Rate, Time Course, and Predictors of Implantable Cardioverter Defibrillator Infections: An Analysis From the SIMPLE Trial
Bibliographic record
Abstract
BackgroundThe number of implantable cardioverter defibrillator (ICD) infections is increasing due to an increased number of ICD implants, higher-risk patients, and more frequent replacement procedures, which carry a higher risk of infection. Reducing the morbidity, mortality, and cost of ICD-related infections requires an understanding of the current rate of this complication and its predictors.MethodsThe Shock Implant Evaluation Trial (SIMPLE) trial randomized 2500 ICD recipients to defibrillation testing or not. Over an average of 3.1 years, patients were seen every 6 months and examined for evidence of ICD infection, which was defined as requiring device removal and/or intravenous antibiotics.ResultsWithin 24 months, 21 patients (0.8%) developed infection. Fourteen patients (67%) with infection presented within 30 days, 20 patients by 12 months, and only 1 patient beyond 12 months. Univariate analysis demonstrated that patients with primary electrical disorders (3 patients, P = 0.009) and those with a secondary prevention indication (13 patients, P = 0.0009) were more likely to develop infection. Among the 2.2% of patients who developed an ICD wound hematoma, 10.4% developed an infection. Among the 8.3% of patients requiring an ICD reintervention, 1.9% developed an infection.ConclusionsThis cohort of ICD recipients at high-volume centres have a low risk of device-related infection. However; strategies to reduce wound hematoma and the need for ICD reintervention could further reduce the rate of infection.
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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) | 0.001 | 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".