Characterization of Face Transplant Candidates Evaluated at Cleveland Clinic and Algorithm to Maximize Efficacy of Screening Process

Introduction As a high-volume referral center for facial transplantation, we have learned significantly from the screening, evaluation, and enrollment process. This report analyzes our algorithm for the assessment of potential face transplant candidates referred to our institution. Methods After institutional review board approval in 2004, a prospectively maintained database was created for patients who were referred face transplant. Records were reviewed for the nature of tissue defect, functional deficit, surgical and medical history, and expert recommendations. Our algorithm begins with a review of a patient's file with a focus on institutional review board criteria. After screening, a phone interview is conducted, and transplantation is discussed. Patients are presented to the team to analyze the medical, psychiatric, and surgical history; support network; and geographic location. Eligible patients are invited for an in-person evaluation, and the case is reviewed again with the team. If approved, the patient can provide consent for transplantation. Results More than 200 patients were referred for transplant evaluation at the Cleveland Clinic from 2004 to 2016. Sixty were eligible for further evaluation for face transplantation based on preliminary screening. Thirteen (6.5% of original cohort) were invited for in-person evaluation and physical examination. Five (2.5% of original cohort, 38.4% invited cohort) of these 13 patients underwent face transplantation, of whom, 3 (1.5% of original cohort, 23.1% invited cohort) underwent face transplantation at our institution. All 3 patients who were ultimately transplanted were referred by a physician. Discussion As the availability of public information on face transplant increases, it is likely that an increase in self-referral for face transplantation will occur. Thus, it is critical that institutions adopt a systematic approach to triage in order to identify appropriate patients. Our algorithm allowed for a high enrollment and transplantation ratio to save patient and institution time and resources. This could be easily adopted by other institutions to save time, money, and resources.