Kidney Week

Abstract: FR-PO1092

Improving the Quality of CKD Care with Risk Prediction and Personalized Recommendations: 1-Year Results from the GEMINI-RAPA Study

Session Information

• CKD: Epidemiology, Risk Factors, and Prevention - 2
  October 25, 2024 | Location: Exhibit Hall, Convention Center
  Abstract Time: 10:00 AM - 12:00 PM

Category: CKD (Non-Dialysis)

• 2301 CKD (Non-Dialysis): Epidemiology, Risk Factors, and Prevention

Authors

• Tangri, Navdeep, University of Manitoba Department of Internal Medicine, Winnipeg, Manitoba, Canada

Navdeep Tangri, MD, PhD

• Leon Mantilla, Silvia Juliana, Chronic Disease Innovation Centre, Winnipeg, Manitoba, Canada

Silvia Juliana Leon Mantilla, MD, MS, MSc

• Watts, Don, Khure Health Inc, Toronto, Ontario, Canada

Don Watts, MBA

• Woo, Cedric, Khure Health Inc, Toronto, Ontario, Canada

Cedric Woo

• Fatoba, Samuel T., Bayer AG, Leverkusen, Nordrhein-Westfalen, Germany

Samuel T. Fatoba, PhD, MPH

• Pergola, Pablo E., Renal Associates PA, San Antonio, Texas, United States

Pablo E. Pergola, MD, PhD

Background

CKD is associated with cardiovascular disease, progression to kidney failure and early mortality. Clinical guidelines recommend (ACEi, SGLT2i, non-steroidal MRA) to slow CKD progression and prevent heart failure in CKD-T2D patients Unfortunately, most patients are recognized late and use of guideline testing and therapies remain low. We implemented Klinrisk, a highly accurate risk prediction algorithm for CKD progression, with clinical decision support (CDS) to identify patients at risk with the goal of improving quality of CKD care in a large nephrology practice.

Methods

Patients >18 years and not on dialysis were included. Data for estimated glomerular filtration rate, albuminuria, demographics, other laboratory tests and comorbid conditions was extracted from the electronic health record (EHR). Individuals were risk stratified using an externally validated risk prediction equation (Klinrisk¹), deployed on Khure Health’s CDS platform Reports are generated quarterly to inform and educate physicians. One year data on changes in guideline directed care are presented here.

Results

Of 16,099 patients that were risk-stratified, 29% were at high risk of CKD progression. Higher risk individuals were similar in age, but more likely to have diabetes, hypertension and heart failure. At one year, UACR testing increased 3 fold from 12.3 to 38.8 %, and UACR/PCR values were available in 83 % of individuals. Among high risk patients (> 10 % risk of progression over 2 years) there was a 19% increase in prescription of RAASi, 96 % increase in prescription of SGLT2i and an 65 % increase in prescription of ns-MRAs.

Conclusion

Integration of a highly accurate machine model for CKD progression when paired with EHR linked clinical decision support improves guideline-recommended testing and therapy in high -risk patients with CKD. Longer follow up and periodic assessment is planned to observe changes in quality metrics and patient outcomes.

Funding

• Commercial Support – Bayer