帕金森病合并睡眠障碍的影响因素与风险预测模型构建OA
Influencing factors and risk prediction model construction for Parkinson's disease complicated with sleep disorders
目的 分析帕金森病(PD)合并睡眠障碍的影响因素,并建立风险预测模型.方法 回顾性收集2021 年6 月至2023 年6 月我院 162 例 PD 患者的临床资料,按 2∶1随机分为开发集 108 例和验证集 54例.所有患者随访6 个月,将开发集患者按是否发生睡眠障碍分组,比较各组一般资料;采用二元多因素Logistic 回归分析各影响因素并建立方程、构建 Nomogram 预测模型,通过 ROC 和 Calibration 曲线验证模型效能及校准能力.结果 开发集、验证集睡眠障碍发生率分别为 52.78%、53.70%.Logistic 回归模型显示,病程、Hoehn-Yahr 分级、TNF-α 水平、血浆同型半胱氨酸(Hcy)水平、每日左旋多巴等效剂量(LEDD)是 PD 合并睡眠障碍的危险因素(均 P<0.05).ROC 曲线显示,Nomogram 模型预测开发集合并睡眠障碍的曲线下面积(AUC)为0.923,灵敏度为87.50%,特异度为91.94%;预测验证集合并睡眠障碍的 AUC 为 0.907,灵敏度为93.06%,特异度为87.33%.Hosmer-Lemeshow 检验显示,Nomogram 模型预测开发集、验证集发生睡眠障碍的概率与实际概率差异均无统计学意义(χ2=0.587,P=0.198;χ2=0.833,P=0.124).结论 病程、Hoehn-Yahr 分级、血浆 TNF-α 水平、血浆 Hcy 水平、LEDD 是 PD 合并睡眠障碍的危险因素,以上述危险因素为依据构建的风险预测 Nomogram 模型在预测 PD 患者发生睡眠障碍时的临床效能良好.
Objective To analyze the influencing factors of Parkinson's disease(PD)complicated with sleep disorders and to establish a risk prediction model.Methods Clinical data of 162 patients with PD admitted to our hospital from June 2021 to June 2023 were retrospectively collected and randomly divided into a development cohort(108 cases)and a validation cohort(54 cases)at a ratio of 2∶1.All patients were followed up for 6 months,and the development cohort was grouped according to the occurrence of sleep disorders,with general data compared between groups.Binary multivariate Logistic regression analysis was performed to identify influencing factors,establish regression equations,and construct a Nomogram prediction model.The efficacy and calibration of the model were verified using ROC and calibration curves.Results The incidence rates of sleep disorders in the development set and validation set were 52.78%and 53.70%,respectively.The Logistic regression model showed that disease duration,Hoehn-Yahr grade,TNF-α level,plasma homocysteine(Hcy)level,and daily levodopa equivalent dose(LEDD)were risk factors for PD complicated with sleep disorders(all P<0.05).The ROC curve showed that the Nomogram model predicted PD complicated with sleep disorders in the development set with an area under the curve(AUC)of 0.923,a sensitivity of 87.50%,and a specificity of 91.94%,while the AUC for the validation set was 0.907,with a sensitivity of 93.06%and a specificity of 87.33%.The Hosmer-Lemeshow test indicated no statistically significant differences between the probabilities of sleep disorders predicted by the Nomogram model and the actual probabilities in both the development set(χ2=0.587,P=0.198)and the validation set(χ2=0.833,P=0.124).Conclusions Disease duration,Hoehn-Yahr grade,plasma TNF-α level,plasma Hcy level,and LEDD are risk factors for PD complicated with sleep disorders.The risk prediction Nomogram model constructed based on these risk factors demonstrates good clinical performance in predicting the occurrence of sleep disorders in PD patients.
邓蓉;王翔
448000 荆门市中医医院(市石化医院)脑病科448000 荆门市中医医院(市石化医院)脑病科
医药卫生
帕金森病睡眠障碍Nomograms危险因素
Parkinson's diseasesleep disordersNomogramsrisk factors
《临床神经病学杂志》 2026 (3)
203-209,7
荆门市科学技术研究与开发计划项目(2023YDKY158)
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