Abstract P249: Association Between Cardiovascular Event Type And Smoking Cessation Rates Among Outpatients With Atherosclerotic Cardiovascular Disease: Insights From The Ncdr Pinnacle Registry

Abstract

Listen

Translate article

Introduction: Understanding the factors that drive cessation may help inform patient counseling and improve quit rates. We aimed to determine if the type of atherosclerotic cardiovascular disease (ASCVD) event impacts the patient’s likelihood of quitting, independent of patient factors. Hypothesis: We assessed the hypothesis that more impactful ASCVD events would be associated with a greater likelihood of smoking cessation (e.g., more likely to quit after CABG vs PCI). Methods: Using 2013-2016 data from the NCDR PINNACLE outpatient cardiac registry, we identified patients who were current smokers and had an eventual diagnosis of ASCVD. Self-reported smoking status was assessed at each consecutive visit and rates of smoking cessation after each interim ASCVD event (MI, PCI, CABG, stroke/TIA, and PAD diagnosis) were examined. We then constructed separate Cox models with non-proportional hazards for interim ASCVD events (time-dependent covariates), adjusting for age, sex, race, dyslipidemia, diabetes, hypertension, and heart failure; and compared models using AIC. Results: Across 358 cardiology practices, 378478 patients were smokers who eventually had ASCVD (mean age 65.8±11.9, male 63.6%). Cessation rates after each ASCVD event were: MI 30.5%, PCI 30.7%, CABG 28.5%, stroke/TIA 24.7%, and PAD 24.6% (Table). After multivariable adjustment, patients were more likely to quit smoking after a stroke/TIA (HR 2.46, 95% CI 2.40-2.52) followed by CABG (HR 2.45, 95% CI 2.37-2.53), PCI (HR 1.85, 95% CI 1.79-1.91), MI (HR 1.79, 95% CI 1.72-1.86), and a new diagnosis of PAD (HR 1.47, 95% CI 1.42-1.51); p<0.0001 for all events. Conclusion: In a large US registry, we found that only ~25-30% of patients reported quitting smoking after an ASCVD event, but the type of ASCVD event significantly impacts the likelihood of smoking cessation. Using these data to inform patient counseling—targeting cessation efforts to those most likely to quit—may help improve outcomes.