Abstract 13629: Unique Age- and Sex-Specific Patterns Identified for Prodromal Symptoms of Sudden Cardiac Arrest

Introduction: The majority of individuals who suffer sudden cardiac arrest (SCA) display prodromal symptoms in the preceding hours, days and weeks. These could potentially improve risk assessment for SCA, making it important to explore the influence of age and sex on symptom manifestation. Hypothesis: Prodromal symptoms of SCA are different based on age and sex. Methods: We included cases of witnessed SCA (ages 35-85) attended by emergency medical services following 911 calls made in Ventura County, CA (2015-2021). SCA was defined as a sudden, unexpected pulseless condition of likely cardiac origin; non-cardiac etiology were excluded. All cases were required to have at least one inclusion symptom recorded in the EMS narrative at the time of the 911 call. These were acute coronary syndrome (ACS) or SCA symptoms reported in the published literature. We constructed mutually exclusive symptom groups in hierarchical order: chest pain, dyspnea, syncope/palpitations, and other. We compared symptoms in middle age (35-64y) and older (65-85y) cases using chi-square tests. Results: Of 404 SCA cases that met analysis criteria, 122 (30%) were female. Women comprised 22% of the middle-age group and 37% of the older group. In the middle-age group, chest pain was the most common symptom among men (48%) but was half as common in women (24%, p<0.01, Figure). Among middle-aged women, dyspnea was the most common symptom (39%) and significantly higher compared to men (22%, p <0.05). Chest pain prevalence was lower in both sexes in the older group (31% in men vs 20% in women). We observed no significant differences in dyspnea by sex in the older group, nor differences in syncope/palpitations or other symptoms in either group by sex. Conclusions: We observed unique pre-SCA prodromal symptom patterns after stratifying by age and sex, most notable in middle age. These findings suggest the importance of considering age and sex while developing SCA prediction algorithms that include prodromal symptoms.