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May 18, 2021
2 Min Read
Kristina Blokhin - stock.adobe.com
Insights into the use of cardiac ultrasound on COVID-19 patients and how AI-derived heart measurements were able to predict virus mortality were revealed at the ACC.21 Scientific Sessions.
Echocardiographic Correlates of in-hospital Death in Patients with Acute COVID-19 Infection: The World Alliance Societies of Echocardiography (WASE-COVID) Study, studied the crossover between COVID-19 and cardiac measurements among 870 patients from 13 medical centers in nine countries – Asia, Europe, United States, Latin America. Dr. Federico Asch, Director of the Cardiovascular and Echo Core Labs at MedStar Health Research Institute, and Dr. Roberto Lang, Director of the University of Chicago's Noninvasive Cardiac Imaging Laboratory, served as principal investigators.
Ultromics' cloud-based, AI platform was tapped to speedily review data from 13 medical centers in nine countries
"Knowing we had to move quickly to figure out if there was anything about heart health that we could associate with COVID-19 outcomes, we were motivated to secure an industry partner to help accelerate our analysis timeline," said Dr. Lang. "Using Ultromics' software to anonymize echocardiograms, upload them to a cloud platform, and use artificial intelligence to quickly and accurately analyze each exam was hugely beneficial in bringing this report forward while the world is still fighting this virus."
Key findings include:
10 of the 13 medical centers performed limited cardiac exams as their primary COVID in-patient practice, and just 3 out of the 13 centers performed comprehensive exams.
In-hospital mortality rates varied by region, 11% in Asia, 19% in Europe, 27% in Latin America, and 26% in U.S.
Left ventricular longitudinal strain (LV LS), right ventricle free wall strain (RV FWS), in addition to age at presentation, lactic dehydrogenase (LDH), and previous lung disease were independently associated with mortality, while left ventricle ejection fraction (LVEF) was not.
Fully automated quantification of LVEF and LVLS using AI minimized variability.
AI-based LV analyses, but not manual, were significant predictors of in-hospital and follow-up mortality.
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