A Population-Based Study of Device Eligibility, Use, and Reasons for Nonimplantation in Patients at Heart Function Clinics
Bibliographic record
Abstract
BACKGROUND: Implantable cardioverter defibrillator (ICD) therapy is lifesaving; however, real-world data regarding the proportion of patients eligible for a primary prevention ICD and subsequent use remain sparse. This study evaluated rates of primary prevention ICD eligibility and use among patients in heart function clinics (HFCs) and to identify reasons for nonimplantation. METHODS: A retrospective study was performed of patients seen at HFCs in Alberta, Canada, from 2013 to 2015. Demographics, comorbidities, clinical indications, and reasons for nonimplantation were abstracted. Eligibility was defined according to the 2008 American College of Cardiology/American Heart Association/Heart Rhythm Society ICD, 2012 American College of Cardiology/American Heart Association/Heart Rhythm Society Focused Update, and 2013 Canadian Cardiovascular Society Cardiac Resynchronization Therapy guidelines. Logistic regression was used to calculate an odds ratio (OR) and 95% confidence interval (CI) for predictors of nonimplantation. RESULTS: Among 1239 patients in HFCs, the median age was 70 years (interquartile range, 59-80), 67% were male, and the median left ventricular ejection fraction was 0.40 (interquartile range, 0.28-0.53). Overall, 45% of patients (n = 553) met guideline criteria for an ICD, and of those, 36% (n = 198) received a device. Among device nonrecipients, 52% (n = 185) had no documented reason for nonimplantation. The most common reason for nonimplantation among nonrecipients was patient preference (48%). Predictors associated with nonimplantation were age more than 75 years (OR, 1.92; 95% CI, 1.31-2.82) and history of cancer (OR, 2.26; 95% CI, 1.07-4.78). At 3 years follow-up, 27% of nonrecipients were deceased. CONCLUSIONS: We found that one-third of patients who met guideline criteria received an ICD and that documentation for nonimplantation was poor.
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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".