Unveiling the risk: predicting out-of-hospital cardiac arrest in geriatric patients with depression - longitudinal...

The association between geriatric depression and out-of-hospital cardiac arrest (OHCA) has not been fully clarified.

This study aimed to develop and validate a predictive model for OHCA in older patients through a longitudinal, population-based approach.

This study analysed data from the National Health Insurance Research Database for the period 2011-2020, focusing on older patients both diagnosed with depression and treated with antidepressant medications. A multivariate logistic regression model was used to identify potential predictors of OHCA. Considering the effect of COVID-19, data-sets from 2019 and 2020 were used as external validation. The model's performance was evaluated using receiver operating characteristic (ROC) curves and confusion matrix metrics.

Out of 104 022 geriatric patients with depression, 2479 (2.4%) experienced OHCA. Significant predictors of OHCA included age, male gender, previous utilisation of medical resources, renal failure with haemodialysis, existing comorbidities, medication changes and recent psychotherapy. The ROC values for the predictive models ranged from 0.707 to 0.771 in the 2019 and 2020 external validations for 7-, 30- and 90-day OHCA. For 2019, the 7-day model demonstrated sensitivity, specificity, positive likelihood ratio, negative likelihood ratio and diagnostic odds ratio of 0.600, 0.718, 2.130, 0.560 and 3.840, respectively. For 2020, these metrics for the 7-day model were 0.775, 0.655, 2.250, 0.340 and 6.550, respectively.

This study developed and validated a predictive model for OHCA in older patients with depression. The model identified crucial predictors, providing valuable insights for psychiatrists and emergency clinicians to identify high-risk patients and implement early preventive measures.