(P-TS-119) Workflow Optimization and Performance Evaluation of the Integrated Red Blood Cell Antibody Identification Software IH-AbID

Red blood cell (RBC) antibody identification may be a challenging and time-consuming task. The aim of this workflow and performance study is to compare the RBC antibody identification process using current practice to the process using the IH-500 System version 3.1 (IH-500 NEXT) with antibody identification software, IH-A^bID (Bio-Rad Medical Diagnostics GmbH, Germany).

Study

Design/Methods:

The IH-500 NEXT System with IH-A^bID was evaluated at two sites using different RBC antibody identification processes. Current practice at Site 1 uses a fully automated system for RBC antibody screening and identification, with an additional panel used in manual method, as needed. The interpretation is carried out using manual worksheets. Current practice at Site 2 uses an automated system for RBC antibody screening, with manual identification and interpretation. Workflow was evaluated using process mapping to assess turnaround and hands-on times for samples containing single and multiple antibodies, and percent change was calculated. Performance was also evaluated by comparing RBC antibody identification results provided by the IH-500 NEXT System with IH-A^bID to results obtained using current manual interpretation. Agreement was calculated as the number of identical conclusions between IH-A^bID and current processes.

Results/Findings:

The workflow evaluation showed that implementation of IH-A^bID decreased the number of manual steps, touch points and decision points by 70%, 67% and 63%, respectively, at Site 1, and 64%, 67% and 60%, respectively, at Site 2. Manual records were fully eliminated. Automating the antibody identification process improved the turnaround time for a single antibody (by 5% and 8%) and multiple antibodies (by 6% and 15%) at Sites 1 and 2, respectively. Hands-on time was substantially reduced for a single antibody (by 73% and 66%) and multiple antibodies (by 84% and 62%) at Sites 1 and 2, respectively. Overall, 48 selected samples, tested blinded, were evaluated for performance, of which 60% (29) had a single antibody, 19% (9) had 2 antibodies, 8% (4) were pan reactive and 13% (6) were negative. An 81% strict concordance and 92% partial agreement (i.e. common specificity identified with other specificities not ruled out on one method) between both methods was found. The few discrepancies (4) were not due to IH-A^bID but to specificities detected with one panel only.

Conclusions:

The IH-500 NEXT System with IH-A^bID software offers opportunities to improve the antibody identification process by reducing the number of manual steps, touch points and decision points, as well as decreasing the turnaround and hands-on times. The performance evaluation showed excellent concordance with current practices while systemizing the antibody identification process.