A decision-making tool to fine-tune abnormal levels in the complete blood count tests
This provides a tool for medical laboratories to fine-tune CBC review criteria, addressing local adjustments needed for resources and populations, but it is incremental as it builds on existing consensus criteria.
The paper tackled the problem of identifying which complete blood count (CBC) variables and cutoff values are associated with higher risks of abnormal blood smear reviews, proposing a decision support tool that correctly identified true cutoff values when sufficient data was available.
The complete blood count (CBC) performed by automated hematology analyzers is one of the most ordered laboratory tests. It is a first-line tool for assessing a patient's general health status, or diagnosing and monitoring disease progression. When the analysis does not fit an expected setting, technologists manually review a blood smear using a microscope. The International Consensus Group for Hematology Review published in 2005 a set of criteria for reviewing CBCs. Commonly, adjustments are locally needed to account for laboratory resources and populations characteristics. Our objective is to provide a decision support tool to identify which CBC variables are associated with higher risks of abnormal smear and at which cutoff values. We propose a cost-sensitive Lasso-penalized additive logistic regression combined with stability selection. Using simulated and real CBC data, we demonstrate that our tool correctly identify the true cutoff values, provided that there is enough available data in their neighbourhood.