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Warning symptoms associated with imminent sudden cardiac arrest: a population-based case-control study with external
Kyndaron Reinier1, Bernadine Dizon1, Harpriya Chugh1
1Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Health System, Los Angeles, CA, USA.
Warning symptoms like chest pain and shortness of breath are linked to sudden cardiac arrest (SCA). These symptoms, particularly in men, may help predict SCA events, but further research is needed.
Area of Science:
- Cardiology
- Public Health
- Digital Health
Background:
- Sudden cardiac arrest (SCA) is a critical global health issue with over 90% mortality.
- Prearrest warning symptoms present an opportunity for digital technology-based interventions to improve survival.
- Understanding the association between symptoms and imminent SCA is crucial for developing predictive tools.
Purpose of the Study:
- To estimate the strength of association between specific symptoms and imminent sudden cardiac arrest.
- To investigate potential sex-specific differences in warning symptoms preceding SCA.
- To explore the utility of prearrest symptoms for predicting SCA events.
Main Methods:
- A case-control study utilizing data from two US community-based studies (PRESTO and SUDS).
- Inclusion of individuals aged 18-85 with witnessed sudden cardiac arrest and a control group with similar symptoms.
- Logistic regression models were employed to evaluate symptom associations in discovery and replication populations.
Main Results:
- Patients with SCA were more likely to report dyspnea, chest pain, diaphoresis, and seizure-like activity compared to controls.
- Sex-specific analysis revealed chest pain, dyspnea, and diaphoresis associated with SCA in men, while only dyspnea was significant in women.
- Findings were largely consistent in the replication population, with some variations in symptom associations by sex.
Conclusions:
- The prevalence and pattern of warning symptoms preceding SCA are sex-specific.
- Warning symptoms show promise for predicting imminent SCA but may require additional features for maximized predictive power.
- Further research is needed to refine predictive models for SCA using symptom data and potentially other digital biomarkers.
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