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Abstract Vascularized composite allotransplantation can improve quality of life and restore functionality. However, the complex tissue composition of vascularized composite allografts (VCAs) presents unique clinical challenges that increase the likelihood of transplant rejection. Under prolonged static cold storage, highly damage-susceptible tissues such as muscle and nerve undergo irreversible degradation that may render allografts non-functional. Skin-containing VCA elicits an immunogenic response that increases the risk of recipient allograft rejection. The development of quantitative metrics to evaluate VCAs prior to and following transplantation are key to mitigating allograft rejection. Correspondingly, a broad range of bioanalytical methods have emerged to assess the progression of VCA rejection and characterize transplantation outcomes. To consolidate the current range of relevant technologies and expand on potential for development, methods to evaluate ex vivo VCA status are herein reviewed and comparatively assessed. The use of implantable physiological status monitoring biochips, non-invasive bioimpedance monitoring to assess edema, and deep learning algorithms to fuse disparate inputs to stratify VCAs are identified.

Vascularized composite allotransplantation addresses injuries to complex anatomical structures such as the face, hand, and abdominal wall. Prolonged static cold storage of vascularized composite allografts (VCA) incurs damage and imposes transportation limits to their viability and availability. Tissue ischemia, the major clinical indication, is strongly correlated with negative transplantation outcomes. Machine perfusion and normothermia can extend preservation times. This perspective introduces multiplexed multi-electrode bioimpedance spectroscopy (MMBIS), an established bioanalytical method to quantify the interaction of the electrical current with tissue components, capable of measuring tissue edema, as a quantitative, noninvasive, real-time, continuous monitoring technique to provide crucially needed assessment of graft preservation efficacy and viability. MMBIS must be developed, and appropriate models explored to address the highly complex multi-tissue structures and time-temperature changes of VCA. Combined with artificial intelligence (AI), MMBIS can serve to stratify allografts for improvement in transplantation outcomes.

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