Multi-Ethnic Comparisons of Diabetes in Heart Failure with Reduced Ejection Fraction: Insights from the HF-ACTION trial and the ASIAN-HF Registry
Bibliographic record
Abstract
AIM: To describe differences in patient characteristics and outcomes by ethnicity in patients with diabetes mellitus (DM) and heart failure (HF) with reduced ejection fraction (HFrEF, ejection fraction ≤35%) in a multi-ethnic cohort. METHODS AND RESULTS: Patient level data from two cohorts (HF-ACTION and ASIAN-HF) were combined, and patients grouped by self-reported ethnicity. DM was defined as the presence of a clinical diagnosis and/or receiving anti-diabetic therapy. A total of 6214 (1324 whites, 674 blacks, 1297 Chinese, 1510 Indians, 717 Malays, 692 Japanese/Koreans) patients were included. The overall prevalence of DM was 39.5% (n = 2454). The prevalence of DM was lowest in whites (29.3%), followed by Japanese/Koreans (34.1%), blacks (35.9%), Chinese (42.3%), Indians (44.2%), and highest in Malays (51.9%). The correlation between age, sex, body mass index, coronary artery disease, hypertension, atrial fibrillation, peripheral vascular disease and chronic kidney disease with DM differed significantly by ethnicity (P for interaction <0.05). The strongest correlations were seen in Malay women, whites with obesity, Indians with coronary artery disease and hypertension, and blacks with chronic kidney disease. On multivariable analyses, DM was significantly associated with the composite of 1-year overall mortality/HF hospitalization (hazard ratio 1.37, 95% confidence interval 1.19-1.57; P < 0.001), with no interaction by ethnicity (P for interaction =0.31). CONCLUSIONS: There is marked heterogeneity in the prevalence and correlates of DM among different ethnic groups with HF worldwide. Subgroups particularly predisposed to DM warrant special attention, since DM increases the combined risk of morbidity and mortality in all ethnicities with HF.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".