Sex disparity of DAPT noncompliance in patients with left main stem PCI with DES.
Malik Faisal IftikharMuhammad Omer Rehman RanaAther NaeemMuhammad Saad WaqasMalik Hasnat Ul Hassan KhanUmer KhiyamWaheed AkhtarAmin MehmoodiJahanzeb MalikPublished in: Medicine (2024)
This retrospective study aims to explore the sex disparity in dual antiplatelet therapy (DAPT) noncompliance among left main stem percutaneous coronary intervention (PCI) patients with drug-eluting stent (DES) and identify predictors associated with non-adherence. Data were collected from the medical records of 1585 patients, including 1104 males and 481 females, who underwent left main stem PCI with DES. Baseline characteristics, angiographic features, and DAPT compliance rates at 1 month and 12 months were analyzed. Univariate logistic regression was used to identify predictors of DAPT noncompliance. The overall DAPT noncompliance rate at 1 month was 8.5%, increasing to 15.5% at 12 months. Females exhibited slightly higher noncompliance rates than males at both 1 month (15.6% vs 14.5%) and 12 months (28.1% vs 19.0%), although the difference was not statistically significant. Smoking status showed a modest impact on non-adherence, with current smokers exhibiting a lower noncompliance rate (14.9% at 1 month). Prior coronary artery disease history was associated with increased noncompliance at 12 months (18.9%). Angiographic characteristics, including lesion location and Syntax score, had no consistent association with DAPT noncompliance. This study highlights sex disparity in DAPT noncompliance among patients undergoing left main stem PCI with DES. Comorbidities, socioeconomic status, smoking status, and prior coronary artery disease history were identified as predictors of non-adherence.
Keyphrases
- antiplatelet therapy
- percutaneous coronary intervention
- coronary artery disease
- acute coronary syndrome
- st segment elevation myocardial infarction
- acute myocardial infarction
- st elevation myocardial infarction
- coronary artery bypass grafting
- patients undergoing
- end stage renal disease
- coronary artery bypass
- smoking cessation
- cardiovascular events
- atrial fibrillation
- machine learning
- chronic kidney disease
- healthcare
- type diabetes
- peritoneal dialysis
- heart failure
- ejection fraction
- left ventricular
- big data
- artificial intelligence
- deep learning