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Abstract: INFO09-FR

Improving the Quality of CKD Care with Risk Prediction and Personalized Recommendations: The GEMINI Project

Session Information

  • Informational Posters - II
    November 03, 2023 | Location: Exhibit Hall, Pennsylvania Convention Center
    Abstract Time: 10:00 AM - 12:00 PM

Category: CKD (Non-Dialysis)

  • No subcategory defined


  • Tangri, Navdeep, University of Manitoba Max Rady College of Medicine, Winnipeg, Manitoba, Canada
  • Leon Mantilla, Silvia Juliana, University of Manitoba Max Rady College of Medicine, Winnipeg, Manitoba, Canada
  • Fatoba, Samuel T., Bayer AG, Leverkusen, Nordrhein-Westfalen, Germany
  • Pergola, Pablo E., Renal Associates PA, San Antonio, Texas, United States

Background: Chronic Kidney Disease (CKD) affects 1 in 7 Americans and can lead to progression to dialysis, cardiovascular disease, and early mortality. Effective interventions exist to slow the progression of CKD and prevent heart failure, but implementation remains a challenge, and use of guideline recommended testing and therapies remain low. Routine, complete collection of guideline recommended blood and urine tests allowing accurate risk prediction with personalized treatment recommendations can improve CKD care, when integrated into clinical workflow.
To implement risk prediction algorithms and clinical decision support for identifying patients at risk of CKD progression and improving quality of CKD care in 5 large nephrology practices representing more than 200 nephrologists and 100,000 patients with CKD. Data from the first practice representing more than 30 providers and 20,000 patients is presented here.
Methods and Results:
Data for estimated glomerular filtration rate, albuminuria, demographics, other laboratory tests and comorbid conditions was extracted from the electronic health record (EHR) and clinical notes. Individuals were risk stratified using an externally validated risk prediction equation (Klinrisk). A dashboard with disease specific educational information, and personalized diagnostic and treatment recommendations was created. The first focus of quality improvement was on albuminuria (UACR) testing. Baseline rates of UACR or UPCR testing were adequate (78%), but the majority of testing was based on UPCR. In the six months following the intervention, UACR rates increased by 66%, whereas UPCR rates increased by 2%. Early positive changes in prescription of goal directed therapy were also observed.
Conclusion: Integration of a highly accurate machine model for CKD progression when paired with EHR linked clinical decision support improves guideline recommended testing in patients with CKD. Enrollment of additional sites and longer follow up will be needed to observe changes in goal directed medical therapy and patient outcomes.


  • Bayer US.