403-P: Impact of Diabetes-Related Factors and Background Antihyperglycemic Therapy on the Efficacy and Safety of Ticagrelor Added to Aspirin: Insights from the THEMIS Trial
Bibliographic record
Abstract
In the THEMIS trial of 19,220 patients with stable CAD and T2DM without prior MI or stroke followed for a median of 39.9 months, ticagrelor plus aspirin (ASA) lowered ischemic CV events but increased major bleeding events compared with placebo plus ASA, with a more favorable net clinical benefit in those with prior PCI. We now report the efficacy and safety by diabetes-related factors and background antihyperglycemic therapy. While the incidence of the primary efficacy outcome (CV death, MI, stroke; MACE) event rates (overall 8.1%) was higher with increasing diabetes duration (11.1% >20 years vs. 6.7% ≤5 years) and A1c (11.8% >10% vs. 6.4% ≤6%), the relative benefits of ticagrelor on MACE reduction (overall HR 0.90; p=0.04) did not differ across these various subgroups (p-values for interaction=0.83 and 0.16, respectively). In contrast, the primary safety outcome (TIMI major bleeding) event rate (overall 1.6%) did not vary by diabetes duration or A1c and was similarly increased by ticagrelor in all of these subgroups (overall HR=2.32 [1.82,2.94]; p<0.01). The impact of diabetes duration and A1c were similar in the prior PCI subgroup. Regardless of treatment assignment, patients treated with insulin vs. not had a higher risk for MACE (HR=1.65 [1.49, 1.83]; p<0.01) with no difference in major bleeding (HR=0.92 [0.71, 1.19]; p=0.52), while those treated with sulfonylurea vs. not had no difference in risk for MACE (HR=1.00 [0.91,1.11]; p=0.93) or major bleeding (HR=1.12 [0.89,1.41]; p=0.34) endpoints. The efficacy and safety of ticagrelor did not differ based on background antihyperglycemic therapy. These data suggest that, in general, the relative benefits and risks of ticagrelor added to aspirin are consistent regardless of T2DM duration, A1c, or background antihyperglycemic therapy. NCT01991795. Disclosure L.A. Leiter: Advisory Panel; Self; Abbott, Amgen, AstraZeneca, Boehringer Ingelheim (Canada) Ltd., Boehringer Ingelheim International GmbH, Eli Lilly and Company, HLS Therapeutics, Inc., Janssen Pharmaceuticals, Inc., Merck & Co., Inc., Novo Nordisk Inc., Sanofi, Servier. Research Support; Self; Amgen, AstraZeneca, Kowa Pharmaceuticals America, Inc., Medicines Company. Speaker’s Bureau; Self; Amgen, AstraZeneca, Boehringer Ingelheim International GmbH, Eli Lilly and Company, HLS Therapeutics, Inc., Janssen Pharmaceuticals, Inc., Medscape, Merck & Co., Inc., Novo Nordisk Inc., Sanofi, Servier. D.L. Bhatt: Research Support; Self; Abbott, Afimmune, Amarin Corporation, Amgen, AstraZeneca, Bayer Inc., Boehringer Ingelheim Pharmaceuticals, Inc., Cardax, Chiesi USA, Inc., CSL Behring, Ferring Pharmaceuticals, Fractyl Laboratories, Inc., Idorsia, Ironwood Pharmaceuticals, Ischemix, Lexicon Pharmaceuticals, Inc., Lilly Diabetes, Medtronic, Pfizer Inc., PhaseBio Pharmaceuticals, Inc., PLx Pharma Inc., Regeneron Pharmaceuticals, Roche Pharma, Sanofi-Aventis, Synaptic. D.K. McGuire: Consultant; Self; Afimmune, Applied Therapeutics, Merck Sharp & Dohme Corp., Metavant. Other Relationship; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Eisai Co., Ltd., Eli Lilly and Company, Esperion Therapeutics, Inc., GlaxoSmithKline plc., Janssen Pharmaceuticals, Inc., Lexicon Pharmaceuticals, Inc., Merck & Co., Inc., Novo Nordisk A/S, Pfizer Inc., Sanofi-Aventis. K. Fox: Other Relationship; Self; AstraZeneca, Servier. T. Simon: Advisory Panel; Self; Novartis Pharmaceuticals Corporation, Servier. Board Member; Self; AstraZeneca, Bayer Healthcare Pharmaceuticals Inc. Consultant; Self; Air Liquide, Sanofi. S. Mehta: Research Support; Self; AstraZeneca. E. Lev: None. R.G. Kiss: Speaker’s Bureau; Self; AstraZeneca, Bayer Healthcare Pharmaceuticals Inc., Boehringer Ingelheim International GmbH, Merck Sharp & Dohme Corp., Pfizer Inc. A.J. Dalby: None. H. Bueno: Advisory Panel; Self; AstraZeneca, Bayer AG, Novartis AG. Consultant; Self; Amgen, Bristol-Myers Squibb, Pfizer Inc. Research Support; Self; AstraZeneca, Novartis AG. W. Ridderstråle: Employee; Self; AstraZeneca. A. Himmelmann: Employee; Self; AstraZeneca. J. Prats: Other Relationship; Self; AstraZeneca. Y. Liu: None. J.J. Lee: None. J. Amerena: None. M.N. Kosiborod: Consultant; Self; Amarin Corporation, Amgen, Applied Therapeutics, AstraZeneca, Bayer AG, Boehringer Ingelheim Pharmaceuticals, Inc., Eisai Inc., Eli Lilly and Company, GlaxoSmithKline plc., Glytec, Intarcia Therapeutics, Janssen Scientific Affairs, LLC., Merck & Co., Inc., Novartis Pharmaceuticals Corporation, Novo Nordisk Inc., Sanofi US, Vifor Pharma Group. Research Support; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc. P.G. Steg: Consultant; Self; Amgen, AstraZeneca, Bayer AG, Boehringer Ingelheim International GmbH, Bristol-Myers Squibb, Idorsia, Mylan, Novartis AG, Pfizer Inc., Sanofi, Servier. Research Support; Self; Amarin Corporation. Speaker’s Bureau; Self; Novo Nordisk A/S.
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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 | 0.002 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".