Estimating the population-level kidney benefits of improved uptake of SGLT2 inhibitors in patients with chronic kidney disease in Australian primary care
Neuen, B. L.; Jun, M.; Wick, J.; Kotwal, S.; Badve, S. V.; Jardine, M. J.; Gallagher, M.; Chalmers, J.; Nallaiah, K.; Perkovic, V.; Peiris, D.; Rodgers, A.; Woodward, M.; Ronksley, P.
Show abstract
BackgroundAlthough sodium glucose co-transporter 2 (SGLT2) inhibitors reduce the risk of kidney failure and death in patients with chronic kidney disease (CKD), they are underused in routine clinical practice. We evaluated the number of patients with CKD in Australia that would be eligible for treatment with an SGLT2 inhibitor and estimated the number of cardiorenal and kidney failure events that could be averted with improved uptake of SGLT2 inhibitors. MethodsUsing nationally-representative Australian primary care data (MedicineInsight), we identified patients that would have met inclusion criteria of the CREDENCE, DAPA-CKD, and EMPA-KIDNEY trials between 1 January 2020 and 31 December 2021. We applied these data to age and sex-stratified estimates of CKD prevalence from the broader Australian population (using national census data) to generate population-level estimates for: (1) the number of CKD patients eligible for treatment with SGLT2 inhibitors and (2) the annual number of potentially preventable cardiorenal (CKD progression, kidney failure, or death due to cardiovascular disease or kidney failure), and kidney failure events with SGLT2 inhibitors based on trial event rates. ResultsIn MedicineInsight, 44.2% of adults with CKD would have met CKD eligibility criteria for an SGLT2 inhibitor; baseline use was 4.1%. Applying these data to the broader Australian population, we estimated 230,246 patients with CKD in Australia would have been eligible for treatment with any SGLT2 inhibitor. Optimal implementation of SGLT2 inhibitors (75% uptake in eligible patients) could reduce cardiorenal and kidney failure events annually in Australia by 3,644 (95% CI 3,526-3,764) and 1,312 (95% CI 1,242-1,385), respectively. ConclusionsImproved uptake of SGLT2 inhibitors for patients with CKD in Australian primary care has the potential to prevent large numbers of patients experiencing CKD progression or dying due to cardiovascular or kidney disease. Identifying strategies to increase the uptake of SGLT2 inhibitors is critical to realising the population-level benefits of this drug class.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- External validation of six clinical models for prediction of unknown chronic kidney disease in a German population 94%
- COVID-19 in patients undergoing renal replacement therapy in Scotland: findings and experience from the Scottish Renal Registry 93%
- Trajectories of atherosclerotic cardiovascular disease risk scores as a predictor for incident chronic kidney disease 92%
Similar papers in this journal
- Prognostic Utility of Total Kidney Volume for Chronic Kidney Disease Risk Prediction: An Observational and Mendelian Randomization Study 95%
- Clonal hematopoiesis of indeterminate potential contributes to accelerated chronic kidney disease progression 94%
- Heterogeneous treatment effects of intensive glycemic control on kidney microvascular outcomes in ACCORD 94%
Similar papers in this journal
- The prevalence of chronic kidney disease in Australian primary care: analysis of a national general practice dataset 98%
- ATP-citrate lyase as a therapeutic target in chronic kidney disease: a Mendelian Randomization analysis 94%
- Shared Decision-Making in Renal Replacement Therapy Selection: Patient Perceptions, Preferences, and Influencing Factors in a Nationwide Cross-Sectional Study in Japan 91%
Similar papers in this journal
- Comparison of low eGFR prevalence and prediction for mortality using 2009 and 2021 CKD-EPI equations in Mexican adults 93%
- Impaired incretin homeostasis in non-diabetic moderate-severe CKD 92%
- Circulating Plasma Biomarkers in Biopsy-Confirmed Kidney Disease: Results from the Boston Kidney Biopsy Cohort 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.