Redefining the molecular rejection states in 3230 heart transplant biopsies: Relationships to parenchymal injury and graft survival.
Philip F HalloranKatelynn Madill-ThomsenArezu Z Aliabadi-ZuckermannMartin CadeirasMarisa G Crespo-LeiroEugene C DepasqualeMario DengJohannes GöklerShelley HallAayla JamilDaniel H KimJon KobashigawaPeter MacdonaldVojtech MelenovskyJignesh PatelLuciano PotenaKeyur ShahJosef StehlikAndreas ZuckermannPublished in: American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons (2024)
The first-generation Molecular Microscope (MMDx) system for heart transplant endomyocardial biopsies used expression of rejection-associated transcripts (RATs) to diagnose not only T cell-mediated rejection (TCMR) and antibody-mediated rejection (ABMR) but also acute injury. However, the ideal system should detect rejection without being influenced by injury, to permit analysis of the relationship between rejection and parenchymal injury. To achieve this, we developed a new rejection classification in an expanded cohort of 3230 biopsies: 1641 from INTERHEART (ClinicalTrials.gov NCT02670408), plus 1589 service biopsies added to improve the power of the machine learning algorithms. The new system used 6 rejection classifiers instead of RATs and generated 7 rejection archetypes: No rejection, 48%; Minor, 24%; TCMR1, 2.3%; TCMR2, 2.7%; TCMR/mixed, 2.7%; early-stage ABMR, 3.9%; and fully developed ABMR, 16%. Using rejection classifiers eliminated cross-reactions with acute injury, permitting separate assessment of rejection and injury. TCMR was associated with severe-recent injury and late atrophy-fibrosis and rarely had normal parenchyma. ABMR was better tolerated, seldom producing severe injury, but in later biopsies was often associated with atrophy-fibrosis, indicating long-term risk. Graft survival and left ventricular ejection fraction were reduced not only in hearts with TCMR but also in hearts with severe-recent injury and atrophy-fibrosis, even without rejection.
Keyphrases
- machine learning
- early stage
- left ventricular
- heart failure
- ejection fraction
- healthcare
- deep learning
- early onset
- atrial fibrillation
- liver failure
- acute myocardial infarction
- hepatitis b virus
- radiation therapy
- lymph node
- extracorporeal membrane oxygenation
- percutaneous coronary intervention
- hypertrophic cardiomyopathy
- left atrial