P086 UNOS UPLOAD TOOL (UUT): An easy automated system for updating and confirming unacceptable antigen results recorded in unet from laboratory results

Abstract

UNet is a data repository for transplant candidates and donors and is managed by the United Network for Organ Sharing (UNOS). Our laboratory updates UNet daily with unacceptable antigens for transplant candidates. We have developed and validated the UUT to automate generation of unacceptable antigen data for both upload and receipt confirmation of values recorded in UNet. Unacceptable antigens are calculated from median fluorescent intensity (MFI) from HLA Class I and II LabScreen assays (One Lambda), analyzed on the Luminex 3D, and interpreted using Fusion (One Lamda). Data is stored in HistoTrac (SystemLink) for consultant sign off and reporting to the medical record. UUT interrogates HistoTrac and can be modified to query other databases (E.g. Fusion) using Excel Power Query and VBA. MFI and prozone data is organized to show recurrent patient data over time, highlighting significant signal changes and shifts above or below reporting cutoffs. After data review a file is generated based on unacceptable antigen criteria for export to UNet. Following upload to UNet, a report of unacceptable antigens for all active patients is exported from UNet. This report is imported to the UUT for confirmation of successful import and highlights changes in unacceptable antigens since the last time the patient was recorded. UUT was validated both retrospectively with historic data of patients with complicated histories as well as concurrently with live clinical data. UNet was manually reviewed to confirm that patient data was recorded correctly. Prior to implementing this tool our process required two people and ∼5 min per patient. With this tool the process is now performed by one person at ∼1.2 min per patient. Use of the UUT in the automated upload of data to UNet reduces both workload and the potential for transcription errors while helping to highlight unexpected changes in patient data over time that could indicate technical artifacts.