Decreasing Endomyocardial Biopsy Frequency in Pediatric Heart Transplantation Using A Rejection Risk Prediction Score—A Single Center Study
Bibliographic record
Abstract
BACKGROUND: Rejection remains an important cause of morbidity and mortality after pediatric heart transplantation (HT). Endomyocardial biopsy (EMB) is the gold standard for rejection diagnosis, but it comes with procedural risk. The frequency of EMB varies significantly across centers. Since April 2018, our center's surveillance EMB schedule is based on a rejection risk prediction score employing age, pre-HT diagnosis, and panel reactive antibodies (PRA). We aimed to evaluate outcomes in the 1st year post-HT before and after risk score implementation. METHODS: Patients who underwent HT at our center at ≤ 18 years of age from January 2015 to December 2020 were reviewed. The primary endpoint was rejection-free survival at 1 year-post- HT. Clinical characteristics were compared for patients transplanted in Era 1 (January 2015-April 2018) and Era 2 (April 2018-December 2020). Cumulative 1-year survival free from rejection and from rejection with hemodynamic compromise (RHC) was compared between eras using Kaplan-Meier survival analysis. RESULTS: 115 patients underwent HT during our study period (52 in Era 1 and 63 in Era 2). There was an increase in VAD utilization between eras (19% in Era 1 vs. 40% in Era 2, p = 0.025), but otherwise no significant difference in demographic or clinical variables between the two eras. No statistically significant difference in freedom from rejection or freedom from RHC was identified between the two eras. There was a 60% reduction in the median number of EMB per patient in the first year post-HT after employing the score (5 in Era 1 vs. 2 in Era 2, p < 0.001). CONCLUSIONS: After employing a rejection risk prediction score, our center decreased the frequency of EMB without worsening early post-HT outcomes, thus establishing the clinical applicability of this tool.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".