Abstract 17304: Sudden Cardiac Death After Acute Heart Failure Hospitalization: Insights From ASCEND-HF
Bibliographic record
Abstract
Introduction: Despite concerns about sudden cardiac death (SCD) early after acute heart failure (AHF) hospitalization, the incidence of SCD and the factors and associated with its occurrence have not been well defined. We evaluated the incidence and predictors of SCD early after AHF hospitalization. Hypothesis: AHF is associated with SCD. Methods: ASCEND-HF included patients with AHF with any ejection fraction (EF). Clinical events including SCD, resuscitated SCD (RSCD), and sustained ventricular tachycardia/ventricular fibrillation (VT/VF) were adjudicated through 30 days. Patients could have more than one event. These three events were combined to form a new composite endpoint, and baseline characteristics associated with this composite were determined by logistic regression and stepwise selection. RSCD and VT/VF were used as time dependent variables in a Cox model to evaluate the association with 180-day all-cause mortality. Results: Among 7,011 patients with available date on SCD, RSCD, or VT/VF, median age was 67 years (IQR 56-76), median EF was 30% (IQR 20-37%), 9% had a history of VT, and 16% had an ICD. The 30-day event rates were 1.8% (n=121) for the composite, 0.6% for SCD (n=43), 0.4% for RSCD (n=24), and 0.9% for VT/VF (n=64). In the multivariable model, chronic obstructive pulmonary disease, history of VT, male sex, higher admission heart rate, and longer baseline QRS duration were associated with SCD, RSCD, or VT/VF (Table). The composite was independently associated with higher 180-day mortality (adjusted HR 6.6, 95% CI 4.8-9.1, p<0.0001). Conclusions: Patients admitted for AHF had relatively high rates of SCD, RSCD, or VT/VF within 30 days of follow-up, and RSCD or VT/VF were associated with higher 180-day mortality. Further studies are needed to evaluate ways to predict and therapies to prevent and treat tachyarrhythmias early after AHF hospitalization, including in those patients who may be eligible for an ICD after medical therapy has been optimized.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".