HOD HASHARON, Israel, May 29, 2019 /PRNewswire/ — Medial EarlySign (earlysign.com), a leader in machine-learning based solutions to aid in early detection and prevention of high-burden diseases, today announced its first suite of diabetes risk predictors for healthcare organizations. Expanding the company’s portfolio of clinical risk predictors, these new diabetes-focused AlgoMarkers are designed to help healthcare systems identify and engage patients at high risk for diabetes and downstream complications.
The initial suite includes EarlySign’s Pre2D AlgoMarker™ solution to identify prediabetic patients at highest risk of progressing to diabetes within a one-year period; and the Diabetes to CKD AlgoMarker™, which identifies type 2 diabetic patients at high risk for developing stage 2-4 chronic kidney disease (CKD) within three years.
“In the U.S. alone, approximately 1.5 million prediabetic adults will become diabetic this year, while between 20% and 40% of diabetic patients worldwide suffer from diabetes-related kidney complications,” said Ori Geva, CEO of Medial Early Sign. “Our Pre2D™ and Diabetes to CKD™ solutions provide healthcare systems opportunities to identify and reach out to high-risk patients within an actionable timeframe, when preventative measures can be initiated, and resources allocated to potentially delay or prevent the onset of disease.”
EarlySign’s Pre2D™ predictive solution applies advanced machine learning-based algorithms to identify “hidden signals” residing in existing, routine blood tests. Factoring in age, gender and BMI – and requiring no special patient preparation – it flags those prediabetic patients at high risk for progressing to diabetes in one (1) year or less. In a retrospective data study of 1.1 million prediabetic patients, the Pre2D AlgoMarker flagged the top 10% of the prediabetic population at risk and successfully identified 58.3% of patients who became diabetic within a 12-month period. This is a 14.7% increase over a logistic regression model that, by flagging 10% of the population, identified only 43.6% of future diabetics.
The Diabetes to CKD™ risk predictor uses basic demographic data, routine lab results, diagnostic codes, and medication information to flag type 2 diabetic patients most likely to develop stages 2-4 of chronic kidney disease in 3 years or less. In a retrospective data study of hundreds of thousands of diabetic patients, the algorithm was able to capture 25.5% of those most likely to progress to CKD within three years, by flagging only 3% of the diabetic population. This amounts to 77% more patients than would have been identified if the last eGFR value was used.
Medial EarlySign will exhibit the Pre2D™ and Diabetes-to-CKD AlgoMarker™ solutions at the ADA’s 79th Scientific Sessions in San Francisco, CA, June 7-11 at Booth 1940.
About Medial EarlySign
Medial EarlySign helps healthcare systems with the early detection and prevention of high-burden diseases. Their suite of outcome-focused software solutions (AlgoMarkers™) find subtle, early signs of high-risk patient trajectories in existing lab results and ordinary EHR data already collected in the course of routine care. EarlySign’s AlgoMarkers help clients identify patients at high risk for conditions such as lower GI disorders, prediabetic progression to diabetes, downstream diabetic complications, chronic kidney disease (CKD), and first coronary artery disease (CAD) and equivalent events. As healthcare systems transition from volume-based to value-based care, EarlySign partners with healthcare organizations to support outcome-focused care delivery, while potentially preventing or delaying the onset of high-burden diseases, downstream complications, and their associated costs. The company’s machine learning platform has been supported by peer-reviewed research published by internationally recognized health organizations and hospitals. Founded in 2009, Medial EarlySign is headquartered in Hod Hasharon, Israel with US headquarters in Boston, MA. For more information, please visit http://us.earlysign.com/.
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SOURCE Medial EarlySign