A 19‐Year Study on Pacemaker‐Related Infections: A Claim for Using Postoperative Antibiotics
Bibliographic record
Abstract
BACKGROUND: Although the incidence of pacemaker-related infection (PMINF) is low, it necessitates removal of the pacing system. There is currently no consensus on antibiotics during implantation. METHODS: A prospective database on patients undergoing pacemaker surgery from 1991 to 2009 was reviewed to determine factors associated with PMINF. Specifically, three eras of antibiotic use were compared to elucidate the effect of antibiotics on PMINF: no antibiotics, perioperative antibiotics, and peri- plus postoperative antibiotics. RESULTS: There were 3,253 procedures with PMINF identified in 46 (1.4%) patients. Over 19 years, PMINF incidence fell from 3.6% (no antibiotics) to 2.9% (perioperative antibiotics), to 0.4% (peri- plus postoperative antibiotics). On univariate analysis, the following were associated with PMINF: nonuse of postoperative antibiotics (3.0% vs 0.4%, P < 0.001), year of implant (P < 0.001), repeat procedures (2.3% vs 1%, P = 0.006), nonuse of perioperative antibiotics (3.6% vs 1.3%, P = 0.027). With postoperative antibiotics, rates were significantly reduced in new implants (1/1,289 = 0.1% vs 22/967 = 2.3%, P < 0.001) and repeat procedures (7/692 = 1.0% vs 16/305 = 5.2%, P < 0.001). On multivariate analysis, the following were significant (standardized coefficients denote relative importance): postoperative antibiotics (0.776), repeat procedures (0.508), year of implant (0.142), perioperative antibiotics (0.088). CONCLUSIONS: The PMINF rate is reduced significantly by perioperative antibiotics with a further significant reduction with postoperative antibiotics. However, the reduction in PMINF rate could be a result of changes in practice in the different time eras. This study suggests consideration of perioperative followed by postoperative antibiotics to minimize pacemaker infections.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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".