Abstract
Coronary artery bypass grafting (CABG) is a commonly performed surgical intervention for coronary artery disease (CAD), aiming to restore blood flow to the myocardium and improve cardiac function. Left ventricular (LV) systolic function is a crucial determinant of postoperative outcomes in CABG patients, yet the predictors of adverse outcomes remain incompletely understood. Objective: This study aimed to evaluate left ventricular function outcomes following CABG and identify predictors of mortality among CABG patients. Methods: A prospective cohort study was conducted at, Ch. Pervaiz Elahi Institute Of Cardiology (CPEIC), Multan, Pakistan, from December 2022 to December 2023. The study included 96 patients undergoing CABG surgery. Preoperative data collection encompassed demographic information, medical history, and baseline clinical characteristics. Left ventricular function was assessed using echocardiography preoperatively and postoperatively. Statistical analysis involved descriptive statistics, univariate analysis, and univariate regression. Results: The study cohort comprised 96 participants, with a mean age of 56.1 years (±12.2) and a male predominance (67.7%). Prevalent comorbidities included diabetes mellitus (78.1%) and hypertension (79.2%). LV systolic dysfunction (30.2%) and old ischemic heart disease (72.9%) were common preexisting conditions. Intraoperative variables included perioperative inotropes (5.2%) and emergent surgeries (3.1%). Postoperative complications included reoperation for bleeding (5.2%) and deterioration of LV ejection fraction (34.4%). The mortality rate was 5.2%. Postoperatively, the mean LVEF increased significantly from 32.00% (±5.219) to 37.00% (±9.801) in isolated CABG patients. Conclusion: This study confirms the significant improvement in LV systolic function following CABG and underscores the high benefit of CABG in patients with reduced EF. However, diabetes mellitus, advanced diastolic dysfunction, and the insertion of IABP were identified as significant predictors of adverse outcomes. Identifying patients with these risk predictors could provide complementary prognostic information and help optimise care, monitoring, and follow-up to improve their expected poor outcomes.
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