International Journal of Cardiology: Heart & Vasculature (Jun 2024)

Predicting outcomes in patients with low ejection fraction undergoing coronary artery bypass graft

  • Mohammad Sadeq Najafi,
  • Soroush Nematollahi,
  • Ahmad Vakili-Basir,
  • Arash Jalali,
  • Arezoo Gholami,
  • Mohadese Dashtkoohi,
  • Saeed Davoodi,
  • Mina Pashang,
  • Namvar Movahedi,
  • Kyomars Abbasi,
  • Soheil Mansourian,
  • Haleh Ashraf,
  • Seyed Hossein Ahmadi Tafti

Journal volume & issue
Vol. 52
p. 101412

Abstract

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Introduction: Reduced left ventricular ejection fraction (LVEF) is a well-known predictor of adverse events after cardiac surgery. We aimed to assess the outcomes in patients with low LVEF undergoing coronary artery bypass graft. Methods: In this retrospective cohort, we included all patients with left ventricular ejection fraction ≤ 40 who underwent coronary artery bypass grafting between March 2007 and March 2016 (with a median follow-up of nine years) at Tehran Heart Center. Demographics and clinical characteristics were extracted from the data registry. Akaike information criterion (AIC) was used. The univariate Cox regression was performed. We investigated the predictors of mortality and major adverse cardiac and cerebrovascular events (MACCE) using Cox multivariable regression. Results: In total, 5,532 cases (79 % male) with a mean age of 65.58 were included in the study. The nine-year overall survival was calculated at 68 %, and more than half of the patients had MACCE (55 %). In adjusted multivariable Cox regression analysis, moderate to severe mitral valve regurgitation, glomerular filtration rate ≤ 60, mild right ventricular dysfunction, and valvular heart disease independently predicted higher mortality. The abovementioned predictors and peripheral vascular disease significantly increased MACCE. Conclusion: Our study indicates the clinical significance of mitral regurgitation, valvular heart disease, and renal function in patients with low ejection fraction treated by coronary artery bypass grafting surgery. Identifying predictors of adverse events can help with clinical decision-making and risk stratification, ultimately improving patient outcomes.

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