Journal
JOURNAL OF HEART AND LUNG TRANSPLANTATION
Volume 38, Issue 6, Pages 636-646Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.healun.2019.01.1318
Keywords
antibody-mediated rejection; T-cell-mediated rejection; injury; heart transplant; microarray
Funding
- Genome Canada
- Canada Foundation for Innovation
- University of Alberta Hospital Foundation
- Alberta Ministry of Advanced Education and Technology
- Mendez National Institute of Transplantation Foundation
- Industrial Research Assistance Program
- Roche Organ Transplant Research Foundation
- One Lambda division of Thermo Fisher
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BACKGROUND: We previously reported a microarray-based diagnostic system for heart transplant endomyocardial biopsies (EMBs), using either 3-archetype (3AA) or 4-archetype (4AA) unsupervised algorithms to estimate rejection. In the present study we examined the stability of machine-learning algorithms in new biopsies, compared 3AA vs 4AA algorithms, assessed supervised binary classifiers trained on histologic or molecular diagnoses, created a report combining many scores into an ensemble of estimates, and examined possible automated sign-outs. METHODS: We studied 889 EMBs from 454 transplant recipients at 8 centers: the initial cohort (N = 331) and a new cohort (N = 558). Published 3AA algorithms derived in Cohort 331 were tested in Cohort 558, the 3AA and 4AA models were compared, and supervised binary classifiers were created. RESULTS: A'lgorithms derived in Cohort 331 performed similarly in new biopsies despite differences in case mix. In the combined cohort, the 4AA model, including a parenchymal injury score, retained correlations with histologic rejection and DSA similar to the 3AA model. Supervised molecular classifiers predicted molecular rejection (areas under the curve [AUCs] >0.87) better than histologic rejection (AUCs <0.78), even when trained on histology diagnoses. A report incorporating many AA and binary classifier scores interpreted by 1 expert showed highly significant agreement with histology (p < 0.001), but with many discrepancies, as expected from the known noise in histology. An automated random forest score closely predicted expert diagnoses, confirming potential for automated signouts. CONCLUSIONS: Molecular algorithms are stable in new populations and can be assembled into an ensemble that combines many supervised and unsupervised estimates of the molecular disease states. (C) 2019 International Society for Heart and Lung Transplantation. All rights reserved.
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