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 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 | 0.000 | 0.000 |
| 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".