Identification of Patients Most Likely to Benefit From Implantable Cardioverter-Defibrillator Therapy
Bibliographic record
Abstract
BACKGROUND: Patients with resuscitated ventricular tachyarrhythmias (ventricular tachycardia/ventricular fibrillation) benefit from implantable cardioverter-defibrillators (ICDs) compared with medical therapy. We hypothesized that the patients who benefit most from an ICD are those at greatest risk of death. METHODS AND RESULTS: In the Canadian Implantable Defibrillator Study (CIDS), 659 patients with resuscitated ventricular tachyarrhythmias were randomly assigned to receive an ICD or amiodarone and were then followed for a mean of 3 years. There were 98 and 83 deaths in the amiodarone and ICD groups, respectively. We used multivariate Cox analysis to assess the impact of baseline parameters on the mortality in the amiodarone group. Reduced left ventricular ejection fraction, advanced age, and poor NYHA status identified high-risk patients (P=0.0001 to 0.0009). Quartiles of risk were constructed, and the mortality reduction associated with ICD treatment in each quartile was assessed. There was a significant interaction between risk quartile and the ICD treatment effect (P=0.011). In the highest risk quartile, there was a 50% relative risk reduction (95% CI 21% to 68%) of death in the ICD group, whereas in the 3 lower quartiles, there was no benefit. Patients who are most likely to benefit from an ICD can be identified with a simple risk score (>/=2 of the following factors: age >/=70 years, left ventricular ejection fraction </=35%, and NYHA class III or IV). Thirteen of 15 deaths that were prevented by the ICD occurred in patients with >/=2 risk factors. CONCLUSIONS: In CIDS, patients at highest risk of death benefited most from ICD therapy. These can be identified easily on the basis of age, poor ventricular function, and poor functional status.
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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) | 0.000 | 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 | 0.000 |
| 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".