Electrical storm in patients with an implantable defibrillator: incidence, features, and preventive therapy: insights from a randomized trial
Bibliographic record
Abstract
AIMS: The purpose of this study was to assess the incidence, features, and clinical sequelae of 'electrical storm' (ES). METHODS AND RESULTS: This study is a prospectively designed secondary analysis of SHIELD; a randomized trial of azimilide for suppression of ventricular tachycardia/fibrillation (VT/VF) leading to implanted cardioverter defibrillator (ICD) therapies. Systematic and rigorous follow-up and blinded adjudication of ICD therapy allowed identification of all ESs (>/=3 separate VT/VF episodes leading to ICD therapies within 24 h). Of 633 ICD recipients, 148 (23%) experienced at least one ES over 1-year follow-up. No clinical predictors of ES were identified. Frequent VT episodes accounted for 91% of all ESs, with the remaining being VF alone or both VT plus VF. ES led to a 3.1-fold increase in arrhythmia-related hospitalization (95% CI 2.3-4.3; P<0.0001) compared with patients with isolated VT/VF, and to a 10.2-fold increase (95% CI 6.4-16.3; P<0.0001) compared with patients without VT/VF. Compared with placebo, azimilide (75 and 125 mg/day) reduced the risk of recurrent ES by 37% (HR=0.63, 95% CI 0.35-1.11, P=0.11) and 55% (HR=0.45, 95% CI 0.23-0.87, P=0.018), respectively. However, the reduction in time-to-first ES did not reach statistical significance by both doses (75 and 125 mg) of azimilide (HR=0.82, 95% CI 0.56-1.19, P=0.29 and HR=0.69, 95% CI 0.46-1.04, P=0.07), respectively. CONCLUSION: ES is common and unpredictable in ICD recipients and it is a strong predictor of hospitalization.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".