BackgroundAllograft failure is common in lung-transplant recipients and leads to poor outcomes including early death. No reliable clinical tools exist to identify patients at high risk for allograft failure. This study tested the use of donor-derived cell-free DNA (%ddcfDNA) as a sensitive marker of early graft injury to predict impending allograft failure.MethodsThis multicenter, prospective cohort study enrolled 106 subjects who underwent lung transplantation and monitored them after transplantation for the development of allograft failure (defined as severe chronic lung allograft dysfunction [CLAD], retransplantation, and/or death from respiratory failure). Plasma samples were collected serially in the first three months following transplantation and assayed for %ddcfDNA by shotgun sequencing. We computed the average levels of ddcfDNA over three months for each patient (avddDNA) and determined its relationship to allograft failure using Cox-regression analysis.FindingsavddDNA was highly variable among subjects: median values were 3·6%, 1·6% and 0·7% for the upper, middle, and low tertiles, respectively (range 0·1%–9·9%). Compared to subjects in the low and middle tertiles, those with avddDNA in the upper tertile had a 6·6-fold higher risk of developing allograft failure (95% confidence interval 1·6–19·9, p = 0·007), lower peak FEV1 values, and more frequent %ddcfDNA elevations that were not clinically detectable.InterpretationLung transplant patients with early unresolving allograft injury measured via %ddcfDNA are at risk of subsequent allograft injury, which is often clinically silent, and progresses to allograft failure.FundNational Institutes of Health.

BACKGROUND: Improved understanding of lung transplant disease states is essential because failure rates are high, often due to chronic lung allograft dysfunction. However, histologic assessment of lung transplant transbronchial biopsies (TBBs) is difficult and often uninterpretable even with 10 pieces. METHODS: We prospectively studied whether microarray assessment of single TBB pieces could identify disease states and reduce the amount of tissue required for diagnosis. By following strategies successful for heart transplants, we used expression of rejection-associated transcripts (annotated in kidney transplant biopsies) in unsupervised machine learning to identify disease states. RESULTS: All 242 single-piece TBBs produced reliable transcript measurements. Paired TBB pieces available from 12 patients showed significant similarity but also showed some sampling variance. Alveolar content, as estimated by surfactant transcript expression, was a source of sampling variance. To offset sampling variation, for analysis, we selected 152 single-piece TBBs with high surfactant transcripts. Unsupervised archetypal analysis identified 4 idealized phenotypes (archetypes) and scored biopsies for their similarity to each: normal; T-cell-mediated rejection (TCMR; T-cell transcripts); antibody-mediated rejection (ABMR)-like (endothelial transcripts); and injury (macrophage transcripts). Molecular TCMR correlated with histologic TCMR. The relationship of molecular scores to histologic ABMR could not be assessed because of the paucity of ABMR in this population. CONCLUSIONS: Molecular assessment of single-piece TBBs can be used to classify lung transplant biopsies and correlated with rejection histology. Two or 3 pieces for each TBB will probably be needed to offset sampling variance.

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The incidence of DVT and PE in lung transplant recipients is high. Posttransplant surveillance and treatment based on findings are warranted.