Risk of new‐onset heart failure in patients using sitagliptin: a population‐based cohort study
Bibliographic record
Abstract
AIMS: To examine whether patients using sitagliptin at the time of an acute coronary syndrome event are at increased risk of incident heart failure compared with those not exposed. METHODS: Using US claims data, people with diabetes without a history of heart failure in the 3 years before hospitalization for acute coronary syndrome were identified for the period 2004 to 2010. We used a nested case-control design, whereby cases were patients who developed incident heart failure <30 days after admission to hospital for acute coronary syndrome and were matched by age and sex with up to 10 controls with no heart failure. Subjects exposed or not exposed to sitagliptin in the 90 days before acute coronary syndrome admission were compared using conditional logistic regression after adjustment for clinical and laboratory data, healthcare utilization and propensity scores. RESULTS: In total, 457 cases of heart failure developing de novo after diagnosis of acute coronary syndrome were matched to 4570 controls. The average age of the subjects was 55 years and 65% were male. Overall, 11 of 147 (7%) people exposed to sitagliptin developed heart failure compared with 446 of the 4880 people not exposed (9%, adjusted odds ratio 0.75, 95% CI 0.38-1.46; P=0.40). Sitagliptin exposure before acute coronary syndrome was not associated with an increased risk of death or heart failure combined (7% vs 9%, adjusted odds ratio 0.66, 95% CI 0.34-1.28). CONCLUSIONS: In our sample of patients who are at high risk of heart failure after acute coronary syndrome, sitagliptin exposure was not associated with an increased risk of de novo heart failure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".