According to co-author Christian Martin-Gill, M.D., M.P.H., chief of the Emergency Medical Services (EMS) division at UPMC, the algorithm will help EMS personnel and emergency department providers identify people having a heart attack and those with reduced blood flow to the heart in a much more robust way compared with traditional ECG analysis.
“This information can help guide EMS medical decisions such as initiating certain treatments in the field or alerting hospitals that a high-risk patient is incoming,” Martin-Gill added. “On the flip side, it’s also exciting that it can help identify low-risk patients who don’t need to go to a hospital with a specialized cardiac facility, which could improve prehospital triage.”
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In the next phase of this research, the team is optimizing how the model will be deployed in partnership with City of Pittsburgh Bureau of Emergency Medical Services. Al-Zaiti said that they’re developing a cloud-based system that integrates with hospital command centers that receive ECG readings from EMS. The model will analyze the ECG and send back a risk assessment of the patient, guiding medical decisions in real-time.
Other authors who contributed to this research were Zeineb Bouzid, Stephanie Helman, M.S.N., R.N., Nathan Riek, Karina Kraevsky-Phillips, M.A., R.N., Gilles Clermont, M.D., Murat Akcakaya, Ph.D., Susan Sereika, Ph.D., Samir Saba, M.D., and Clifton Callaway, M.D., Ph.D., all of Pitt; Jessica Zègre-Hemsey, Ph.D., R.N., of the University of North Carolina; Ziad Faramand, M.D., of Northeast Georgia Health System; Mohammad Alrawashdeh, Ph.D., of Harvard Medical School; Richard Gregg, M.S., of Philips Healthcare; Peter Van Dam, of University Medical Center Utrecht; Stephen Smith, M.D., of Hennepin Healthcare and the University of Minnesota; and Yochai Birnbaum, M.D., of Baylor College of Medicine.
This research was supported by the National Heart, Lung, and Blood Institute, the National Center for Advancing Translational Sciences and the National Institute for Nursing Research through grants R01HL137761, UL1TR001857, K23NR017896 and KL2TR002490.
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