Implantable Cardioverter-Defibrillators in Heart Failure Patients with Reduced Ejection Fraction and Diabetes
Bibliographic record
Abstract
AIM: There is limited information on the outcomes after primary prevention implantable cardioverter-defibrillator (ICD) implantation in patients with heart failure (HF) and diabetes. This analysis evaluates the effectiveness of a strategy of ICD plus medical therapy vs. medical therapy alone among patients with HF and diabetes. METHODS AND RESULTS: A patient-level combined-analysis was conducted from a combined dataset that included four primary prevention ICD trials of patients with HF or severely reduced ejection fractions: Multicenter Automatic Defibrillator Implantation Trial I (MADIT I), MADIT II, Defibrillators in Non-Ischemic Cardiomyopathy Treatment Evaluation (DEFINITE), and Sudden Cardiac Death in Heart Failure Trial (SCD-HeFT). In total, 3359 patients were included in the analysis. The primary outcome of interest was all-cause death. Compared with patients without diabetes (n = 2363), patients with diabetes (n = 996) were older and had a higher burden of cardiovascular risk factors. During a median follow-up of 2.6 years, 437 patients without diabetes died (178 with ICD vs. 259 without) and 280 patients with diabetes died (128 with ICD vs. 152 without). ICDs were associated with a reduced risk of all-cause mortality among patients without diabetes [hazard ratio (HR) 0.56, 95% confidence interval (CI) 0.46-0.67] but not among patients with diabetes (HR 0.88, 95% CI 0.7-1.12; interaction P = 0.015). CONCLUSION: Among patients with HF and diabetes, primary prevention ICD in combination with medical therapy vs. medical therapy alone was not significantly associated with a reduced risk of all-cause death. Further studies are needed to evaluate the effectiveness of ICDs among patients with diabetes.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".