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Ann Thorac Surg. 2011 Jun;91(6):1844-50. doi: 10.1016/j.athoracsur.2011.02.008. Epub 2011 Apr 14.

Utility of a simple algorithm to grade diastolic dysfunction and predict outcome after coronary artery bypass graft surgery.

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Department of Anesthesiology, Duke Clinical Research Institute, Duke University Medical Center, Durham, North Carolina 27710, USA.



Inclusion of a measure of left ventricular diastolic dysfunction (LVDD) may improve risk prediction after cardiac surgery. Current LVDD grading guidelines rely on echocardiographic variables that are not always available or aligned to allow grading. We hypothesized that a simplified algorithm involving fewer variables would enable more patients to be assigned a LVDD grade compared with a comprehensive algorithm, and also be valid in identifying patients at risk of long-term major adverse cardiac events (MACE).


Intraoperative transesophageal echocardiography data were gathered on 905 patients undergoing coronary artery bypass graft surgery, including flow and tissue Doppler-based measurements. Two algorithms were constructed to categorize LVDD: a comprehensive four-variable algorithm, A, was compared with a simplified version, B, with only two variables-transmitral early flow velocity and early mitral annular tissue velocity-for ease of grading and association with MACE.


Using algorithm A, only 563 patients (62%) could be graded, whereas 895 patients (99%) received a grade with algorithm B. Over the median follow-up period of 1,468 days, Cox modeling showed that LVDD was significantly associated with MACE when graded with algorithm B (p=0.013), but not algorithm A (p=0.79). Patients with the highest incidence of MACE could not be graded with algorithm A.


We found that an LVDD algorithm with fewer variables enabled grading of a significantly greater number of coronary artery bypass graft patients, and was valid, as evidenced by worsening grades being associated with MACE. This simplified algorithm could be extended to similar populations as a valid method of characterizing LVDD.

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