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Updated: Jun 2, 2026
Noninvasive Determination of Vortex Formation Time Using Transesophageal Echocardiography During Cardiac Surgery
Published on: November 28, 2018
Utility of a simple algorithm to grade diastolic dysfunction and predict outcome after coronary artery bypass graft
Madhav Swaminathan1, Alina Nicoara, Barbara G Phillips-Bute
1Department of Anesthesiology, Duke Clinical Research Institute, Duke University Medical Center, Durham, North Carolina 27710, USA. swami001@mc.duke.edu
Insights
A simplified algorithm for grading left ventricular diastolic dysfunction (LVDD) after cardiac surgery enabled more patients to be assessed and accurately predicted long-term major adverse cardiac events (MACE). This method improves risk stratification in coronary artery bypass graft patients.
Area of Science:
- Cardiology
- Echocardiography
- Cardiac Surgery
Background:
- Left ventricular diastolic dysfunction (LVDD) grading is crucial for risk prediction post-cardiac surgery.
- Current echocardiographic guidelines for LVDD grading are often limited by data availability and consistency.
- A simplified LVDD assessment may enhance grading feasibility and predictive accuracy.
Purpose of the Study:
- To develop and validate a simplified algorithm for LVDD grading after coronary artery bypass graft (CABG) surgery.
- To compare the grading capacity and prognostic value of a simplified LVDD algorithm versus a comprehensive one.
- To assess the association between LVDD grades and long-term major adverse cardiac events (MACE).
Main Methods:
- Intraoperative transesophageal echocardiography data from 905 CABG patients were analyzed.
- Two algorithms for LVDD grading were compared: a comprehensive four-variable (A) and a simplified two-variable (B) approach.
- Algorithm B utilized transmitral early flow velocity and early mitral annular tissue velocity.
Main Results:
- Algorithm B graded 99% of patients (895/905), significantly more than algorithm A (62%, 563/905).
- LVDD graded by algorithm B was significantly associated with MACE (p=0.013), unlike algorithm A (p=0.79).
- Patients with the highest MACE incidence were ungradable with the comprehensive algorithm A.
Conclusions:
- A simplified LVDD algorithm significantly increases the number of assessable patients after CABG.
- The simplified algorithm is valid for risk prediction, correlating with long-term MACE.
- This two-variable LVDD grading method offers a practical approach for similar patient populations.
Background:
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).
Methods:
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.
Results:
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.
Conclusions:
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.
