Risk factors and modeling of sleep disorders in patients with alcohol withdrawal

doi:10.19854/j.cnki.1008-2425.2025.04.0004

Abstract

Abstract: Objective To analyze the risk factors of sleep disorders in alcohol withdrawal patients,and establish a risk prediction model of sleep disorders in alcohol withdrawal patients by decision tree algorithm. Methods The clinical data of 166 patients with alcohol withdrawal admitted from October 2020 to September 2024 were retrospectively analyzed.According to the Pittsburgh Sleep Quality Index (PSQI),patients with alcohol withdrawal with a total score of >7 were included in the sleep disorder group,and those with alcohol withdrawal with a total score of ≤7 were included in the non-sleep disorder group.Multivariate Logistic regression analysis was used to screen the risk factors of sleep disorders in patients with alcohol withdrawal.Modeler software was used to build a decision tree model of sleep disorders in patients with alcohol withdrawal.The model was internally verified by the 5-fold cross-validation method,and the predictive efficacy of the model was compared. Results Among 166 patients with alcohol withdrawal,49 patients had concurrent sleep disorder,117 patients did not have concurrent sleep disorder,the incidence of sleep disorder was 29.52%;Body mass index ≥24kg/m²,drinking time ≥10 years,dependent decision-making style,anxiety,depression,PACS grades 4-6 were all independent risk factors for sleep disorders in alcohol withdrawal patients (P＜0.05).The probabilistic prediction model P=1 / [1+e-^{(-2.903+1.469*X1+1.289 *X2+1.589 *X3+1.345*X4+1.125*X5+1.499*X6)}],and the overall accuracy of the model prediction is 81.7%.The prediction accuracy of the model is 73.9% after cross-validation with 5 fold.The decision tree showed that depression had the greatest effect on sleep disorder in alcohol withdrawal patients,and the information gain was 0.41.ROC results showed that the AUC values of both models were moderately effective in predicting sleep disorders in patients with alcohol withdrawal. Conclusion Body mass index,drinking time,decision style,anxiety,depression and PACS grading of alcohol abstinence patients can predict the risk of sleep disorders.The decision tree model of sleep disorders in alcohol abstinence patients constructed in this study can predict the risk of sleep disorders.

Key words: Alcohol withdrawal, Sleep disorders, Risk factors, Decision tree model

摘要： 目的分析酒精戒断患者发生睡眠障碍的危险因素,通过决策树算法建立酒精戒断患者发生睡眠障碍的风险预测模型。方法回顾性分析2020年10月~2024年9月收治的166例酒精戒断患者的临床资料,根据匹兹堡睡眠质量指数(PSQI)评估结果将总分值>7分的酒精戒断患者纳入睡眠障碍组,将≤7分的酒精戒断患者纳入非睡眠障碍组,采用多因素Logistic回归分析筛选酒精戒断患者发生睡眠障碍的危险因素,运用Modeler软件构建酒精戒断患者发生睡眠障碍的决策树模型,采用5折交叉验证法对模型进行内部验证,并对比模型的预测效能。结果 166例酒精戒断患者中,睡眠障碍组患者49例,非睡眠障碍组患者117例,睡眠障碍发生率为29.52%;身体质量指数≥24 kg/m²、饮酒时间≥10年、依赖型决策风格、有焦虑、有抑郁、PACS分级4~6级均是酒精戒断患者发生睡眠障碍的独立危险因素(P<0.05);概率预测模型P=1／[1+e-^{(-2.903+1.469*X1+1.289 *X2+1.589 *X3+1.345*X4+1.125*X5+1.499*X6)}],模型预测总体正确性为81.7%;经5折交叉验证显示,模型预测正确率为73.9%;决策树显示,抑郁对酒精戒断患者发生睡眠障碍的影响最大,信息增益为0.41;ROC结果显示,两种模型预测酒精戒断患者发生睡眠障碍的AUC值均为中等效能。结论酒精戒断患者的身体质量指数、饮酒时间、决策风格、焦虑、抑郁及PACS分级均可预测其睡眠障碍发生风险,本研究构建的酒精戒断患者发生睡眠障碍的决策树模型可预测患者睡眠障碍发生风险。

关键词: 酒精戒断, 睡眠障碍, 危险因素, 决策树模型

CLC Number:

749.6⁺2

Wu Nannan, Zhao Jing, Xiao Tingting. Risk factors and modeling of sleep disorders in patients with alcohol withdrawal[J]. Chinese Journal of Stereotactic and Functional Neurosurgery, 2025, 38(4): 213-219.

武囡囡, 赵婧, 肖婷婷. 酒精戒断患者发生睡眠障碍的危险因素及模型建立[J]. 立体定向和功能性神经外科杂志, 2025, 38(4): 213-219.

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