Improve Chronic Kidney Disease Care with Predictive AI Analytics

Detect CKD risk from clinical variables and prioritize patients for enhaced care and cost reduction.

Problem

Chronic Kidney Disease (CKD) affects approximately 37 million adults in the US, and another group of 20-25 million are estimated to be at risk of developing it (1). Despite the serious health consequences and high costs, particularly in advanced stages, where they can increase fivefold (2), the disease often goes unnoticed; 9 out of 10 adults with CKD are unaware of their condition (1). Furthermore, among those who also have diabetes, 25% progress rapidly to a severe stage within two years (1). Meanwhile, 660,000 people face total kidney failure (3), further highlighting the importance of early detection and effective management strategies (4).

Why it matters

Chronic Kidney Disease (CKD) affects approximately 37 million adults in the US, with 20-25 million more at risk.
9 out of 10 adults with CKD are unaware of their condition, and medical costs can increase fivefold in advanced stages.
25% of CKD patients with diabetes progress to severe stages within two years, and 660,000 people face total kidney failure.

Solution

“KidneyCare AI”, a predictive model, has been designed to predict CKD risk by analyzing clinical and lifestyle factors. This predictive tool allows healthcare professionals to more effectively identify at-risk individuals, tailor interventions, and proactively manage the disease to improve patient outcomes.

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Datasources

The model's synthetic data set includes health variables such as blood glucose levels, blood pressure readings, age, and other key indicators of kidney health. Studies from the USRDS (1) and the NIH NIDDK (3) helped shape the variables used in the model to reflect real-world clinical scenarios, improving its predictive reliability for CKD.

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Citations

USRDS. “2016 USRDS Annual Data Report: Epidemiology of Kidney Disease in the United States.” National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, 2016. United States Renal Data System. 2016 USRDS annual data report: Epidemiology of kidney disease in the United States. National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, 2016.
Golestaneh, Alvarez L., et al. “All-Cause Costs Increase Exponentially With Increased Chronic Kidney Disease Stage.” The American Journal of Managed Care, vol. 23, no. 10 Suppl, 2017, pubmed.ncbi.nlm.nih.gov/28978205/. Accessed 5 June 2020.
National Institute of Diabetes and Digestive and Kidney Diseases. “Kidney Disease Statistics for the United States | NIDDK.” National Institute of Diabetes and Digestive and Kidney Diseases, 9 Mar. 2021. Accessed 5 June 2020.
Vassalotti JA¡DeVinney R;Lukasik S¡McNaney S¡Montgomery E¿Voss C;Winn D. “CKD Quality Improvement Intervention With PCMH Integration: Health Plan Results." The American Journal of Managed Care, vol. 25, no. 11, 2019, pubmed.ncbi.nlm.nih.gov/31747237). Accessed 5 June 2020.