Predictors of Sudden Cardiac Arrest Among Patients With Post-Myocardial Infarction Ejection Fraction Greater Than 35%

Background Sudden cardiac arrest (SCA) risk increases after myocardial infarction (MI) in patients with a reduced ejection fraction (EF). However, the risk factors for SCA among patients with a post-MI EF >35% remain poorly understood. Methods and Results Using the Optum de-identified electronic health record data set from 2008 to 2017, we identified patients with an incident MI diagnosis and troponin elevation who had a post-MI EF >35% and underwent coronary angiography. Primary outcome was SCA within 1 year post-MI. The database was divided into derivation (70%) and validation (30%) cohorts by random selection. Cox proportional hazard regression was used to generate and validate a risk prediction model. Among 31 286 patients with an MI (median age 64.1; 39% female; 87% White), 499 experienced SCA within 1 year post-MI (estimated probability 1.8%). Lack of revascularization at MI, post-MI EF <50%, Black race, renal failure, chronic obstructive pulmonary disease, antiarrhythmic therapy, and absence of beta blocker therapy were independent predictors of SCA. A multivariable model consisting of these variables predicted SCA risk (C-statistic 0.73). Based on this model, the estimated annual probability of SCA was 4.4% (95% CI, 3.9-4.9) in the highest quartile of risk versus 0.6% (95% CI, 0.4-0.8) in the lowest quartile. Conclusions Patients with a post-MI EF >35% have a substantial annual risk of SCA. A risk model consisting of acute coronary revascularization, EF, race, renal failure, chronic obstructive pulmonary disease, antiarrhythmic therapy, and beta blocker therapy can identify patients with higher risk of SCA, who may benefit from further risk stratification and closer monitoring.

Automated summary

Patients with a post-MI EF >35% have a substantial annual risk of SCA, with factors such as lack of revascularization, post-MI EF <50%, Black race, renal failure, chronic obstructive pulmonary disease, antiarrhythmic therapy, and absence of beta blocker therapy predicting higher risk. Identifying high-risk patients using a multivariable risk model can help in further risk assessment and monitoring to prevent SCA.