Predictors of Appropriate Implantable Cardioverter Defibrillator (ICD) Therapy in Primary Prevention Patients with Ischemic and Nonischemic Cardiomyopathy
Bibliographic record
Abstract
Background: We sought to assess predictors of appropriate implantable cardioverter defibrillator (ICD) therapy in patients receiving primary prevention ICDs. Methods: Four hundred twenty-one consecutive patients (ischemic and nonischemic) undergoing primary prevention ICD implantation were studied. Patients were grouped based on the presence/absence of appropriate ICD therapy. Summary data and stored electrograms from ICDs were reviewed to determine appropriateness of therapy. Predictors of therapy were assessed by both univariate and multivariate Cox regression analysis. Results: Of 421 primary prevention patients undergoing ICD implantation, 79 (19%) had received appropriate ICD therapies. By univariate comparison, nonsustained ventricular tachycardia (NSVT), male sex, left ventricle diastolic diameter (LVDD), and hypertension were all significant predictors for ICD therapy over a mean follow-up time of 751 ± 493 days (P ≤ 0.05). The use ofβ-blockers was found to be a negative predictor. In the ischemic cardiomyopathy (ICM) population, 55 (17%) patients received ICD therapy and this was predicted by NSVT, hypertension, LVDD, and left atrial diameter.β-blockers were protective. In the nonischemic dilated cardiomyopathy (NIDCM) population, 24 (23%) received appropriate therapies, which were predicted by NSVT, male sex, dual chamber device, lack of biventricular device, and lack ofβ-blockers. By multivariate analysis, NSVT, hypertension, and lack ofβ-blockers were significant for ICM, while NSVT and absence ofβ-blockers were predictive for NIDCM. Ejection fraction, New York Heart Association class, and QRS width were not significantly different between therapy and no-therapy groups in any population. Conclusions: ICD-delivered therapy occurred in 19% of primary prevention patients with both ischemic and dilated cardiomyopathy and was predicted by NSVT and a lack ofβ-blocker use. (PACE 2010; 33:320–329)
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".