Association of Diabetes and Kidney Function According to Age and Systolic Function with the Incidence of Sudden Cardiac Death and Non-Sudden Cardiac Death in Myocardial Infarction Survivors with Heart Failure
Bibliographic record
Abstract
AIMS: An implantable cardioverter-defibrillator (ICD) is recommended for reducing the risk of sudden cardiac death (SCD) in myocardial infarction (MI) patients with a left ventricular ejection fraction (LVEF) ≤ 30%, as well as patients with a LVEF ≤ 35% and heart failure symptoms. Diabetes and/or impaired kidney function may confer additional SCD risk. We assessed the association between these two risk factors with SCD and non-SCD among MI survivors taking account of age and LVEF. METHODS AND RESULTS: , individually and together, conferred a higher risk of SCD [adjusted competing risk: hazard ratio (HR) 1.23, 1.23, and 1.32, respectively; all P < 0.03] and non-SCD (HR 1.34, 1.52, and 2.13, respectively; all P < 0.0001). Annual SCD rates in patients with LVEF > 35% and with diabetes, impaired kidney function, or both (2.0%, 2.5% and 2.7%, respectively) were comparable to rates observed in patients with LVEF 30-35% but no such risk factors (1.7%). However, these patients had also similarly higher non-SCD rates, such that the ratio of SCD to non-SCD was not increased. Importantly, this ratio was mostly dependent on age, with higher overall ratios in youngest subgroups (0.89 in patients < 55 years vs. 0.38 in patients ≥ 75 years), regardless of risk factors. CONCLUSION: Although MI survivors with LVEF > 35% with diabetes, impaired kidney function, or both are at increased risk of SCD, the risk of non-SCD risk is even higher, suggesting an extension of the current indication for an ICD to them is unlikely to be worthwhile. MI survivors with low LVEF and aged < 55 years are likely to have the greatest potential benefit from ICD implantation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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".