Diabetic Nephropathy

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Diabetic Nephropathy

Risk Modeling for Diabetic Nephropathy CKD

Kidney problems are one of the most common diabetes-related complications; approximately 20% – 40% of all diabetics worldwide will be affected by diabetic nephropathy, or renal dysfunction. Between 2005 – 2008, 6.9 million U.S. adult Type 1 and Type 2 diabetics (roughly 34%) were estimated to have diabetic nephropathy. This includes chronic kidney disease (CKD) as well as end stage renal disease (ESRD). Diabetes is the leading cause of ESRD in the United States, Europe, and Japan. Pooled clinical data from 54 countries show that at least 80% of ESRD cases are caused by diabetes, hypertension, or a combination of both.


diabetic nephropathy risk
This means that every opportunity to intervene and delay or prevent diabetes can play an important role in reducing diabetic nephropathy risks. That is why we are working on a way for healthcare organizations (HCOs) to predict and quantify these risks before diabetes-related CKD symptoms occur.


Predictive Risk Modeling for Diabetic Nephropathy

Medial EarlySign is developing a machine learning-based AI algorithms that can identify diabetic subpopulations at high risk for developing diabetic nephropathy. The algorithm is a machine learning and artificial intelligence-based clinical risk predictor designed to aid HCO in identifying which diabetic patients are at high risk for developing chronic kidney disease (CKD) within a specific time period. The model enables HCOs to allocate their resources more effectively, timely intervene with diabetics at high risk for nephropathy, and work towards improved patient outcomes.


Our work utilizes advanced algorithms and predictive analytics to deliver diabetic kidney disease risk profiles using our proprietary machine learning technology. The model examines EHR and lab data to find hidden anomalies in diabetic patient data that indicate signs of high risk for kidney disease. This gives health care providers opportunities to intervene early and treat diabetics at risk to potentially delay or halt the progression of CKD, even before its onset.

Retrospective Study

In a retrospective clinical data study, Medial EarlySign’s model analyzed a variety of factors for CKD, to research data from a cohort of more than 500,000 diabetic patients to predict who might be at high risk for developing CKD within a specific time frame. Our diabetic nepropathy algorithm identified more people at high risk for developing CKD.


Contact us for more details and information about Medial EarlySign’s diabetic nephropathy algorithm research.