T O Diallo1, M Bergeron1, P Seely2, X Yang1, T Ding3, M Plews1, P Sandstrom1, T B Ball1,4,5, A F A Meyers1,6,5. 1. National HIV Immunology and Retrovirology Laboratories, Public Health Agency of Canada, Winnipeg, Manitoba, Canada. 2. Medical Devices Bureau, Health Canada, Ottawa, Ontario, Canada. 3. Health Products and Food Branch, Health Canada, Ottawa, Ontario, Canada. 4. Department of Medical Microbiology and Immunology, University of Manitoba, Winnipeg, Manitoba, Canada. 5. Department of Medical Microbiology, University of Nairobi, Nairobi, Kenya. 6. Department of Medical Microbiology, University of Manitoba, Winnipeg, Manitoba, Canada.
Abstract
BACKGROUND: Automation in HIV clinical flow cytometry when appropriately applied brings considerable standardisation benefits. The Canadian Immunology Quality Assessment Program (CIQAP) detected situations where operators did not manually override automated software in the event of improper output on the Epics XL and FC500 CD4 immunophenotyping platforms. The automated gating algorithm identifies lymphocytes using a double gate strategy based on CD45 × side scatter (SS) gating and a light scatter FS × SS gate known to fail with sub optimal specimens. METHOD: To generate correct interpretation and results CIQAP introduced a simple protocol modification, bypassing the light scatter gate to include all cells characterized by the CD45 gate. Seventeen problem cases were reanalysed for both absolute and relative T-cell subsets accuracy and compared to the CIQAP group mean values. Results were found to be associated with the percentage of lymphocytes excluded by the automated light scatter gate. RESULTS: The modified manual protocol resolved poor performance in 14 instances out of 17 problem cases. It was found to improve accuracy when the light scatter gate excluded greater than 5% of the cells. The remaining three cases had a lymphocyte recovery of greater than 94.6% in the original automated analysis. CONCLUSION: There is a risk in relying solely on automated gating procedures when using the Epics XL and FC500 CD4 immunophenotyping platforms. Laboratory managers have the responsibility to intervene when required. EQA providers are equally responsible to alert the clinical laboratories of the need to update operator training to deal with stressed specimens.
BACKGROUND: Automation in HIV clinical flow cytometry when appropriately applied brings considerable standardisation benefits. The Canadian Immunology Quality Assessment Program (CIQAP) detected situations where operators did not manually override automated software in the event of improper output on the Epics XL and FC500 CD4 immunophenotyping platforms. The automated gating algorithm identifies lymphocytes using a double gate strategy based on CD45 × side scatter (SS) gating and a light scatter FS × SS gate known to fail with sub optimal specimens. METHOD: To generate correct interpretation and results CIQAP introduced a simple protocol modification, bypassing the light scatter gate to include all cells characterized by the CD45 gate. Seventeen problem cases were reanalysed for both absolute and relative T-cell subsets accuracy and compared to the CIQAP group mean values. Results were found to be associated with the percentage of lymphocytes excluded by the automated light scatter gate. RESULTS: The modified manual protocol resolved poor performance in 14 instances out of 17 problem cases. It was found to improve accuracy when the light scatter gate excluded greater than 5% of the cells. The remaining three cases had a lymphocyte recovery of greater than 94.6% in the original automated analysis. CONCLUSION: There is a risk in relying solely on automated gating procedures when using the Epics XL and FC500 CD4 immunophenotyping platforms. Laboratory managers have the responsibility to intervene when required. EQA providers are equally responsible to alert the clinical laboratories of the need to update operator training to deal with stressed specimens.
Authors: Eustache Paramithiotis; Scott Sugden; Eszter Papp; Marie Bonhomme; Todd Chermak; Stephanie Y Crawford; Stefanie Z Demetriades; Gerson Galdos; Bruce L Lambert; John Mattison; Thomas McDade; Stephane Pillet; Robert Murphy Journal: Front Immunol Date: 2022-05-26 Impact factor: 8.786
Authors: Adrienne F A Meyers; Michèle Bergeron; Madhuri Thakar; Tao Ding; Alexandre Martel; Paul Sandstrom; Bharati Mahajan; Philip Abraham; Sandhya Kabra; Namita Singh; Trevor Peter; Terry B Ball Journal: Afr J Lab Med Date: 2016-10-12