2216-PUB: Patient Preferences for Newer Oral Therapies in Type 2 Diabetes
Bibliographic record
Abstract
ADA/EASD guidelines emphasize the importance of patient engagement in therapy decisions. Beyond glycemic effects of type 2 diabetes (T2D) therapies, only SGLT2i and GLP-1RA have cardiovascular (CV) benefits. Key attributes of such therapies may influence their use/adoption. We evaluated patient preferences towards three oral T2D therapies using conjoint analysis. This analysis used an online survey, completed by 553 respondents with T2D in the U.S. (mean age ±SD was 64±9; 55% had CV risk; 27% had CV disease), to present 7 hypothetical, blinded pair-wise drug profile comparison choices composed of different benefit-risk attributes and effect ranges (levels). Attributes/levels were derived from combinations of phase 3 trial data for empagliflozin 25mg (SGLT2i), oral semaglutide 14mg (GLP-1RA) and sitagliptin 100mg (DPP-4i). The predicted therapy preference outcomes and the relative importance of one attribute relative to another were calculated (in %). The preference outcome was highest for the profile matching empagliflozin, ranked first by 56% (z-test, p<0.05), versus 38% for sitagliptin and 6% for oral semaglutide. Results were overall consistent in subgroup analyses. Genital infection risk was the most important perceived attribute with a relative score of 19% (z-test, p<0.05). Second and similarly important were fasting requirements (15%), weight reduction (15%), risk of vomiting (14%) and CV benefit (12%). Next was risk of nausea (11%). Last were HbA1c reduction (8%) and ability to take medication with other drugs (6%). While blinded to drug name/dose, respondents were also asked to choose explicitly between drug profiles similar to empagliflozin (chosen by 41%), sitagliptin (31%), oral semaglutide (11%), and ’none of the options’ (17%). The drug profile comparable to empagliflozin was the preferred agent; however, CV benefit was not the top patient priority. A shared physician-patient decision model and increased patient education are needed to ensure optimal use of guideline directed therapies in T2D. Disclosure G. Savarese: Advisory Panel; Self; AstraZeneca. Consultant; Self; Genesis, Societ Prodotti Antibiotici. Research Support; Self; AstraZeneca, Merck Sharp & Dohme Corp., Novartis Pharmaceuticals Corporation, Vifor Pharma Group. Speaker’s Bureau; Self; Roche Pharma, Servier, Vifor Pharma Group. 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. C. Pang: None. R. Wood: Consultant; Self; Abbott, ADOCIA, American Diabetes Association, Ascensia Diabetes Care, Boehringer Ingelheim Pharmaceuticals, Inc., CeQur Corporation, Dexcom, Inc., Eli Lilly and Company, Insulet Corporation. Employee; Self; dQ&A Market Research Inc. J.T. George: Employee; Self; Boehringer Ingelheim International GmbH. N. Soleymanlou: Employee; Self; Boehringer Ingelheim (Canada) Ltd. Funding Boehringer Ingelheim and Eli Lilly and Company Diabetes Alliance
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.031 | 0.003 |
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".