Renal injury and recovery in pediatric patients after ventricular assist device implantation and cardiac transplant
Bibliographic record
Abstract
Abstract Background The use of ventricular assist devices (VADs) in children with heart failure may be of particular benefit to those with accompanying renal failure, as improved renal function is seen in some, but not all recipients. We hypothesized that persistent renal dysfunction at 7 days and/or 1 month after VAD implantation would predict chronic kidney disease (CKD) 1 year after heart transplantation (HT). Methods Linkage analysis of all VAD patients enrolled in both the PEDIMACS and PHTS registries between 2012 and 2016. Persistent acute kidney injury (P‐AKI), defined as a serum creatinine ≥1.5× baseline, was assessed at post‐implant day 7. Estimated glomerular filtration rate (eGFR) was determined at implant, 30 days thereafter, and 12 months post‐HT. Pre‐implant eGFR, eGFR normalization (to ≥90 mL/min/1.73 m2), and P‐AKI were used to predict post‐HT CKD (eGFR <90 mL/min/1.73 m2). Results The mean implant eGFR was 85.4 ± 46.5 mL/min/1.73 m2. P‐AKI was present in 19/188 (10%). Mean eGFR at 1 month post‐VAD implant was 131.1 ± 62.1 mL/min/1.73 m2, significantly increased above baseline (P < 0.001). At 1 year post‐HT (n = 133), 60 (45%) had CKD. Lower pre‐implant eGFR was associated with post‐HT CKD (OR 0.99, CI: 0.97‐0.99, P = 0.005); P‐AKI was not (OR 0.96, CI: 0.3‐3.0, P = 0.9). Failure to normalize renal function 30 days after implant was highly associated with CKD at 1 year post‐transplant (OR 12.5, CI 2.8‐55, P = 0.003). Conclusions Renal function improves after VAD implantation. Lower pre‐implant eGFR and failure to normalize renal function during the support period are risk factors for CKD development after HT.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".