Rate, Time Course, and Predictors of Implantable Cardioverter Defibrillator Infections: An Analysis From the SIMPLE Trial
Bibliographic record
Abstract
Background The 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. Methods The S hock Impl ant E valuation 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. Results Within 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. Conclusions This 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 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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".