Risk factors for thromboembolic events in pediatric patients with ventricular assist devices
Bibliographic record
Abstract
Objective Pediatric patients on ventricular assist devices (VAD) are at risk of thromboembolic (TE) complications. Our objective was to identify factors associated with TE events, including the role of initial anticoagulation strategy and device type in the pediatric VAD population. Methods This was a retrospective, single-center review (2005-2022) of children who were implanted with paracorporeal pulsatile (PP), paracorporeal continuous (PC), or a combination of devices. Patient- and device-related factors were collected. Kaplan-Meier survival analysis was performed to determine freedom from TE. Cox proportional hazard analysis was conducted to look for factors associated with TE events. Results Ninety-five patients included with a median age of 0.9 years (interquartile range, 0.3, 5.4); median weight of 8.4 kg (interquartile range, 4.5, 17.8), and 63.2% with noncongenital heart disease. Device breakdown included 47.4% PC, 24.2% PP, and 23.2% combination of devices. Initial anticoagulation was either heparin (61.5%) or bivalirudin (38.5%). In Kaplan-Meier analysis, unadjusted freedom from a TE event was significantly greater in those who received bivalirudin as their initial anticoagulation strategy ( P = .02) and PP VADs ( P = .02). In multivariate analysis, initial anticoagulation strategy with bivalirudin (hazard ratio, 0.30; 95% confidence interval, 0.12-0.75, P = .01) was associated with a reduced hazard of TE events, whereas PC device strategy was found to be associated with an increased hazard (hazard ratio, 2.78; 95% confidence interval, 1.12-6.88, P = .03). Conclusions This study suggests that PC device strategy and heparin as an initial anticoagulation strategy are associated with increased hazard of TE events. Further research is required to understand the interaction between device type and initial anticoagulation strategy.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".