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

Published online by Cambridge University Press:  23 October 2025

Chih-Wei Sung Open the ORCID record for Chih-Wei Sung [Opens in a new window] ,
Cheng-Che Chen ,
Yun-Ting Chih ,
Cheng-Yi Fan Open the ORCID record for Cheng-Yi Fan [Opens in a new window]  and
Edward Pei-Chuan Huang Open the ORCID record for Edward Pei-Chuan Huang [Opens in a new window]
Chih-Wei Sung*
Affiliation:
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
Cheng-Che Chen
Affiliation:
Department of Psychiatry, National Taiwan University Hospital Hsin-Chu Branch, Hsinchu, Taiwan Department of Psychiatry, College of Medicine, National Taiwan University, Taipei, Taiwan
Yun-Ting Chih
Affiliation:
Department of Emergency Medicine, National Taiwan University Hospital Hsin-Chu Branch, Hsinchu, Taiwan
Cheng-Yi Fan
Affiliation:
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 Institute of Molecular Medicine, National Tsing Hua University, Hsinchu, Taiwan
Edward Pei-Chuan Huang
Affiliation:
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 Department of Emergency Medicine, National Taiwan University Hospital, Taipei, Taiwan
*
Correspondence: Chih-Wei Sung. Email: chihweisung@ntu.edu.tw; 114228@ntuh.gov.tw
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Abstract

Background

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

Aims

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

Method

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.

Results

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.

Conclusion

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.

Keywords

Out-of-hospital cardiac arrestprediction modeldepressiongeriatricNational Health Insurance Research Database

Information

Type
Original Article
Information
The British Journal of Psychiatry , First View , pp. 1 - 8
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Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of Royal College of Psychiatrists

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