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Pei, Lulu X.; Leepile, Tebogo T.; Kroeun, Hou; Cochrane, Kelsey; Samson, Kaitlyn; Fischer, Jordie A. J.; Williams, Brock; Bonifacio, Lizl; Karakochuk, Crystal D.

Multidisciplinary Digital Publishing Institute

Genetic hemoglobinopathies are the most common single-gene disorder worldwide. Some automated hematology analyzers have the capability of flagging individuals who may have hematological disorders based on complete blood count (CBC) biomarkers. We aimed to evaluate the accuracy of a hematology analyzer in identifying genetic hemoglobinopathies in Cambodian women and to determine which hematological biomarkers are the best predictors. A CBC was completed using a Sysmex XN-1000 analyzer and hemoglobinopathies were determined with capillary hemoglobin electrophoresis for 808 nonpregnant Cambodian women. Sysmex XN-1000 Interpretive Program (IP) messages, which flag potential hematological disorders, were produced from CBC results. Then, 2 × 2 tables were used to determine sensitivity and specificity of the IP message “Hemoglobin defect” to detect a genetic hemoglobinopathy. Receiver operating characteristic (ROC) analyses assessed the diagnostic ability of six CBC biomarkers to predict a genetic hemoglobinopathy. In total, 74% of women had a hemoglobinopathy (predominantly Hb E and α-thalassemia). “Hb defect” IP message sensitivity and specificity for genetic hemoglobinopathy detection were 10.4% and 98.6%, respectively. Variable selection strategies yielded a two-variable model including mean corpuscular volume (MCV) and red blood cell (RBC) count (AIC = 99.83, AUCROC = 0.98 (95% CI: 0.97, 0.99)) for the prediction of a homozygous EE disorder. Sensitivity and specificity values do not justify the use of Sysmex XN-1000 IP flag messages for identification of genetic hemoglobinopathies in Cambodian women. Development of an algorithm based on MCV and RBC biomarkers may optimize the screening ability of automated hematology analyzers.

Article

eng

Vancouver : University of British Columbia Library

10.14288/1.0396081

Land and Food Systems, Faculty of; Science, Faculty of; Non UBC; Statistics, Department of

10.3390/diagnostics11020228

Faculty; Researcher

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