Risk factors for hematoma in patients undergoing cardiac device procedures: A WRAP-IT trial analysis
Bibliographic record
Abstract
Background Implant site hematoma is a known complication of cardiac device procedures and can lead to major consequences. Objectives To evaluate risk factors for hematoma and further understand the relationship between anticoagulant (AC), antiplatelet (AP) use, and hematoma development. Methods We included 6800 patients from the WRAP-IT trial. To assess baseline and procedural characteristics associated with hematoma within the first 30 days postprocedure, a stepwise Cox regression model was implemented with minimal Akaike information criterion. Cox regressions were also used to evaluate AC/AP use and hematoma risk. Results The overall rate of hematoma was 2.2%. The model identified 11 baseline and procedural characteristics associated with hematoma risk. AC use (hazard ratio [HR]: 2.44, P < .001), lower body mass index (HR: 1.06, P < .001), and history of valve surgery (HR: 2.11, P < .001) were associated with the highest risk. AP use, male sex, history of coronary artery disease, existing pocket, history of nonischemic cardiomyopathy, number of previous cardiac implantable electronic device (CIED) procedures, procedure time, and lead revision were associated with moderate risk. Antithrombotic use was high overall (86%) and AC+AP use was highly predictive of hematoma risk. Regardless of AC status, AP use was associated with an almost doubling of risk vs no AP (HR = 1.85, P = .0006) in the general cohort. Interruption of AC was associated with the lowest hematoma risk (HR = 2.35) while heparin bridging (HR = 4.98) and AP use vs no AP use (HR = 1.85) was associated with the highest hematoma risk. Conclusion The results of this analysis highlight risk factors associated with the development of hematoma in patients undergoing CIED procedures and can inform antithrombotic management.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".