Sparing the Prod: Providing an Alternative to Endomyocardial Biopsies With Noninvasive Surveillance After Heart Transplantation During COVID-19
Bibliographic record
Abstract
Background The COVID-19 pandemic has reduced access to endomyocardial biopsy (EMB) rejection surveillance in heart transplant (HT) recipients. This study is the first in Canada to assess the role for noninvasive rejection surveillance in personalizing titration of immunosuppression and patient satisfaction post-HT. Methods In this mixed-methods prospective cohort study, adult HT recipients more than 6 months from HT had their routine EMBs replaced by noninvasive rejection surveillance with gene expression profiling (GEP) and donor-derived cell-free DNA (dd-cfDNA) testing. Demographics, outcomes of noninvasive surveillance score, hospital admissions, patient satisfaction, and health status on the medical outcomes study 12-item short-form health survey (SF-12) were collected and analyzed, using t tests and χ 2 tests. Thematic qualitative analysis was performed for open-ended responses. Results Among 90 patients, 31 (33%) were enrolled. A total of 36 combined GEP/dd-cfDNA tests were performed; 22 (61%) had negative results for both, 10 (27%) had positive GEP/negative dd-cfDNA results, 4 (11%) had negative GEP/positive dd-cfDNA results, and 0 were positive on both. All patients with a positive dd-cfDNA result (range: 0.19%-0.81%) underwent EMB with no significant cellular or antibody-mediated rejection. A total of 15 cases (42%) had immunosuppression reduction, and this increased to 55% in patients with negative concordant testing. Overall, patients' reported satisfaction was 90%, and on thematic analysis they were more satisfied, with less anxiety, during the noninvasive testing experience. Conclusions Noninvasive rejection surveillance was associated with the ability to lower immunosuppression, increase satisfaction, and reduce anxiety in HT recipients, minimizing exposure for patients and providers during a global pandemic.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".