Diagnostic accuracy of automated hematology analyzer abnormal flags for detecting hematological malignancies: A systematic review and meta-analysis

Background: Hematological malignancies including leukemia, lymphoma, and myelodysplastic syndromes, are characterized by clonal proliferation of abnormal blood or bone marrow cells. Early and accurate detection is essential for improving treatment outcomes and survival. Automated hematology analyzers generate abnormal flags that may indicate underlying hematologic malignancies; however, their overall diagnostic accuracy has not been comprehensively evaluated. This systematic review and meta-analysis aimed to assess the diagnostic performance of abnormal flags for detecting hematological malignancies. Methods: A systematic search of PubMed, PubMed Central, Scopus, ScienceDirect, and Google Scholar was conducted to identify relevant diagnostic accuracy studies. Methodological quality was evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2(QUADAS-2) tool. Pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were calculated using a bivariate random-effects model in Stata version 17.0. Heterogeneity was assessed using the I2 statistic, and subgroup and meta-regression analyses were performed to explore potential sources of variability. Results: Twenty-eight studies met the inclusion criteria. The pooled sensitivity and specificity of abnormal hematology analyzer flags for detecting hematological malignancies were 91% (95% CI: 87%–94%) and 89% (95% CI: 84%–92%), respectively, indicating good diagnostic accuracy. Significant heterogeneity was observed across studies (I2 > 50%). Meta-regression analysis identified the type of abnormal flag as a significant source of heterogeneity in sensitivity (p