Higher chances of survival to hospital admission after out-of-hospital cardiac arrest in patients with previously diagnosed heart disease

Abstract

AimThis study aimed to determine whether patients suffering from out-of-hospital cardiac arrest (OHCA) with a pre-OHCA diagnosis of heart disease have higher survival chances than patients without such a diagnosis and to explore possible underlying mechanisms.MethodsA retrospective cohort study in 3760 OHCA patients from the Netherlands (2010–2016) was performed. Information from emergency medical services, treating hospitals, general practitioner, resuscitation ECGs and civil registry was used to assess medical histories and the presence of pre-OHCA diagnosis of heart disease. We used multivariable regression analysis to calculate associations with survival to hospital admission or discharge, immediate causes of OHCA (acute myocardial infarction (AMI) vs non-AMI) and initial recorded rhythm.ResultsOverall, 48.1% of OHCA patients had pre-OHCA heart disease. These patients had higher odds to survive to hospital admission than patients without pre-OHCA heart disease (OR 1.25 (95%CI 1.05 to 1.47)), despite being older and more often having cardiovascular risk factors and some non-cardiac comorbidities. These patients also had higher odds of shockable initial rhythm (SIR) (OR 1.60 (1. 36 to 1.89)) and a lower odds of AMI as immediate cause of OHCA (OR 0.33 (0.25 to 0.42)). Their chances of survival to hospital discharge were not significantly larger (OR 1.16 (0.95 to 1.42)).ConclusionHaving pre-OHCA diagnosed heart disease is associated with better odds to survive to hospital admission, but not to hospital discharge. This is associated with higher odds of a SIR and in a subgroup with available diagnosis a lower proportion of AMI as immediate cause of OHCA.

Sudden cardiac death: recognising hidden risk among women versus men

“…Crucially, however, among both men and women, almost half of SCD victims were not diagnosed with cardiovascular disease prior to SCD: 48.6% of men and even 49.3% of women. This observation mirrors previous studies that were not sex-stratified 2 6. Our present inability to recognise the underlying cardiovascular disease that increased these victims’ risk of SCA deprives them of any chance to receive timely treatment for their disease that could have saved them from suffering SCA altogether.…”

“…Our present inability to recognise the underlying cardiovascular disease that increased these victims’ risk of SCA deprives them of any chance to receive timely treatment for their disease that could have saved them from suffering SCA altogether. The benefit of earlier recognition of such disease is also demonstrated by the recent observation that, in the event that SCA does occur, SCA victims with previously diagnosed cardiovascular disease have better odds of surviving to hospital admission than SCA victims without such known disease, partly due to a higher chance of having a shockable initial rhythm 6. Clearly, to reduce the societal burden of SCD, we must focus our efforts on earlier recognition of SCA risk.…”

Using routine primary care data to assess Sudden Cardiac Arrest risk in people with type 2 diabetes: a proof-of-concept case-control study

“…Moreover, an out-of-hospital SCA is often the first manifestation of (coronary) heart disease 17,18 . Accordingly, around 50% of SCA victims have never consulted a cardiologist prior to the SCA, and are therefore not represented in cardiologic care records 19 . Furthermore, even when some of these SCA victims without prior CVD history are included in a study, for example in studies using death certificates or population cohorts, there is often insufficient data on cardiovascular risk factors or there are not enough SCA cases and thus not enough statistical power to study SCA 20 .…”

Machine learning of electrophysiological signals for the prediction of ventricular arrhythmias: systematic review and examination of heterogeneity between studies

“…3 , 4 , 5 However, LV dysfunction is inadequate as the sole surrogate marker for the underlying dynamic and complex mechanisms responsible for malignant VA. 6 , 7 The majority of patients who suffer an out-of-hospital cardiac arrest or SCD have preserved left ventricular systolic function. 8 , 9 New approaches to predict VA may be enabled by a combination of artificial intelligence (AI) and the increasing availability in electrophysiological signals obtained non-invasively using body-surface electrocardiography (ECG), intra-cardiac devices or wearable sensors. Machine learning (ML) and deep learning (DL) facilitate detection of ECG signatures and patterns that are unrecognizable by the human eye and might indicate sub-clinical pathology.…”