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Prediction of cardiac arrest in patients with heart failure in Sweden: a registry study with development of a machine learning model
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Prediction of cardiac arrest in patients with heart failure in Sweden: a registry study with development of a machine learning model
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Journal Article
Prediction of cardiac arrest in patients with heart failure in Sweden: a registry study with development of a machine learning model
2026
Overview
Objective30-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.DesignA nationwide registry-based observational study.SettingData were sourced from the Swedish Heart Failure Registry (1 January 2005 to 31 December 2021).ParticipantsThis 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 analysisThe 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. The model was evaluated with Brier score, observed versus predicted cumulative incidence, Concordance-index (C-index) and time-dependent area under the curve of a receiver operating characteristics graph (AUC-ROC).ResultsIn this cohort, 2399 (5%) patients had received cardiopulmonary resuscitation (CPR) (5%), and 31 989 (71%) patients died without resuscitation. Our model with 82 predictors had a low Brier score indicating a capacity to accurately predict cumulative incidence of cardiac arrest on a group level. However, the model also had a low C-index 0.52 and low AUC-ROC 0.63–0.65.ConclusionOur Random Survival Forest model for competing risk could not accurately predict cardiac arrest in individual patients newly diagnosed with heart failure, because the event death without attempted resuscitation was treated as a competing event. The lack of information on transitions to palliative care and Do-Not-Attempt-CPR-orders limits the clinical relevance of any cardiac arrest prediction model.
Publisher
British Medical Journal Publishing Group,BMJ Publishing Group LTD,BMJ Publishing Group
