BMC geriatrics · 2025
Prediction of out-of-hospital cardiac arrest in older patients with insomnia: a longitudinal population study.
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Sung Chih-Wei, Chen Cheng-Che, Chih Yun-Ting, Fan Cheng-Yi, Huang Edward Pei-Chuan
Аннотация
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.