Sung Chih-Wei, Chen Cheng-Che, Chih Yun-Ting, Fan Cheng-Yi, Huang Edward Pei-Chuan
Department of Emergency Medicine, National Taiwan University Hospital Hsin-Chu Branch, Hsinchu, Taiwan.
Department of Emergency Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan.
BMC Geriatr. 2025 Aug 7;25(1):600. doi: 10.1186/s12877-025-06285-x.
The association of insomnia in older patients with out-of-hospital cardiac arrest (OHCA) is not completely elucidated. The current study developed and validated a predictive model for OHCA in older patients using population-based analysis.
This study used data from the National Health Insurance research database. The cohort included older patients (aged more than 65 years) diagnosed with insomnia and treated with insomnia medications. The multivariate logistic regression model was used to analyze potential OHCA predictors. The model's performance was evaluated via internal and external validations using the receiver operating characteristic curve and confusion matrix indices.
Of the 438,147 older patients with insomnia, 6,931 (1.6%) experienced OHCA. The key predictors included age, male sex, previous use of medical resources, treatment with hemodialysis, existing comorbidities, medication possession ratio, medication changes, and recent psychotherapy. The receiver operating characteristic curve values of the predictive models for 7-, 30-, and 90-day OHCA ranged from 0.757 to 0.787. The 2019 and 2020 external validation confirmed that the model was robust. The sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio of the 7-day model in 2019 were 0.781, 0.754, 2.78, 0.42, and 6.58, respectively. Meanwhile, the sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio of the 7-day model in 2020 were 0.731, 0.677, 2.27, 0.40, and 5.71, respectively.
This study developed a robust predictive model for OHCA among older patients with insomnia. The model was effective in identifying important predictors that could assist psychiatrists in recognizing high-risk individuals and enhancing preventive care.
老年院外心脏骤停（OHCA）患者中失眠的相关性尚未完全阐明。本研究通过基于人群的分析，开发并验证了一种老年患者OHCA的预测模型。
本研究使用了国民健康保险研究数据库中的数据。该队列包括诊断为失眠并接受失眠药物治疗的老年患者（年龄超过65岁）。采用多因素逻辑回归模型分析潜在的OHCA预测因素。使用受试者工作特征曲线和混淆矩阵指标，通过内部和外部验证来评估模型的性能。
在438,147名老年失眠患者中，6,931名（1.6%）发生了OHCA。关键预测因素包括年龄、男性、既往医疗资源使用情况、血液透析治疗、现有合并症、药物持有率、药物变化和近期心理治疗。7天、30天和90天OHCA预测模型的受试者工作特征曲线值在0.757至0.787之间。2019年和2020年的外部验证证实该模型具有稳健性。2019年7天模型的敏感性、特异性、阳性似然比、阴性似然比和诊断比值比分别为0.781、0.754、2.78、0.42和6.58。同时，2020年7天模型的敏感性、特异性、阳性似然比、阴性似然比和诊断比值比分别为0.731、0.677、2.27、0.40和5.71。
本研究开发了一种针对老年失眠患者OHCA的稳健预测模型。该模型有效地识别了重要的预测因素，可帮助精神科医生识别高危个体并加强预防保健。
