Observational studyCardiovascular ultrasound2026
Computer modeling to improve measurement of stroke volume with echocardiography.
Observational study in Cardiovascular ultrasound, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
6 authors.
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Abstract
backgroundThe stroke volume (SV) can be measured by a human expert (HE) using the left ventricular outflow tract diameter (LVOTd) and its velocity time integral (VTI) with echo. If the SV is known, the cardiac output and systemic vascular resistance can be calculated. We have previously described a machine learning computer model (CM) to estimate the LVOTd. The accuracy of LVOTd
methodOver 20 months, a prospective observational study was done in patients with a PAC placed for clinical indications. Participants underwent an echocardiogram, and at the same time the SV was measured by PAC. The SV with echo was calculated using the LVOTd
resultsComputer modeling improves measurement of stroke volume with echocardiography. Eighty-four patients with a PAC were enrolled. With human expert measurement of the LVOTd and the VTI, the SV could be calculated in 59 (70%), and with the LVOTd
conclusionsA machine learning model of the LVOTd allows accurate calculation of the SV using only the VTI, simplifying the assessment while increasing the yield by 22%. Machine learning tools can lower the bar for obtaining quantitative metrics with echo. Adding objective and repeatable measures will improve the ability of echo to guide therapy in the critically ill.
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