HEARTBIT
Bibliographic record
Abstract
Background Acute cellular allograft rejection remains a major cause of limited long-term graft survival in cardiac transplantation. A critical challenge in trying to reduce the incidence of acute cellular rejection arises from the difficulty of accurately and efficiently making diagnoses. The primary approach requires taking small pieces of heart muscle tissue, which is highly invasive and costly, and suffers from sampling error and inter-observer grading variability. Replacing the biopsy with a simple blood test would be of great value to patients and substantially reduce healthcare costs. Approach and Methods We previously used high-throughput, untargeted transcriptomic profiling in blood samples of heart transplant patients to identify 9 mRNAs and 5 proteins whose combined expression discriminated patients undergoing acute cellular rejection from those who were not. We now validated the mRNA targets on a clinically-amenable NanoString nCounter platform. The performance of the novel assay, HEARTBIT, as well as that of a proteogenomic ensemble including 5 proteins, was assessed by cross-validation. Results In cross-validation the area under the receiver operating characteristic curve (AUC) of the transcriptomic signature was 0.81, with 47% specificity at ≥ 90% sensitivity. Addition of 5 proteins to the mRNA panel using ensembling resulted in an enhanced performance (AUC of 0.86, with 65% specificity at ≥ 90% sensitivity, in cross-validation). Summary and Conclusion Here, we demonstrate 1) successful translation of biomarker signatures from untargeted high-throughput screening onto a compact, clinically-amenable technological platform, and 2) promising utility of our novel assay, HEARTBIT, for improved detection of acute cellular rejection.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.171 | 0.162 |
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".