PEDS3: Risk Factors for Thromboembolic Events in Pediatric Patients with Ventricular Assist Devices
Bibliographic record
Abstract
Background: Pediatric patients on ventricular assist device (VAD) support are at risk of thromboembolic (TE) complications which include pump thrombosis, ischemic stroke, and transient ischemic attack. Methods: We conducted a retrospective chart review of pediatric patients implanted with Paracorporeal Pulsatile (PP) or Paracorporeal Continuous (PC) VADs between 2005-2022, at the Stollery Children’s Hospital (Edmonton, AB). Patients that transitioned from PC to PP were classified in a combination group. Patient and device related factors, including initial anti-coagulation strategies were collected. Kaplan Meier (KM) survival analysis was performed to determine freedom from TE event based on initial anti-coagulation strategy and VAD type. Univariate and multivariate Cox proportional hazard analysis was conducted to look for factors associated with TE events. Results: Of the 95 patients included, median age was 0.92 years (IQR 0.27, 5.37), median weight at implant was 8.4 kg (IQR 4.4, 18.0). Almost two-thirds (63%) had non congenital disease, with 47% supported on PC devices, 25% on PP devices, and 28% with a combination of devices. Initial anti-coagulation was with either Heparin (61.5%) or Bivalirudin (38.5%). Unadjusted freedom from a TE event was significantly higher in those who received Bivalirudin as their initial anti-coagulation strategy (p=0.022, Figure 1). KM analysis based on device type found that PC VADs predicted shorter freedom from TE events (p=0.012). In multivariate analysis, initial anticoagulation strategy (HR 0.359, 95% CI 0.165–0.785, p=0.01) for Bivalirudin was protective against events, while device type (HR 10.9, 95% CI 2.68–44.2, p<0.001), specifically PC devices, was found to be a predictor of TE events. Conclusions: This study suggests that device type, particularly PC, and heparin as an initial anti-coagulation strategy are risk factors TE events. Further work is needed to understand the interaction between device type and initial anti-coagulation strategy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".