Minimally and Non-invasive Approaches to Rejection Identification in Vascularized Composite Allotransplantation

Objective: Rejection is common and pernicious following Vascularized Composite Allotransplantation (VCA). Current monitoring and diagnostic modalities include the clinical exam which is subjective and biopsy with dermatohistopathologic Banff grading, which is subjective and invasive. We reviewed literature exploring nonand minimally invasive modalities for diagnosing and monitoring rejection (NIMMs) in VCA. Methods: PubMed, Cochrane, and Embase databases were queried, 3125 unique articles were reviewed, yielding 26 included studies exploring 17 distinct NIMMs. Broadly, NIMMs involved Imaging, Liquid Biomarkers, Epidermal Sampling, Clinical Grading Scales, and Introduction of Additional Donor Tissue. Results: Serum biomarkers including MMP3 and donor-derived microparticles rose with rejection onset. Epidermal sampling non-invasively enabled measurement of cytokine & gene expression profiles implicated in rejection. Both hold promise for monitoring. Clinical grading scales were useful diagnostically as was reflection confocal microscopy. Introducing additional donor tissue showed promise for preemptively identifying rejection but requires additional allograft tissue burden for the recipient. Conclusion: NIMMs have the potential to dramatically improve monitoring and diagnosis in VCA. Many modalities show promise however, additional research is needed and a multimodal algorithmic approach should be explored.

Automated summary

Rejection following Vascularized Composite Allotransplantation (VCA) is a common and harmful issue. Current monitoring and diagnostic methods are subjective and invasive. This review explored nonand minimally invasive modalities (NIMMs) for diagnosing and monitoring rejection in VCA. NIMMs, such as imaging, liquid biomarkers, epidermal sampling, clinical grading scales, and introduction of additional donor tissue, have shown promise for improved monitoring and diagnosis. However, further research is needed and a multimodal algorithmic approach should be explored.