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

Associations of Clinical Characteristics With Sudden Cardiac Arrest in People With Type 2 Diabetes With and Without Cardiovascular Disease: A Longitudinal Case-Control Study Using Routine Primary Care Data

OBJECTIVE

To assess longitudinal associations with sudden cardiac arrest (SCA) of clinical characteristics recorded in primary care in people with type 2 diabetes (T2D), both with and without cardiovascular disease (CVD).

RESEARCH DESIGN AND METHODS

We performed a case-control study, with SCA case subjects with T2D from the Amsterdam Resuscitation Studies (ARREST) registry of out-of-hospital resuscitation attempts in the Dutch Noord-Holland region (2010-2020) and up to five matched (age, sex, T2D, general practitioner [GP] practice) non-SCA control subjects. We collected relevant clinical measurements, medication use, and medical history from GPs' electronic health care records. We analyzed the associations of clinical characteristics and medication use with SCA in the total sample and in subgroups with or without CVD using multivariable time-dependent Cox regression (hazard ratios, 95% confidence intervals).

RESULTS

We included 689 SCA case subjects and 3,230 non-SCA control subjects. In multivariable models, low fasting glucose (<4.5 mmol/mol: 1.91 [1.00-3.64]), antihypertensive (1.80 [1.39-2.33]), glucose lowering (oral only: 1.32 [1.06-1.63]; insulin only: 2.31 [1.71-3.12]; oral and insulin: 1.64 [1.21-2.22]), heart failure (1.91 [1.55-2.35]), and QTc-prolonging prokinetic (1.78 [1.27-2.50]), antibiotic (1.35 [1.05-1.73]), and antipsychotic (2.10 [1.42-3.09]) medication were associated with SCA in the total sample. In subgroup effect modification analyses, QTc-prolonging antibiotic (1.82 [1.26-2.63]) and antipsychotic (3.10 [2.09-4.59]) medication use were associated with SCA only in those without CVD.

CONCLUSIONS

In people with T2D, low fasting glucose and QTc-prolonging prokinetic, antibiotic, or antipsychotic medication use and a history of heart failure are associated with SCA risk. Subgroup analyses indicate antibiotic and antipsychotic medication use increases SCA risk specifically in those without CVD.

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

BACKGROUND

Ventricular arrhythmia (VA) precipitating sudden cardiac arrest (SCD) is among the most frequent causes of death and pose a high burden on public health systems worldwide. The increasing availability of electrophysiological signals collected through conventional methods (e.g. electrocardiography (ECG)) and digital health technologies (e.g. wearable devices) in combination with novel predictive analytics using machine learning (ML) and deep learning (DL) hold potential for personalised predictions of arrhythmic events.

METHODS

This systematic review and exploratory meta-analysis assesses the state-of-the-art of ML/DL models of electrophysiological signals for personalised prediction of malignant VA or SCD, and studies potential causes of bias (PROSPERO, reference: CRD42021283464). Five electronic databases were searched to identify eligible studies. Pooled estimates of the diagnostic odds ratio (DOR) and summary area under the curve (AUROC) were calculated. Meta-analyses were performed separately for studies using publicly available, ad-hoc datasets, versus targeted clinical data acquisition. Studies were scored on risk of bias by the PROBAST tool.

FINDINGS

2194 studies were identified of which 46 were included in the systematic review and 32 in the meta-analysis. Pooling of individual models demonstrated a summary AUROC of 0.856 (95% CI 0.755-0.909) for short-term (time-to-event up to 72 h) prediction and AUROC of 0.876 (95% CI 0.642-0.980) for long-term prediction (time-to-event up to years). While models developed on ad-hoc sets had higher pooled performance (AUROC 0.919, 95% CI 0.867-0.952), they had a high risk of bias related to the re-use and overlap of small ad-hoc datasets, choices of ML tool and a lack of external model validation.

INTERPRETATION

ML and DL models appear to accurately predict malignant VA and SCD. However, wide heterogeneity between studies, in part due to small ad-hoc datasets and choice of ML model, may reduce the ability to generalise and should be addressed in future studies.

FUNDING

This publication is part of the project DEEP RISK ICD (with project number 452019308) of the research programme Rubicon which is (partly) financed by the Dutch Research Council (NWO). This research is partly funded by the Amsterdam Cardiovascular Sciences (personal grant F.V.Y.T).

Out-of-Hospital Cardiac Arrest during the COVID-19 Pandemic: A Systematic Review

Objective: Out-of-hospital cardiac arrest (OHCA) is a prominent cause of death worldwide. As indicated by the high proportion of COVID-19 suspicion or diagnosis among patients who had OHCA, this issue could have resulted in multiple fatalities from coronavirus disease 2019 (COVID-19) occurring at home and being counted as OHCA. Methods: We used the MeSH term "heart arrest" as well as non-MeSH terms "out-of-hospital cardiac arrest, sudden cardiac death, OHCA, cardiac arrest, coronavirus pandemic, COVID-19, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)." We conducted a literature search using these search keywords in the Science Direct and PubMed databases and Google Scholar until 25 April 2022. Results: A systematic review of observational studies revealed OHCA and mortality rates increased considerably during the COVID-19 pandemic compared to the same period of the previous year. A temporary two-fold rise in OHCA incidence was detected along with a drop in survival. During the pandemic, the community's response to OHCA changed, with fewer bystander cardiopulmonary resuscitations (CPRs), longer emergency medical service (EMS) response times, and worse OHCA survival rates. Conclusions: This study's limitations include a lack of a centralised data-gathering method and OHCA registry system. If the chain of survival is maintained and effective emergency ambulance services with a qualified emergency medical team are given, the outcome for OHCA survivors can be improved even more.