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Updated: Jan 11, 2026

Noninvasive Determination of Vortex Formation Time Using Transesophageal Echocardiography During Cardiac Surgery
Published on: November 28, 2018
Evaluating the Clinical Agreement Between Corrected Left Ventricular Ejection Time and Corrected Carotid Flow Time in
Esmée C de Boer1, Frederique M de Raat1, Catarina Dinis Fernandes2
1Department of Electrical Engineering, Technical University of Eindhoven, Eindhoven, The Netherlands; Department of Anesthesiology, Catharina Hospital, Eindhoven, The Netherlands.
Insights
Manually measured corrected carotid flow time (ccFT) closely aligns with corrected left ventricular ejection time (cLVET) in CABG patients. This non-invasive method offers a promising alternative for cardiac contractility assessment during surgery.
Area of Science:
- Cardiology
- Anesthesiology
- Medical Devices
Background:
- Perioperative hemodynamic monitoring is vital for reducing complications in coronary artery bypass graft (CABG) surgery.
- Corrected left ventricular ejection time (cLVET) assesses cardiac contractility but requires specialized echocardiography skills.
- Corrected carotid flow time (ccFT), a non-invasive Doppler method, presents a potential alternative to cLVET.
Purpose of the Study:
- To evaluate the clinical agreement between ccFT and cLVET in patients undergoing CABG surgery.
- To determine if ccFT can serve as a viable surrogate for cLVET in a real-world clinical setting.
Main Methods:
- Prospective observational study including 17 adult CABG patients.
- Measurements of cLVET via transesophageal echocardiography and ccFT via carotid ultrasound.
- Analysis using Bazett's and Wodey's formulas, with both manual and automated quantification.
Main Results:
- Manual ccFT quantification showed strong correlation with cLVET (r=0.88-0.96, p<0.001).
- Acceptable bias (<11 ms) and narrow limits of agreement were observed for manual ccFT.
- Manual ccFT demonstrated good concordance (91-93%) with cLVET, superior to automated analysis.
Conclusions:
- Manually computed ccFT exhibits strong clinical agreement with cLVET in CABG patients.
- Manually derived ccFT is a potential surrogate for cLVET, offering a less invasive monitoring option.
- Optimization of automated algorithms for ccFT is necessary for future clinical integration.
Objective:
Perioperative hemodynamic monitoring plays a crucial role in reducing complications during coronary artery bypass graft (CABG) surgery. Corrected left ventricular ejection time (cLVET) is used to assess cardiac contractility but requires specialized echocardiography expertise. Corrected carotid flow time (ccFT), a non-invasive carotid Doppler-based method, could serve as an alternative, but its agreement with cLVET in CABG patients remains uncertain. This study evaluates the clinical agreement between these two measures in a real-world clinical setting.
Methods:
In this prospective observational study, 17 adult patients undergoing CABG surgery were included. cLVET was measured using transesophageal echocardiography, while ccFT was assessed via carotid ultrasound, both before incision and after thoracic closure. All values were calculated using Bazett's and Wodey's formulas, manually and through an automated algorithm. Correlation, Bland-Altman, and concordance analyses were conducted to evaluate the relationships between the measures.
Results:
Manual quantification showed better clinical agreement than automated analysis. Manually-derived ccFT values showed a strong correlation with cLVET using Bazett's formula (r = 0.88, 95% CI [0.77-0.94], p < 0.001) and Wodey's formula (r = 0.96, 95% CI [0.91-0.98], p < 0.001), with an acceptable bias (<11 ms) and narrow limits of agreement (Bazett: -35 to 57 ms; Wodey: -15 to 35 ms). Concordance was acceptable for Bazett (91%) and good for Wodey (93%).
Conclusion:
While automated quantification yielded poor clinical agreement, manually computed ccFT demonstrated a strong clinical agreement with cLVET. This suggests that manually derived ccFT values could serve as a viable surrogate for cLVET. Further effort is needed to optimize automated algorithms for future clinical applications.
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