Prognostic impact of inappropriate defibrillator shocks in a population cohort
Bibliographic record
Abstract
BACKGROUND: There is a relative paucity of data linking inappropriate implantable cardioverter-defibrillator (ICD) shocks to adverse clinical outcomes. OBJECTIVE: To examine the association between inappropriate ICD shocks and mortality or heart transplantation in a large population cohort. DESIGN, SETTING, PATIENTS: A cohort study which included all subjects who underwent ICD implantation between 1998 and 2008 and were followed up at our institution. MAIN OUTCOME MEASURES: Multivariable Cox regression analyses were conducted to investigate the effect of inappropriate shocks on the risk of death and heart transplantation. Appropriate and inappropriate ICD therapies were modelled as time-dependent covariates. RESULTS: A total of 1698 patients were included. During a median follow-up of 30 months, there were 246 (14.5%) deaths and 42 (2.5%) heart transplants. The incidence of inappropriate shocks was 10% at 1 year and 14% at 2 years. In the adjusted model, inappropriate shocks were not associated with death or transplantation (HR=0.97, 95% CI 0.70 to 1.36, p value=0.873). In contrast, appropriate shocks were associated with adverse outcomes (HR=3.11, 95% CI 2.41 to 4.02, p value<0.001). The lack of association between inappropriate shocks and outcomes persisted for those with severely impaired left ventricular function (ejection fraction <30%) and for those receiving multiple inappropriate treatments. CONCLUSIONS: In this study, we observed no association between inappropriate ICD shocks and increased mortality or heart transplantation, even among those with severely impaired cardiac function. These findings question whether inappropriate ICD shocks lead to adverse outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".