Predictors of Implantable Cardioverter-Defibrillator Use in Patients with Ischemic Cardiomyopathy
Bibliographic record
Abstract
OBJECTIVES: The objective of this study was to identify and examine ICD utilization in a large group of eligible coronary artery bypass grafting (CABG) patients with impaired left ventricular function. METHODS: We conducted a retrospective study of ICD eligible patients who had previously undergone CABG surgery between March 1, 1995 and June 30, 2008 at a single tertiary care institution. All patients with a pre-operative left ventricular ejection fraction (LVEF) ≤ 35% were considered ICD eligible. The events of interest were ICD implantation and mortality, based on administrative data linkage. RESULTS: A total of 1,169 out of 11,931 CABG patients operated on during the same period had LVEF ≤ 35% and were defined as ICD eligible (mean EF = 27.3% +/- 6.4%). Of these eligible patients, only 101 received an ICD during follow-up (8.6%). The median time to implant was 255 days (14-1078). The single variable that independently predicted eventual ICD implantation was a history of arrhythmia (OR = 7.4; CI, 4.4-12.2). The variables that predicted not having an ICD implanted during follow-up included the need for urgent CABG (OR = 0.5; CI, 0.2-0.9), age > 70 years (OR = 0.5; CI, 0.3-0.8), female gender (OR = 0.2; CI,0.1-0.6), or having chronic obstructive lung disease (OR = 0.5; CI,0.3-0.8). As a data validation step, a series of consecutive patient records were reviewed (n=80) showing that fewer than 23% underwent appropriate follow-up EF assessment post revascularization. CONCLUSION: Our findings suggest that CABG patients with ischemic cardiomyopathy have low rates of ICD utilization. This is particularly evident among females and elderly patients. Furthermore our data suggests that few patients post-revascularization undergo follow-up EF assessment despite current guidelines likely contributing to the low rates of ICD utilization.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".