Prediction of cardiac arrest in patients with heart failure in Sweden: a registry study with development of a machine learning model

25 Jun 20264 min read0 viewsJournal Feed

GIST (Key Takeaways)

Objective 30-day survival after cardiac arrest is low, 12.4% and 36% for out-of-hospital and in-hospital cardiac arrest, respectively. Heart failure is a known risk condition for cardiac arrest. Improving our ability to identify patients at high risk of cardiac arrest would enable prevention.
We aimed to develop a prediction model for cardiac arrest to be used in patients newly diagnosed with heart failure. Design A nationwide registry-based observational study. Setting Data were sourced from the Swedish Heart Failure Registry (1 January 2005 to 31 December 2021).
Participants This cohort included 45 068 patients discharged from hospital after first hospitalisation for newly diagnosed heart failure. Patients discharged from hospital with palliative care and/or implantable defibrillators were excluded. Outcome measure and analysis The primary outcome was defined as cardiac arrest registered in the Swedish Registry for Cardiopulmonary Resuscitation until final follow-up (15 November 2022).
Patients who died without resuscitation were treated as competing events. A Random Survival Forest model for competing risk was developed using predictors from the heart failure registry.

Clinical Editorial

Summary

BMJ Open published a clinical update in Research Highlights on 25 Jun 2026.

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