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Predict Chronic Kidney Disease

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

Problem

Chronic Kidney Disease (CKD) is a complex condition in which patients experience excessive cardiovascular and other adverse events and carry a heavy burden of morbidity, mortality, and healthcare costs. An estimated 37 million people—15% of adults—have CKD and another 20–25 million are at risk for developing it (1).And yet, CKD remains under-recognized by providers and patients, especially in its early stages when patients are largely asymptomatic. Nine in ten adults with CKD are not aware of their condition and one in two people with extremely low kidney function do not know they have CKD (1). Healthcare costs increase dramatically as CKD progresses and in later stages, are five to ten times higher than for someone without CKD (2). These costs are primarily due to hospitalizations resulting from severe complications that often accompany reduced kidney function.

Why it matters

  • 660,000 people live with kidney failure (3).
  • 37 million adults in the U.S. have CKD, a number that has doubled each of the last two decades (1).
  • 9 in 10 adults with mild-to-moderate CKD are not aware of their condition and 25% of them (who also have diabetes) will experience rapid progression within two years (1).

Effective interventions can improve outcomes and reduce healthcare costs. For example, an intervention among beneficiaries of a Maryland health plan reduced hospital admissions by 30 to 45% (depending on CKD stage), readmissions by more than 70%, and costs by 20% (4).To expand the use of value-based programs for CKD, CMS recently announced the Advancing American Kidney Health initiative designed to increase value-based models starting in 2020 (5). AI-based models are ideally suited to help clinicians and care teams in value-based programs by identifying patients to help promote early diagnosis, slow CKD progression, and anticipate and avoid complications and adverse events.

Solution

AI can help you promote early diagnosis, slow the progression of CKD, and anticipate and avoid complications and adverse events. AI helps to identify people with undiagnosed CKD or at risk of rapid progression, predict adverse events due to poor medication adherence, predict CKD-related complications (eg, hyperkalemia), or identify patients who are likely to will start dialysis in the next year.

Datasources

  • Electronic Health Records: EHR data with comprehensive patient histories of vital signs and symptoms, problem lists and chief complaints, tests results, diagnoses and procedures, and prescriptions.
  • Remote Monitoring Data: Remote monitoring data capture key vital signs and health behaviors (e.g. blood pressure, heart rate, blood glucose, activity levels, etc.).
  • Social Determinants of Health (SDoH): Geo-centric data with details about the social and environmental influences on people’s health and outcomes.

Citations

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. “HHS To Transform Care Delivery for Patients with Chronic Kidney Disease | CMS.” Cms.Gov, Centers for Medicare 8: Medicaid Services, 10 July 2019, www.cms.gov/newsroom/press-releases/hhs- transform-care-delivery-patients-chronic-kidney- diseaset:-:text=Today%2C%20delivering%200n%20President%20Trump'sMedicare%20and%20M edicaid%20Innovation%20payment. Accessed 5 June 2020.

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