{"id":909,"date":"2019-05-31T11:15:48","date_gmt":"2019-05-31T11:15:48","guid":{"rendered":"https:\/\/digitone.news\/?p=909"},"modified":"2019-06-04T17:31:56","modified_gmt":"2019-06-04T17:31:56","slug":"medial-early-sign-releases-diabetes-predictive-software","status":"publish","type":"post","link":"https:\/\/digitone.news\/index.php\/2019\/05\/31\/medial-early-sign-releases-diabetes-predictive-software\/","title":{"rendered":"Medial Early Sign Releases Diabetes Predictive Software"},"content":{"rendered":"\n
\"Diabets\"<\/figure>\n\n\n\n

Medial EarlySign (earlysign.com), a leader in machine-learning based solutions to aid in early detection and prevention of high-burden diseases, 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.  <\/p>\n\n\n\n

The initial suite includes EarlySign’s Pre2D AlgoMarker™ solution to identify pre-diabetic 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.<\/p>\n\n\n\n

“In the U.S. alone, approximately 1.5 million pre-diabetic 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.”<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

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 \u2013 and requiring no special patient preparation \u2014 it flags those pre-diabetic patients at high risk for progressing to diabetes in one (1) year or less. In a retrospective data study of 1.1 million pre-diabetic patients, the Pre2D AlgoMarker flagged the top 10% of the pre-diabetic 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.<\/p>\n\n\n\n

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.
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Medial EarlySign (earlysign.com), a leader in machine-learning based solutions to aid in early detection and prevention of high-burden diseases, 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 […]<\/p>\n","protected":false},"author":1,"featured_media":910,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[48,45],"tags":[],"class_list":{"0":"post-909","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-medicaldevice","8":"category-newproduct"},"_links":{"self":[{"href":"https:\/\/digitone.news\/index.php\/wp-json\/wp\/v2\/posts\/909","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/digitone.news\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/digitone.news\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/digitone.news\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/digitone.news\/index.php\/wp-json\/wp\/v2\/comments?post=909"}],"version-history":[{"count":2,"href":"https:\/\/digitone.news\/index.php\/wp-json\/wp\/v2\/posts\/909\/revisions"}],"predecessor-version":[{"id":942,"href":"https:\/\/digitone.news\/index.php\/wp-json\/wp\/v2\/posts\/909\/revisions\/942"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/digitone.news\/index.php\/wp-json\/wp\/v2\/media\/910"}],"wp:attachment":[{"href":"https:\/\/digitone.news\/index.php\/wp-json\/wp\/v2\/media?parent=909"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/digitone.news\/index.php\/wp-json\/wp\/v2\/categories?post=909"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/digitone.news\/index.php\/wp-json\/wp\/v2\/tags?post=909"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}