Risk Factors for CIED Infection After Secondary Procedures
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVES: This study aimed to identify risk factors for infection after secondary cardiac implantable electronic device (CIED) procedures. BACKGROUND: Risk factors for CIED infection are not well defined and techniques to minimize infection lack supportive evidence. WRAP-IT (World-wide Randomized Antibiotic Envelope Infection Prevention trial), a large study that assessed the safety and efficacy of an antibacterial envelope for CIED infection reduction, offers insight into procedural details and infection prevention strategies. METHODS: This analysis included 2,803 control patients from the WRAP-IT trial who received standard preoperative antibiotics but not the envelope (44 patients with major infections through all follow-up). A multivariate least absolute shrinkage and selection operator machine learning model, controlling for patient characteristics and procedural variables, was used for risk factor selection and identification. Risk factors consistently retaining predictive value in the model (appeared >10 times) across 100 iterations of imputed data were deemed significant. RESULTS: Of the 81 variables screened, 17 were identified as risk factors with 6 being patient/device-related (nonmodifiable) and 11 begin procedure-related (potentially modifiable). Patient/device-related factors included higher number of previous CIED procedures, history of atrial arrhythmia, geography (outside North America and Europe), device type, and lower body mass index. Procedural factors associated with increased risk included longer procedure time, implant location (non-left pectoral subcutaneous), perioperative glycopeptide antibiotic versus nonglycopeptide, anticoagulant, and/or antiplatelet use, and capsulectomy. Factors associated with decreased risk of infection included chlorhexidine skin preparation and antibiotic pocket wash. CONCLUSIONS: In WRAP-IT patients, we observed that several procedural risk factors correlated with infection risk. These results can help guide infection prevention strategies to minimize infections associated with secondary CIED procedures.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 it