Defibrillation Testing at the Time of ICD Insertion: An Analysis From the Ontario ICD Registry
Bibliographic record
Abstract
BACKGROUND: increasingly, ICD implantation is performed without defibrillation testing (DT). OBJECTIVES: To determine the current frequency of DT, the risks associated with DT, and to understand how physicians select patients to have DT. METHODS: between January 2007 and July 2008, all patients in Ontario, Canada who received an ICD were enrolled in this prospective registry. RESULTS: a total of 2,173 patients were included; 58% had new ICD implants for primary prevention, 25% for secondary prevention, and 17% had pulse generator replacement. DT was carried out at the time of ICD implantation or predischarge in 65%, 67%, and 24% of primary, secondary, and replacement cases respectively (P = <0.0001). The multivariate predictors of a decision to conduct DT included: new ICD implant (OR = 13.9, P < 0.0001), dilated cardiomyopathy (OR = 1.8, P < 0.0001), amiodarone use (OR = 1.5, P = 0.004), and LVEF > 20% (OR = 1.3, P = 0.05). A history of atrial fibrillation (OR = 0.58, P = 0.0001) or oral anticoagulant use (OR = 0.75, P = 0.03) was associated with a lower likelihood of having DT. Age, gender, NYHA class, and history of stroke or TIA did not predict DT. Perioperative complications, including death, myocardial infarction, stroke, tamponade, pneumothorax, heart failure, infection, wound hematoma, and lead dislodgement, were similar among patients with (8.7%) and without (8.3%) DT (P = 0.7) CONCLUSIONS: DT is performed in two-thirds of new ICD implants but only one-quarter of ICD replacements. Physicians favored performance of DT in patients who are at lower risk of DT-related complications and in those receiving amiodarone. DT was not associated with an increased risk of perioperative complications.
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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 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".