A Validated Risk Stratification That Incorporates MAGIC Biomarkers Predicts Long-Term Outcomes in Pediatric Patients with Acute GVHD
Bibliographic record
Abstract
Acute graft versus host disease (GVHD) is a common and serious complication of allogeneic hematopoietic cell transplantation (HCT) in children but overall clinical grade at onset only modestly predicts response to treatment and survival outcomes. Two tools to assess risk at initiation of treatment were recently developed. The Minnesota risk system stratifies children for risk of nonrelapse mortality (NRM) according to the pattern of GVHD target organ severity. The Mount Sinai Acute GVHD International Consortium (MAGIC) algorithm of 2 serum biomarkers (ST2 and REG3α) predicts NRM in adult patients but has not been validated in a pediatric population. We aimed to develop and validate a system that stratifies children at the onset of GVHD for risk of 6-month NRM. We determined the MAGIC algorithm probabilities (MAPs) and Minnesota risk for a multicenter cohort of 315 pediatric patients who developed GVHD requiring treatment with systemic corticosteroids. MAPs created 3 risk groups with distinct outcomes at the start of treatment and were more accurate than Minnesota risk stratification for prediction of NRM (area under the receiver operating curve (AUC), .79 versus .62, P = .001). A novel model that combined Minnesota risk and biomarker scores created from a training cohort was more accurate than either biomarkers or clinical systems in a validation cohort (AUC .87) and stratified patients into 2 groups with highly different 6-month NRM (5% versus 38%, P < .001). In summary, we validated the MAP as a prognostic biomarker in pediatric patients with GVHD, and a novel risk stratification that combines Minnesota risk and biomarker risk performed best. Biomarker-based risk stratification can be used in clinical trials to develop more tailored approaches for children who require treatment for GVHD.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".