Influence of ejection fraction on hospital mortality, morbidity, and costs for CABG patients

G L Kay1, G W Sun, A Aoki

  • 1Heart Institute, Good Samaritan Hospital, Los Angeles, California 90017-2395, USA.

Insights

Preoperative ejection fraction (EF) significantly impacts outcomes for coronary artery bypass grafting. Patients with EF 0.40 or greater show the best results, while lower EF values indicate increased mortality and morbidity.

Area of Science:

  • Cardiology
  • Health Economics

Background:

  • Preoperative ejection fraction (EF) is a known risk factor for adverse outcomes in patients undergoing isolated coronary artery bypass grafting (CABG).
  • Understanding the precise impact of EF on mortality, morbidity, and costs is crucial for patient management and resource allocation.

Purpose of the Study:

  • To investigate the influence of preoperative ejection fraction (EF) on isolated coronary artery bypass grafting (CABG) outcomes.
  • To analyze the relationship between EF stratification and hospital mortality, cardiac mortality, hospital morbidity, and hospital costs.

Main Methods:

  • Retrospective review of 1,354 consecutive patients undergoing isolated CABG.
  • Stratification of preoperative ejection fraction (EF) into regular intervals for comparative analysis.
  • Development and application of a novel statistical tool, "discharge analysis," for cost data evaluation.

Main Results:

  • Patients with an ejection fraction (EF) of 0.40 or greater demonstrated the most favorable outcomes, characterized by the lowest mortality, morbidity, and costs.
  • EF values above 0.40 did not show further predictive value for outcomes.
  • Patients with EF less than 0.30 experienced significantly poorer outcomes compared to those with EF between 0.30 and 0.39.

Conclusions:

  • Ejection fraction (EF) is a statistically validated predictor of mortality, morbidity, and resource utilization in CABG patients.
  • Patients can be effectively categorized into three groups based on EF ( >0.40, 0.30-0.39, <0.30) for predicting clinical and cost outcomes.
  • A new statistical method, "discharge analysis," was developed to enhance the accuracy of cost analysis in healthcare settings.
Abstract

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