【作者】 赵峰；

【导师】 刘恒；

【作者基本信息】 青岛大学 ， 护理（专业学位）， 2025， 硕士

【摘要】 目的1.本研究旨在调查帕金森病患者发生睡眠障碍的现状,探讨可能造成其睡眠障碍的影响因素。2.本研究旨在构建帕金森病睡眠障碍临床预测模型,并对模型进行评价和验证,为提高帕金森病患者睡眠质量提供参考依据。方法第一阶段本研究通过文献回顾法编制《帕金森病睡眠障碍潜在影响因素提取表》初稿,之后根据专家会议的结果形成终稿,从而拟定帕金森病睡眠障碍潜在影响因素。第二阶段本研究将采用便利抽样法,于2023年11月—2024年06月对就诊于青岛市某三级甲等医院神经内、外科的230名帕金森病患者进行横断面调查,并按照7:3的比例将研究对象划分为训练集和测试集。使用一般资料调查表、帕金森病睡眠量表-2、帕金森病疾病分期、统一帕金森病评定量表-Ⅲ、汉密尔顿焦虑量表、汉密尔顿抑郁量表-17、改良国王帕金森病疼痛量表中文版、蒙特利尔认知评估量表和疲劳严重度量表对帕金森病患者睡眠现状开展调查并分析相关影响因素。本研究采用SPSS 26.0和R Studio 4.4.2对数据进行统计学分析。首先,通过单因素分析初步筛选出帕金森病睡眠障碍相关影响因素中有意义的变量,以P<0.05代表差异具有统计学意义。随后采用Logistic回归分析和决策树探讨帕金森病睡眠障碍的独立影响因素,并构建帕金森病睡眠障碍预测模型。最后通过区分度、敏感度、特异度和临床决策曲线等指标对模型进行评价,并利用测试集数据对模型进行内部验证。结果1.经文献回顾和专家会议后,拟定的帕金森病睡眠障碍潜在影响因素包括性别、年龄、BMI、起病年龄、病程、婚姻状况、居住地、居住状态、文化程度、家庭月收入、医保类型、疾病分期、吸烟史、饮酒史、运动障碍、认知功能、左旋多巴每日等效剂量、焦虑、抑郁、疼痛和疲劳。2.本研究共纳入帕金森病患者230人,其中发生睡眠障碍者共有145人,未发生睡眠障碍者共有85人,帕金森病睡眠障碍患病率为63.04%。3.帕金森病睡眠障碍组和非睡眠障碍组单因素分析结果中差异具有统计学意义的变量为:性别、认知障碍、焦虑、抑郁、疼痛、疾病分期、病程和左旋多巴等效日剂量。4.Logistic回归预测模型显示帕金森病患者睡眠障碍的独立影响因素是焦虑、抑郁、疼痛、疾病分期和病程。5.决策树预测模型显示帕金森病患者睡眠障碍的独立影响因素是性别、焦虑、抑郁、疼痛、疾病分期和病程。6.Logistic回归预测模型特异度0.671,敏感度0.748,受试者工作曲线下面积为0.782。决策树预测模型特异度0.689,敏感度0.835,受试者工作曲线下面积为0.853。结论1.帕金森病患者睡眠障碍患病率为63.04%,其影响因素包括性别、焦虑、抑郁、疼痛、疾病分期和病程。2.Logistic回归预测模型特异度0.671,敏感度0.748,受试者工作曲线下面积为0.782,决策树预测模型特异度0.689,敏感度0.835,受试者工作曲线下面积为0.853。总体而言,决策树模型整体预测性能更佳,可为科学管理帕金森病睡眠障碍和早期识别高危人群提供参考。更多还原

【Abstract】 Objectives1.The aim of this study is to investigate the current status of sleep disorders occurring in patients with Parkinson’s disease and to explore the influencing factors that may cause their sleep disorders.2.The aim of this study is to construct a column-line diagram and a decision tree prediction model of sleep disorders in patients with Parkinson’s disease,and to evaluate and validate the model,so as to provide a reference basis for improving the sleep quality of patients with Parkinson’s disease.MethodsIn the first stage,the first draft of the Extraction Table of Potential Influencing Factors for Sleep Disorders in Parkinson’s disease patients was formed through the literature review method,and then the final draft was formed based on the results of the experts’meeting,so as to formulate the potential influencing factors for sleep disorders in Parkinson’s Disease.In the second stage,this study will use convenience sampling method to conduct a cross-sectional survey of 230 Parkinson’s disease patients attending neurology and surgery departments of a tertiary-level hospital in Qingdao City from November 2023 to June 2024,and divide the study population into training set and test set according to the ratio of 7:3.The General Information Questionnaire,Parkinson’s Disease Sleepiness Scale-2,Parkinson’s Disease Disease Staging,Unified Parkinson’s Disease Rating Scale Part III,Hamilton Anxiety Inventory,Hamilton Depression Inventory-17,Modified King’s Parkinson’s Disease Pain Inventory in Chinese,and Montreal Cognitive Assessment Inventory were used to carry out a survey of the current status of sleep in patients with Parkinson’s disease and to analyse the relevant factors that influence it.SPSS 26.0 and R Studio 4.4.2 were used to statistically analyse the data in this study.Firstly,variables with significance in the influencing factors related to sleep disorders in Parkinson’s disease patients were initially screened by univariate analysis,and P<0.05 was used to represent statistically significant differences in the variables.Subsequently,Logistic Regression Analysis and Decision Tree Classification were used to construct the prediction model of sleep disorders in Parkinson’s disease patients respectively,and explore the independent risk factors of sleep disorders in Parkinson’s disease patients.Finally,the models were evaluated by plotting the working curves of the subjects and calculating the predictive model differentiation,sensitivity,specificity and clinical decision curve;and the models were internally validated using the validation set data.Results1.After a review of the literature and expert meeting,the factors potentially influencing sleep disorders in Parkinson’s disease were formulated to include gender,age,BMI,age of onset,duration of disease,marital statu s,place of residence,state of residence,education,monthly household income,type of health insurance,disease stage,history of smoking,history of alcohol consumption,motor impairment,cognitive function,levodopa daily equivalent dose,anxiety,and depression.2.A total of 230 patients with Parkinson’s disease were included in this study,of whom a total of 145 developed sleep disorders and a total of 85 did not develop sleep disorders,and the incidence of sleep disorders in Parkinson’s disease was 63.04%.3.Variables with statistically significant differences in the results of univariate analyses between the sleep disordered and non-sleep disordered groups in Parkinson’s disease were:gender,cognitive impairment,anxiety,depression,pain,disease stage,disease duration,and levodopa equivalent daily dose.4.Logistic regression analyses showed that the independent influences on sleep disorders in Parkinson’s disease patients were anxiety,depression,pain,disease stage,and disease duration.5.Decision tree modelling showed that the independent influences on sleep disorders in Parkinson’s disease patients were gender,anxiety,depression,pain,disease stage and disease duration.6.Logistic regression prediction model had a specificity of 0.671,a sensitivity of0.748,and an area under the subject’s working curve of 0.782.Decision tree prediction model had a specificity of 0.689,a sensitivity of 0.835,and an area under the subject’s working curve of 0.853.Conclusion1.The prevalence of sleep disorders in Parkinson’s disease patients was 63.04 per cent.Factors affecting sleep disorders in Parkinson’s disease patients included gender,anxiety,depression,pain,disease stage and disease duration.2.The logistic regression prediction model had a specificity of 0.671,a sensitivity of 0.748,and an area under the subject’s working curve of 0.782,while the decision tree prediction model had a specificity of 0.689,a sensitivity of 0.835,and an area under the subject’s working curve of 0.853.Overall,the decision tree prediction model had a better predictive performance,and the results of the study can provide a good basis for early identification and intervention of sleep disorders in Parkinson’s disease.The results of this study can provide a scientific basis for the early identification and intervention of sleep disorders,especially the application of decision tree prediction model can provide a useful reference for the clinical treatment and the development of prevention strategies.更多还原