Treatment with Insulin is Associated with Worse Outcome in Patients with Chronic Heart Failure and Diabetes
Bibliographic record
Abstract
AIMS: Up to one-third of patients with diabetes mellitus and heart failure (HF) are treated with insulin. As insulin causes sodium retention and hypoglycaemia, its use might be associated with worse outcomes. METHODS AND RESULTS: We examined two datasets: 24 012 patients with HF from four large randomized trials and an administrative database of 4 million individuals, 103 857 of whom with HF. In the former, survival was examined using Cox proportional hazards models adjusted for baseline variables and separately for propensity scores. Fine-Gray competing risk regression models were used to assess the risk of hospitalization for HF. For the latter, a case-control nested within a population-based cohort study was conducted with propensity score. Prevalence of diabetes mellitus at study entry ranged from 25.5% to 29.5% across trials. Insulin alone or in combination with oral hypoglycaemic drugs was prescribed at randomization to 24.4% to 34.5% of the patients with diabetes. The rates of death from any cause and hospitalization for HF were higher in patients with vs. without diabetes, and highest of all in patients prescribed insulin [propensity score pooled hazard ratio for all-cause mortality 1.27 (1.16-1.38), for HF hospitalization 1.23 (1.13-1.33)]. In the administrative registry, insulin prescription was associated with a higher risk of all-cause death [odds ratio (OR) 2.02, 95% confidence interval (CI) 1.87-2.19] and rehospitalization for HF (OR 1.42, 95% CI 1.32-1.53). CONCLUSIONS: Whether insulin use is associated with poor outcomes in HF should be investigated further with controlled trials, as should the possibility that there may be safer alternative glucose-lowering treatments for patients with HF and type 2 diabetes mellitus.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".