Abstract 10390: Impact of Diabetes Mellitus on the Prognosis of Patients With Heart Failure and Preserved Left Ventricle Ejection Fraction: Insights From the TOPCAT Study
Bibliographic record
Abstract
Background: The impact of diabetes mellitus (DM) for patients with heart failure and preserved left ventricle ejection fraction (HFpEF) remains unclear. We aimed to determine the impact of DM on the prognosis of patients with DM and HFpEF enrolled in the TOPCAT trial. Methods: We classified TOPCAT patients into three groups: insulin-dependent DM (IDDM), non-insulin dependent DM (NIDDM) and non-DM. We investigated the occurrence of a major adverse cardiovascular event (MACE), defined as CV mortality, hospitalization for HF, non-fatal myocardial infarction (MI), non-fatal non-hemorrhagic stroke, or aborted cardiac arrest. Secondary outcomes included the individual components of MACE and all-cause mortality. We used multivariate Cox proportional hazards regression models to evaluate the independent associations of DM on time to MACE. Results: There were 1,134 DM patients (439 IDDM and 695 NIDDM) and 2,307 non-DM patients. The IDDM patients were younger and more likely to be male, non-white, and from the Americas. Despite having the highest median left ventricle’s ejection fraction (LVEF) (58%) compared to NIDDM and non-DM patients (57% and 56%, respectively, p=0.007), they were more symptomatic. Forty-eight percent of IDDM patients were of New York Heart Association (NYHA) III/IV functional classes compared to 36% of NIDDM and 29% of non-DM patients (p<0.001). IDDM patients had significantly increased risks for MACE, CV mortality, MI, HF hospitalization, and all-cause mortality when compared to non-DM patients (p-values<0.01). NIDDM patients had similar risks for MACE and the secondary outcomes as the non-DM patients. The effect of diabetes on outcomes was similar between regions. Conclusion: Among HFpEF patients, IDDM but not NIDDM was independently associated with increased risks of MACEs. Future research is needed to evaluate whether optimal medications and lifestyle interventions may reduce MACE in these high-risk patients.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".