Timing of device reimplantation and reinfection rates following cardiac implantable electronic device infection: a systematic review and meta-analysis
Bibliographic record
Abstract
Objectives Initial management of cardiac implantable electronic device (CIED) infection requires removal of the infected CIED system and treatment with systemic antibiotics. However, the optimal timing to device reimplantation is unknown. The aim of this study was to quantify the incidence of reinfection after initial management of CIED infection, and to assess the effect of timing to reimplantation on reinfection rates. Design Systematic review and meta-analysis. Interventions A systematic review and meta-analysis was performed of studies published up to February 2018. Inclusion criteria were: (a) documented CIED infection, (b) studies that reported the timing to device reimplantation and (c) studies that reported the proportion of participants with device reinfection. A meta-analysis of proportions using a random effects model was performed to estimate the pooled device reinfection rate. Primary and secondary outcome measures The primary outcome measure was the rate of CIED reinfection. The secondary outcome was all-cause mortality. Results Of the 280 screened studies, 8 met inclusion criteria with an average of 96 participants per study (range 15–220 participants). The pooled incidence rate of device reinfection was 0.45% (95% CI, 0.02% to 1.23%) per person year. A longer time to device reimplantation >72 hours was associated with a trend towards higher rates of reinfection (unadjusted incident rate ratio 4.8; 95% CI 0.9 to 24.3, p=0.06); however, the meta-regression analysis was unable to adjust for important clinical covariates. There did not appear to be a difference in reinfection rates when time to reimplantation was stratified at 1 week. Heterogeneity was moderate (I 2 =61%). Conclusions The incident rate of reinfection following initial management of CIED infection is not insignificant. Time to reimplantation may affect subsequent rates of device reinfection. Our findings are considered exploratory and significant heterogeneity limits interpretation. PROSERO registration number CRD4201810960.
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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 | 0.015 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.051 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".