Hematology · Medical Literature
Diagnostic accuracy of automated hematology analyzer abnormal flags for detecting hematological malignancies: A systematic review and meta-analysis.
Abstract
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. 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. 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 < 0.001), whereas both the type of abnormal flag and the analyzer platform significantly influenced specificity. Automated hematology analyzer abnormal flags showed promising diagnostic performance. However, substantial heterogeneity and differences in analyzer platforms, flag types, and reference standards reduce the certainty and generalizability of pooled estimates. Nevertheless, these findings support the use of abnormal hematology analyzer flags as an effective initial screening tool in routine laboratory practice, particularly in resource-limited settings where rapid and cost-effective diagnostic support is essential. Systematic review registration PROSPERO (CRD42024601908).
Clinical relevance
Hematological malignancies — including leukemia, lymphoma, and myelodysplastic syndromes — require early, accurate detection to optimize treatment outcomes and survival, yet a comprehensive evidence synthesis of automated hematology analyzer abnormal flags as a screening tool had not previously been performed. This meta-analysis of 28 diagnostic accuracy studies addresses that gap, providing the first pooled estimates of sensitivity and specificity for these flags across diverse clinical and laboratory settings. The findings are particularly timely given increasing demands for cost-effective, high-throughput screening strategies, especially in resource-limited environments where advanced confirmatory diagnostics may be inaccessible.
Key findings
1. Pooled sensitivity of automated hematology analyzer abnormal flags for detecting hematological malignancies was 91% (95% CI: 87%–94%), indicating a low false-negative rate suitable for an initial screening tool. 2. Pooled specificity was 89% (95% CI: 84%–92%), reflecting a relatively low false-positive burden and reducing unnecessary downstream workup in unaffected patients. 3. Significant heterogeneity was observed across the 28 included studies (I² > 50%), limiting the certainty and direct generalizability of the pooled estimates. 4. Meta-regression identified the type of abnormal flag as a statistically significant source of heterogeneity in sensitivity (p < 0.001), underscoring that flag-specific performance varies meaningfully. 5. Both the type of abnormal flag and the analyzer platform were significant independent predictors of heterogeneity in specificity, highlighting that platform choice influences diagnostic performance. 6. Methodological quality was assessed using the QUADAS-2 tool, and pooled diagnostic odds ratio, positive likelihood ratio, and negative likelihood ratio were derived via a bivariate random-effects model, though specific LR and DOR values were not reported in the abstract and cannot be precisely cited here.
Practice implications
Automated hematology analyzer abnormal flags demonstrate sufficiently high pooled sensitivity (91%) and specificity (89%) to support their routine use as a first-line triage screen for hematological malignancies in both high-volume and resource-limited laboratory settings, potentially expediting referral for confirmatory morphological review, flow cytometry, or bone marrow biopsy. Clinicians and laboratory directors should recognize that diagnostic performance is significantly influenced by flag type and analyzer platform, and institutional validation of the specific analyzer and flag algorithms in use is essential before applying these pooled estimates to local practice. Given the substantial inter-study heterogeneity, a positive or negative flag result should always be interpreted in conjunction with clinical presentation and peripheral blood morphology rather than used as a standalone diagnostic determinant.