Vascularized Composite Allotransplantation (VCA) is a reconstructive option that involves transplantation of composite tissues, such as skin, muscle, and bone, in one surgical procedure. However, graft rejection remains a major challenge that needs to be addressed to improve patient outcomes.

To address this challenge, scientists at Johns Hopkins University (JHU), Henry M. Jackson Foundation for the Advancement of Military Health (HJF) and Uniformed Services University of the Health Sciences (USUHS) have developed a Clinical Decision Support Tool (CDST) based on Infrared (IR) and Three Charge-Coupled Device (3CCD) imaging technology that can specifically monitor the immunological status of an allograft.

Applications and Advantages

• Current technologies for monitoring rejection post-VCA include traumatic and invasive punch biopsies for histological assessment as well as clinical evaluation
• Proposed CDST uses the non-invasive, imaging technologies 3CCD and near IR, for more sensitive, and advance detection of graft rejection when compared to clinical exam
• Imaging data is combined with computational models to develop a “rejection algorithm” and dashboard analysis to enable monitoring and early intervention by surgeons

Innovation Description

VCA allows one to restore the appearance, anatomy, and function of defects that cannot be repaired with conventional techniques. It is considered as the most feasible solution for management of military ballistic injuries to the face and neck, scalp, calvarial reconstruction, and aesthetic reconstruction in burn patients, etc.

As VCA may elicit strong immune responses, routine clinical application of VCA is limited, despite successful results. Rejection post-VCA is currently monitored through traumatic and invasive punch biopsies for histological assessment as well as clinical evaluation.

Our researchers developed a non-invasive CDST that uses seven features to maximize predictive accuracy for “rejection” or “non-rejection”. It includes three 3CCD measures of oxygenation, three measures of blood perfusion, and monitoring the presence or absence of 13 pro- or anti-inflammatory biomarkers. Compared against existing rejection monitoring methods, it is sensitive, non-invasive, not traumatic, precise and predictive of potential graft rejection. A schematic representation of the CDST tool development is shown in Fig. 1.
 

Figure 1: Pipeline for clinical decision
support tool (CDST) development

Inventors

• Renhua Li, Ph.D., HJF
• Gerald Brandacher, Ph.D., JHU
• Oh Byoung Chol, Ph.D., JHU
• Seth Schobel, Ph.D., HJF
• Eric A. Elster, M.D., USU

Innovation Status

The predictive model was successfully developed and tested in swine and tentatively validated by using data from human upper extremity transplant recipients. Further validation in human VCA transplant recipients is planned.

Intellectual Property Status

A provisional application has been filed.