Use of SGLT2 Inhibitors and Glucagon-Like Peptide 1 (GLP-1) Receptor Agonists in Australian Primary Care Patients with Type 2 Diabetes Mellitus (T2DM) Stratified by CKD Status
Bibliographic record
Abstract
Background: SGLT2i and GLP1-RA reduce the risk of kidney failure and cardiovascular events in patients with T2DM and CKD. We aim to understand their use in Australian primary care patients with T2DM by CKD status. Methods: We identified adults (>18 years) with T2DM who attended 1 of 392 general practices participating in a national quality improvement program (MedicineInsight) and had ≥1 eGFR measure between 2011-2019. CKD was defined according to KHA-CARI/KDIGO guidelines. Outcomes assessed were ≥1 prescription for SGLT2i (if eGFR >20ml/min/1.73m2) or GLP1-RA (if eGFR >15ml/min/1.73m2) during 2020-2021, by CKD status. Two secondary analyses were conducted; SGLT2i use in patients with CKD with UACR >22.6mg/mmol, and GLP1-RA use in those meeting CKD outcome trial enrolment criteria (NCT03819153; see figure for eGFR/UACR definitions). Results: 114,499 adults with T2DM were included (113,563 and 113,940 with eGFR >20 and >15mL/min/1.73m2, respectively), with a median age of 68, of whom 45% were female, and 32% had CKD. SGLT2i were prescribed 13% in patients without CKD, 14% with CKD and 17% with CKD and UACR >22.6mg/mmol (p values vs. no CKD: <0.05). GLP1-RA were prescribed in 8% of patients without CKD, 10% with CKD and 11% meeting CKD outcome trial inclusion criteria (p values vs. no CKD: <0.05). Conclusion: In this large, contemporary primary care cohort of patients with T2DM, SGLT2i and GLP1-RA prescription rates were higher in CKD than those without, but relatively low across both groups. Implementation strategies to improve medication uptake, with a focus on those who are likely to derive greatest benefit are needed. Funding: Commercial Support - The Renal Division of The George Institute for Global Health has received sponsorship funding provided by Boehringer Ingelheim and Eli Lilly Alliance and is supported by the University of New South Wales Scientia Program. The design, analysis, interpretation or writing of this work was performed independent of all funding bodies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".