Implantable Cardioverter-Defibrillators in Ischaemic Versus Non-Ischaemic Heart Failure: Insights from the VICTORIA Trial
Bibliographic record
Abstract
Abstract Aims Guidelines recommend the use of implantable cardioverter-defibrillators (ICDs) to reduce the risk of sudden cardiac death (SCD) among individuals with heart failure (HF) with reduced ejection fraction (HFrEF). However, the magnitude of benefit from ICD therapy remains unclear in those with a non-ischaemic aetiology of HF. Methods and results Participants with HFrEF and recent HF decompensation in the VICTORIA trial were categorized based on the utilization of a baseline ICD and HF aetiology. A propensity-score adjusted model was used to assess the effect of the presence of an ICD on SCD, cardiovascular death (including SCD) and all-cause death. Of 5040 participants with HFrEF (53.6% ischaemic; 46.4% non-ischaemic), 1399 (27.8%) had an ICD. Over a median of 10.8 months, pre-existing ICD was associated with an overall reduction in SCD (adjusted hazard ratio [aHR] 0.64, 95% confidence interval [CI] 0.43–0.96), but no difference in cardiovascular death (aHR 0.99, 95% CI 0.83–1.18) or all-cause death (aHR 1.02, 95% CI 0.87–1.19). HF aetiology did not modify the effects of ICD on SCD (ischaemic HF: aHR 0.61, 95% CI 0.38–0.98; non-ischaemic HF: aHR 0.72, 95% CI 0.36–1.43; pinteraction = 0.69). Despite relative underuse of ICDs in women as compared to men (16.4% vs. 26.8%), women with an ischaemic cause of their HF had a significant reduction in SCD (aHR 0.2, 95% CI 0.05–0.82; pinteraction = 0.029). The presence of atrial fibrillation modulated ICD treatment effect on SCD (pinteraction = 0.015), with no benefit observed in those with atrial fibrillation. Conclusions Among patients with HFrEF with recent decompensation, presence of an ICD was associated with a reduction in SCD, but did not translate to a reduction in the risk of cardiovascular or all-cause death. Future research is required to evaluate which patients with HFrEF 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 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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".