Graft vasculopathy of vascularized composite allografts in humans: a literature review and retrospective study.
Zhi Yang NgAlexandre G LellouchIvy A RosalesLuke GeogheganAmon-Ra GamaRobert B ColvinLaurent A LantieriMark A RandolphCurtis L CetruloPublished in: Transplant international : official journal of the European Society for Organ Transplantation (2019)
Mechanisms of chronic rejection of vascularized composite allografts (VCA) remain poorly understood and likely present along a spectrum of highly varied clinicopathological findings. Across both animal and human VCA however, graft vasculopathy (GV) has been the most consistent pathological finding resulting clinically in irreversible allograft dysfunction and eventual loss. A literature review of all reported clinical VCA cases with documented GV up to December 2018 was thus performed to elucidate the possible mechanisms involved. Relevant data extracted include C4d deposition, donor-specific antibody (DSA) formation, extent of human leukocyte antigen (HLA) mismatch, pretransplant panel reactive antibody levels, induction and maintenance immunosuppression used, the number of preceding acute rejection episodes, and time to histological confirmation of GV. Approximately 6% (13 of 205) of all VCA patients reported to date developed GV at a mean of 6 years post-transplantation. 46% of these patients have either lost or had their VCAs removed. Neither C4d nor DSA alone was predictive of GV development; however, when both are present, VCA loss appears inevitable due to progressive GV. Of utmost concern, GV in VCA does not appear to be abrogated by currently available immunosuppressive treatment and is essentially irreversible by the time of diagnosis with allograft loss a likely eventuality.
Keyphrases
- end stage renal disease
- ejection fraction
- newly diagnosed
- endothelial cells
- chronic kidney disease
- oxidative stress
- prognostic factors
- peritoneal dialysis
- case report
- multiple sclerosis
- induced pluripotent stem cells
- patient reported outcomes
- liver failure
- machine learning
- electronic health record
- intensive care unit
- deep learning
- pluripotent stem cells
- mesenchymal stem cells