Underuse of cardiorenal protective agents in high-risk diabetes patients in primary care: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Sodium-glucose cotransporter-2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP-1RA) have shown benefits in patients with diabetes and cardiovascular disease (CVD), heart failure (HF), and chronic kidney disease (CKD). OBJECTIVE: We assessed benchmark outcomes (Hemoglobin A1c, LDL-C, and blood pressure), identified the prevalence of cardiorenal indications for SGLT2i and GLP-1RA, and compared prescribing rates of GLP1-RA and SGLT2i in those with and without cardiorenal indications. METHODS: We analyzed data from January 2018-June 2019 for 7168 patients with diabetes using electronic medical records from the Northern Alberta Primary Care Research Network, a regional network of the Canadian Primary Sentinel Surveillance Network (CPCSSN). Patients with and without cardiorenal comorbidities were compared using descriptive statistics and two proportion Z tests. RESULTS: Hemoglobin A1c ≤ 7.0% was met by 56.8%, blood pressure < 130/80 mmHg by 62.1%, LDL-C ≤ 2.0 mmol/L by 45.3% of patients. There were 4377 patients on glucose lowering medications; metformin was most common (77.7%), followed by insulin (24.6%), insulin secretagogues (23.6%), SGLT2i (19.7%), dipeptidyl peptidase-4 inhibitor (19.3%), and GLP-1RA (9.4%). A quarter of patients had cardiorenal indications for SGLT2i or GLP-1RA. Use of SGLT2i in these patients was lower than in patients without cardiorenal comorbidities (14.9% vs 21.2%, p < 0.05). GLP-1RA use in these patients was 4.6% compared with 11% in those without cardiorenal comorbidities (p < 0.05). DISCUSSION: Contrary to current evidence and recommendations, SGLT2i and GLP1-RA were less likely to be prescribed to patients with pre-existing CVD, HF, and/or CKD, revealing opportunities to improve prescribing for patients with diabetes at high-risk for worsening cardiorenal complications.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".