A decision-making tool to fine-tune abnormal levels in the complete blood count tests

TL;DR

This paper introduces a decision support tool that uses a cost-sensitive Lasso-penalized additive logistic regression to identify CBC variables associated with higher risks of abnormal blood smears, aiding technologists in decision-making.

Contribution

It presents a novel, data-driven method combining stability selection and logistic regression to accurately determine cutoff values for CBC variables, improving upon existing manual review criteria.

Findings

Accurately identifies true cutoff values with sufficient data.

Demonstrates effectiveness on both simulated and real CBC data.

Supports decision-making in laboratory settings.

Abstract

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.