Subjects: Product/Service, Business Update
AgileMD receives FDA clearance for eCART to predict in-hospital clinical deterioration across medical conditions
AgileMD, Inc., an AI-powered clinical decision support company, announced today that the U.S. Food and Drug Administration (FDA) has granted 510(k) marketing clearance for its eCART Clinical Deterioration Suite. eCART is a research-based, AI-driven software as a medical device (SaMD) that utilizes a machine learning algorithm to continuously assess hospitalized patients' risk of impending death or intensive care unit (ICU) transfer, assisting medical staff in swift and accurate recognition of patients requiring increased medical attention. This clearance was based on clinical performance data in nearly two million hospitalizations from 21 hospitals, including an unprecedented level of real-world prospective data, to ensure consistent accuracy across geography, age, race, and top medical conditions.
Approximately ten percent of hospitalized patients deteriorate during their hospital stay.i Delays in escalation of care are associated with increased mortality and length of stay.ii While sepsis is the leading cause of clinical deterioration in high-risk patients, over half deteriorate for other reasons.iii As an all-cause clinical deterioration prediction device, eCART captures risk across all ward patients, including those who are septic, allowing clinical teams to utilize a more streamlined approach to assessing the patients on their watch.
"eCART was designed to maximize early identification of at-risk patients, minimize false alarms and decrease clinician workload, rather than add to it," said Dr. Dana Edelson, Chief Medical Officer and co-founder of AgileMD. "We do that by marrying a best-in-class analytic with a highly intuitive user interface and well-tested clinical workflow, giving front line clinical teams the confidence and ability to escalate care when appropriate."
eCART is the product of over a decade of research at the University of Chicago. The software is directly embedded into the electronic health record (EHR) and leverages up to 97 real-time variables, including labs, vital signs, and nursing assessments, to generate an eCART score and risk designation. From there, clinical staff are guided to embedded clinical pathways for care evaluation and management.
"This landmark clearance delivers on the need and the importance of real-world clinical performance data," said Borna Safabakhsh, AgileMD's CEO and co-founder. "We believe that should be the standard for all AI-powered clinical models to ensure trust and safety."
About AgileMD
At AgileMD, we are driven to improve patient outcomes by making evidence-based care universally accessible. Since our founding at the University of Chicago, our work has been evaluated in 80 peer-reviewed publications and we have received nearly $3M in federal grant funding from the National Institutes of Health (NIH) and the Department Health & Human Services (HHS). AgileMD is backed by premier startup partners MATTER, Y Combinator, Rock Health and the Polsky Center for Entrepreneurship and Innovation. Our early warning and clinical pathways products have been used by over 135,000 providers in over 250 U.S. hospitals in the care of more than 4 million patient encounters.
This project has been funded in part with Federal funds from the Department of Health and Human Services (HHS); administration for Strategic Preparedness and Response; Biomedical Advanced Research and Development Authority (BARDA), Division of Research Innovation and Ventures under Contract No. 75A50121C00043, with additional funding support from the National Institutes of Health (NIH), including the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under Award Number R44EB030955 and the National Heart, Lung and Blood Institute (NHLBI) under Award Number R01HL157262. The content is solely the responsibility of the company and does not necessarily represent the official views of the National Institutes of Health.
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i Byrd TF, Southwell B, Ravishankar A, et al. Validation of a Proprietary Deterioration Index Model and Performance in Hospitalized Adults. JAMA Netw Open. 2023;6(7):e2324176. doi:10.1001/jamanetworkopen.2023.24176
ii Churpek MM, Wendlandt B, Zadravecz FJ, Adhikari R, Winslow C, Edelson DP. Association between intensive care unit transfer delay and hospital mortality: A multicenter investigation. J Hosp Med. 2016;11(11):757-762. doi:10.1002/jhm.2630
iii Churpek MM, Ingebritsen R, Carey KA, et al. Causes, Diagnostic Testing, and Treatments Related to Clinical Deterioration Events among High-Risk Ward Patients. Preprint. medRxiv. 2024;2024.02.05.24301960. Published 2024 Feb 6. doi:10.1101/2024.02.05.24301960
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