ArticleDiagnostics (Basel, Switzerland)2021
Can Automated Hematology Analyzers Predict the Presence of a Genetic Hemoglobinopathy? An Analysis of Hematological Biomarkers in Cambodian Women.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed, 5 citations in OpenAlex.
- Evaluation of machine learning pipeline for blood culture outcome prediction on prospectively collected emergency department data.Journal of medical microbiology · 2026Article
- "Infectious mononucleosis" flag, high-fluorescence lymphocyte percentage, and platelet-to-lymphocyte ratio as diagnostic and prognostic biomarkers for infectious mononucleosis in Chinese children.Italian journal of pediatrics · 2025Article
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Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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, AUC
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