Abstract: FR-PO320
Risk-Based Implementation of SGLT2 Inhibitors: Insights from the CANVAS Program and CREDENCE Trial
Session Information
- Diabetic Kidney Disease: Clinical Modeling, Diagnosis, Education, and More
October 25, 2024 | Location: Exhibit Hall, Convention Center
Abstract Time: 10:00 AM - 12:00 PM
Category: Diabetic Kidney Disease
- 702 Diabetic Kidney Disease: Clinical
Authors
- Yeung, Emily K., Guy's and St Thomas' NHS Foundation Trust, London, London, United Kingdom
- Ferguson, Thomas W., University of Manitoba Faculty of Health Sciences, Winnipeg, Manitoba, Canada
- Tangri, Navdeep, University of Manitoba Faculty of Health Sciences, Winnipeg, Manitoba, Canada
- Vaduganathan, Muthiah, Washington DC VA Medical Center, Washington, District of Columbia, United States
- Arnott, Clare Gabrielle, The George Institute for Global Health, Sydney, New South Wales, Australia
- Jun, Min, The George Institute for Global Health, Sydney, New South Wales, Australia
- Kotwal, Sradha S., The George Institute for Global Health, Sydney, New South Wales, Australia
- Jardine, Meg, NHMRC Clinical Trials Centre, Camperdown, New South Wales, Australia
- Perkovic, Vlado, The George Institute for Global Health, Sydney, New South Wales, Australia
- Heerspink, Hiddo Jan L., The George Institute for Global Health, Sydney, New South Wales, Australia
- Neuen, Brendon Lange, The George Institute for Global Health, Sydney, New South Wales, Australia
Background
The Kidney Disease Improving Global Outcomes (KDIGO) 2024 guideline for the evaluation and management of chronic kidney disease (CKD) recommends using a validated risk score to estimate absolute risk of kidney failure (1A recommendation). Given that those at highest risk of CKD progression are also at highest risk for cardiovascular outcomes, we sought to determine if a risk-based approach would identify those who benefit most from a cardio-kidney perspective with SGLT2 inhibition.
Methods
In this post-hoc individual participant data analysis of the CANVAS program and CREDENCE trial, we categorized participants according to risk of CKD progression using the KDIGO classification of CKD, Klinrisk algorithm, and the Kidney Failure Risk Equation (restricted to participants with eGFR <60 mL/min1/1.73m2). Effects of canagliflozin on a cardio-kidney composite outcome of 40% decline in eGFR, kidney failure or death due to cardiovascular or kidney disease were analyzed using Cox and Poisson regression models.
Results
Across higher kidney risk categories, participants were more likely to have lower eGFR, higher urine albumin:creatinine ratio, higher systolic blood pressure, and longer duration of diabetes (all p<0.0001). Overall, canagliflozin reduced the relative risk of the cardio-kidney composite outcome by 26% (HR 0.74, 95% CI 0.67-0.82). The relative benefits of canagliflozin were at least as large across higher kidney risk categories (Figure; Panel A). Absolute risk reductions were largest in participants at highest baseline risk (Figure; Panel B).
Conclusion
The use of validated kidney risk scores can accurately identify those who benefit most from SGLT2 inhibition with canagliflozin.