Estimating the population-level kidney benefits of improved uptake of SGLT2 inhibitors in patients with chronic kidney disease in Australian primary care
Bibliographic record
Abstract
Abstract Background Although 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. Methods Using 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. Results In 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. Conclusions Improved 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".