0.884 versus 0.853. According to the ROC curve, the specificity and the sensitivity were 0.879 and 0.840 for SVM, and 0.780 and 0.840 for MLR, respectively.

LIMITATIONS

Retrospective study and relatively small sample size.

CONCLUSIONS

Both SVM and MLR had good discriminative power, but SVM has better performance in predicting PHN in outpatients with HZ, regarding the prediction accuracy and specificity.

摘要

背景

带状疱疹后神经痛（PHN）是带状疱疹（HZ）最常见的并发症，对现有治疗方法具有很强的抗药性。HZ 的研究表明，早期积极的疼痛干预可以有效地预防 PHN；因此，准确预测门诊 HZ 患者的 PHN 并及时治疗 HZ，将对患者大有裨益。多变量逻辑回归（MLR）常用于预测 PHN。然而，支持向量机（SVM）在预测门诊 HZ 患者的 PHN 方面研究甚少。

目的

本回顾性研究旨在分析 HZ 门诊患者的数据，通过与 MLR 比较，评估 SVM 用于预测 PHN 的效果。

研究设计

回顾性研究。

地点

中国麻醉科。

方法

回顾 2015 年 1 月 1 日至 2020 年 5 月 31 日期间 732 例 HZ 门诊患者的数据。使用最小绝对值收缩和选择算子（LASSO）算法筛选 HZ 门诊患者发生 PHN 的危险因素。然后，基于筛选出的危险因素，使用 SVM 和 MLR 预测 HZ 门诊患者的 PHN。其中 600 例患者的数据用于训练集，132 例患者的数据用于测试集。从 132 例测试患者的数据中绘制受试者工作特征（ROC）曲线。通过曲线下面积（AUC）评估模型的预测准确性。

结果

HZ 门诊患者 PHN 的发生率为 19.4%。LASSO 算法筛选出的危险因素包括性别、年龄、VAS 评分、皮损面积、初始治疗时间、焦虑、HZ 部位（多发皮损）、HZ 类型（水疱型）和疼痛类型（刀割样）。测试集中 SVM 和 MLR 的 AUC 分别为 0.884 和 0.853。根据 ROC 曲线，SVM 的特异性和灵敏度分别为 0.879 和 0.840，MLR 的特异性和灵敏度分别为 0.780 和 0.840。