Postoperative Low‐Molecular‐Weight Heparin Bridging Is Associated with an Increase in Wound Hematoma Following Surgery for Pacemakers and Implantable Defibrillators
Bibliographic record
Abstract
BACKGROUND: The perioperative management of patients receiving oral anticoagulation therapy (OAC) who undergo pacemaker (PM) and defibrillator (ICD) surgery remains controversial. Low-molecular-weight heparin (LMWH) is often used; however, wound hematoma is a common complication. METHODS: At a single academic Canadian center, between July 2003 and June 2005, details of perioperative OAC bridging and the rate of wound hematoma requiring reoperation or interruption of OAC were reviewed for all patients receiving LMWH bridging for PM or ICD surgery. RESULTS: A total of 148 PM/ICD patients underwent perioperative bridging with LMWH. A significant hematoma occurred in 23 patients, requiring reoperation in three patients. No patient died, developed infection, or stroke. The initial bridging regimen included LMWH (enoxaparin 1 mg/kg BID) given until evening prior to surgery, and reinitiated on postoperative day 3. In response to high rates of postoperative hematoma, subsequent protocols omitted the LMWH on the evening before surgery, all postoperative LMWH, or both. The use of LMWH the night before surgery had no effect on hematoma rates (12% vs 17%, P = 0.62); however, the use of any postoperative LMWH increased hematoma rates (23% vs 8%, P = 0.01). Hematoma rates were not increased in patients receiving acetylsalicylic acid (19% vs 16%, P = 0.62) or clopidogrel (25% vs 17%, P = 0.54). In a multivariate analysis, independent predictors of significant wound hematoma included postoperative LMWH (P = 0.001), a higher international normalized ratio on the day of surgery (P = 0.03), and male sex (P = 0.05). CONCLUSION: Elimination of postoperative LMWH was associated with a substantial reduction in hematoma rates following PM and ICD surgery.
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 | 0.001 | 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".