Abstract 9920: Utilizing Synchronous Healthcare Delivery to Optimize the Use of Guideline Directed Medical Therapies in Patients With Type 2 Diabetes: Results From the DECIDE-CV Clinic
Bibliographic record
Abstract
Introduction: The high burden of comorbidities among patients with Type 2 Diabetes (T2D) may contribute to the low use of guideline directed medical therapies (GDMT) that improve CV outcomes, including sodium glucose cotransporter-2 inhibitors (SGLT2i) and glucagon-like-peptide-1 receptor agonists (GLP1RA). Hypothesis: The DECIDE-CV clinic at McGill University (Montreal, Canada) is a novel synchronous healthcare program whereby patients with T2D are seen at each visit simultaneously by a cardiologist, endocrinologist, and nephrologist to enable rapid GDMT implementation. We hypothesized that synchronous healthcare delivery would increase SGLT2i and GLP1RA use among multimorbid patients with T2D. Methods: We conducted a pre/post analysis of GDMT use throughout patient follow-up in the DECIDE-CV clinic. We evaluated the first 76 patients (2020-10-26 to 2022-04-18) and used Canadian diabetes/CV guidelines with Quebec medication coverage criteria to assess eligibility for SGLT2i and GLP1RA. A 2-sample test for proportions compared use of GDMT at baseline and follow-up. Results: At baseline, the mean age of patients was 68.5 years old, 79% were male, 33% were non-white minorities, 50% had CKD, 64% had HF, and 58% had ASCVD. The median eGFR was 60.1 ml/min/1.73m 2 (IQR 40.7, 93.8), median NT-proBNP was 434 (IQR 123, 1425), and median HbA1c was 7.3% (IQR 6.8, 8.7). At baseline only 37% were prescribed a SGLT2i and 3% a GLP1RA despite being guideline eligible and having medication coverage. After the first visit, the use of therapies significantly increased to 90% for SGLT2i and 39% for GLP1RA. At the end of follow-up, 98% were prescribed a SGLT2i and 57% were prescribed a GLP1RA (P-value comparing proportion GDMT < 0.001; Figure 1). Conclusions: Among patients eligible for GDMT, the initial use of SGLT2i and GLP1RA was low. Our model of synchronous healthcare delivery in a multi-comorbid population, significantly increased the use of SGLT2i and GLP1RA.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".