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 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 | 0.001 |
| 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".