Prevalence of <scp>SGLT2</scp> inhibitor and <scp>GLP1</scp> receptor agonist prescriptions in type 2 diabetes patients with and without chronic kidney disease: Analysis of an Australian primary care dataset
Bibliographic record
Abstract
Abstract Aims Sodium‐glucose co‐transporter 2 inhibitors (SGLT2 inhibitors) and glucagon‐like peptide‐1 receptor agonists (GLP1‐RA) have cardio‐kidney‐metabolic benefit. This study aimed to understand the prevalence of prescription of SGLT2 inhibitors and GLP1‐RA among patients with T2DM with and without chronic kidney disease (CKD) in Australian primary care. Materials and Methods We conducted a retrospective, cohort study of adults with T2DM who attended primary care practices participating in MedicineInsight. The outcome of interest was the percentage of patients with CKD who received ≥1 prescription for an SGLT2 inhibitor or a GLP1‐RA (assessed separately) compared to those without CKD during 2020–2021. We also assessed prescriptions in a sub‐population of CKD patients who met trial inclusion criteria: SGLT2 inhibitor (estimated glomerular filtration rate [eGFR] ≥20 mL/min/1.73m 2 and urine albumin‐creatinine ratio [UACR] ≥22.6 mg/mmol) and GLP1‐RA (eGFR ≥50 to <75 mL/min/1.73m 2 and UACR >33.9 to <565 mg/mmol, or eGFR ≥25 to <50 mL/min/1.73m 2 and UACR >11.3 to <565 mg/mmol). Results Of 114 499 adults with T2DM, 36 840 (32.1%) also had CKD. SGLT2 inhibitors were prescribed in 13.6% of patients without CKD, 14.4% of patients with CKD and 17.8% of patients meeting the trial target population definition. Across these groups, GLP1‐RA were prescribed in 8.8%, 10.1% and 11.3% of patients, respectively. More advanced CKD stage and severely increased albuminuria were associated with a lower likelihood of SGLT2 inhibitor or GLP1‐RA being prescribed. Conclusion We observed low rates of SGLT2 inhibitor and GLP1‐RA prescriptions in patients with T2DM irrespective of CKD status. Strategies are needed to improve prescription rates, with a particular focus on patients with high kidney and cardiovascular risk.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 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".