Abstract P013: Validating The Utility Of Plasma Biomarkers In The Prediction Of Readmission Or Mortality Following Congenital Heart Surgery
Bibliographic record
Abstract
Introduction: Unplanned readmission is associated with higher risks of complications, death, and increased costs. Accurate statistical models to stratify the risk of 30-day readmission or death after congenital heart surgery could help clinical teams focus care on those patients at highest risk. We hypothesized biomarkers could improve prediction for readmission or mortality. Methods: Levels of pre- and postoperative ST2, Galectin-3, NT-proBNP and GFAP were measured in plasma samples from 162 pediatric congenital heart surgery patients from Johns Hopkins Hospital with external validation in 360 pediatric patients from an international multi-center TRIBE-AKI cohort. A model based on clinical variables from the Society of Thoracic Surgery Congenital database (STS-CHSD) was developed in the Hopkins cohort. We tested and externally validated the clinical models and biomarker panels in the TRIBE-AKI cohort using AUROC statistics and Kaplan-Meier cox hazard regression models. Results: There were 55 patients (10.5%) that experienced unplanned readmission or died within 30 days after congenital heart surgery. The STS-CHSD clinical model resulted in an AUROC of 0.617 (95% CI: 0.47 - 0.76). The derivation cohort with the biomarker augmented STS-CHSD clinical model resulted in a significantly improved AUROC of 0.802 (95%CI: 0.72 - 0.89; p=0.003). External validation of the biomarker augmented STS-CHSD clinical model showed limited improvement (AUROC: 0.60; 95% CI: 0.49-0.72; p value= 0.47). Conclusions: Our findings indicate that these biomarkers can be used for early identification of children at increased risk of readmission or death after pediatric congenital heart surgery. While the addition of biomarkers improve prediction in our derivation cohort, external validation was poor. Our findings suggest there are other potential biomarkers and factors to be explored to improve prediction of readmission or mortality for children following congenital heart surgery.
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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 | 0.017 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".