Predictors of appropriate shock after generator replacement in patients with an implantable cardioverter defibrillator
Bibliographic record
Abstract
BACKGROUND: Implantable cardioverter defibrillators (ICDs) are indicated for the primary prevention of sudden cardiac death in patients with reduced left ventricular ejection fraction (LVEF). The ongoing risk/benefit profile of an ICD at generator replacement is unknown. This study aimed to identify predictors of appropriate ICD shocks and therapies after first ICD generator replacement, and its procedure-related complications. METHODS: We conducted a multicenter, retrospective cohort study including patients with primary prevention ICDs who underwent generator replacement between April 2005 and July 2015 at three Canadian centers. The primary and secondary outcomes were appropriate ICD shock and any appropriate ICD therapy, respectively. Procedure-related complication rates were also reported. RESULTS: Of the 219 patients in the cohort, 61 (28%) experienced an appropriate shock while 40 (18%) experienced appropriate antitachycardia pacing over a median follow up of 2.2 years. Independent predictors of appropriate ICD shocks included: LVEF at time of replacement (adjusted odds ratio [OR] 0.4 per 10% increase in LVEF, P < .001), a history of appropriate ICD shocks prior to replacement (OR 4.9, P < .001), and a history of inappropriate ICD shocks (OR 4.2, 95%, P < .002). Similar predictors were identified for the secondary outcome of any appropriate ICD therapy. Device-related complications were reported in 25 (11%) patients, with 1 (0.5%) resulting in death, 14 (6.3%) requiring site re-operation, and 6 (2.7%) requiring cardiac surgical management. CONCLUSION: Not all primary prevention ICD patients undergoing generator replacement will require appropriate device therapies afterwards. Generator replacement is associated with several risks that should be weighed against its anticipated benefit. A comprehensive assessment of the risk-benefit profile of patients undergoing generator replacement is warranted.
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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 | 0.000 | 0.003 |
| 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 | 0.000 | 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".