1144-P: Patient Phenotypes and SGLT2 Inhibition in Type 2 Diabetes Mellitus: Insights from the EMPA-REG OUTCOME Trial
Bibliographic record
Abstract
In EMPA-REG OUTCOME, empagliflozin (EMPA) reduced risk of cardiovascular (CV) death by 38% and hospitalization for heart failure (HHF) by 35% in patients with type 2 diabetes (T2D) and CV disease. We aimed to identify phenotypes of patients with different risk of outcomes and to explore treatment effects across these groups. Overall, 7020 patients were treated with EMPA 25, 10 mg or placebo (PBO). For this post-hoc analysis, patients were randomly separated into training (2/3) and validation sets (1/3 of patients). Latent class analysis identified 3 clusters using 6639 patients with complete data. The association of clusters to CV death and CV death/HHF, and treatment effect of EMPA vs. PBO across clusters was explored by Cox regression. Cluster 1 included younger patients with shorter T2D duration. Cluster 2 included more women with non-coronary atherosclerotic disease (CAD), and Cluster 3 older patients with advanced CAD. In the training set, risk of CV death varied across clusters (Cluster 2 vs. 1 HR 1.83 [95% CI 1.23, 2.71], Cluster 3 vs. 1 HR 1.86 [1.30, 2.67]) with similar pattern for CV death/HHF. Treatment effect of EMPA was consistent across clusters (Figure). Results were replicated in the validation set. We identified 3 phenotypes of patients with varying risk of outcomes. The consistent treatment effect across clusters reaffirms the robustness of CV death/HHF reduction with EMPA. Disclosure A. Sharma: Advisory Panel; Self; Boehringer Ingelheim International GmbH, Roche Pharma. Research Support; Self; Bristol-Myers Squibb, Merck & Co., Inc. Speaker’s Bureau; Self; Novartis Pharmaceuticals Corporation. A. Ofstad: Employee; Self; Boehringer Ingelheim International GmbH. T. Ahmad: None. B. Zinman: Advisory Panel; Self; Abbott, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Janssen Pharmaceuticals, Inc., Merck Sharp & Dohme Corp., Novo Nordisk Inc., Sanofi-Aventis. I. Zwiener: Employee; Self; Boehringer Ingelheim International GmbH. D.H. Fitchett: Consultant; Self; AstraZeneca, Boehringer Ingelheim International GmbH. Speaker’s Bureau; Self; Lilly Diabetes. Other Relationship; Self; Novo Nordisk Inc. C. Wanner: Advisory Panel; Self; Eli Lilly and Company, Merck & Co., Inc., Mundipharma International. Consultant; Self; Boehringer Ingelheim (Canada) Ltd., Sanofi Genzyme. Speaker’s Bureau; Self; AstraZeneca. Other Relationship; Self; Boehringer Ingelheim International GmbH. J.T. George: Employee; Self; Boehringer Ingelheim International GmbH. S. Hantel: Employee; Self; Boehringer Ingelheim Pharma GmbH & Co. KG. N. Desai: None. R.J. Mentz: Consultant; Self; Amgen, AstraZeneca, Bayer Healthcare Pharmaceuticals Inc., Boehringer Ingelheim Pharmaceuticals, Inc., Merck & Co., Inc., Novartis Pharmaceuticals Corporation, Sanofi. Research Support; Self; GlaxoSmithKline plc. Funding Boehringer Ingelheim and Eli Lilly and Company Diabetes Alliance
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".