Impact of pre-analytical variables - temperature, agitation, storage duration, and blood-to-anticoagulant ratio - on...

The complete blood count is a widely used medical laboratory test that is essential for both diagnosis and treatment decisions. While pre-analytic factors, such as storage temperature, transport conditions, blood volume anticoagulant ratio, and testing delay, might impact the accuracy of the results, the degree of this influence requires further quantification. In the present study, we aim to look more closely at these factors and how they affect complete blood count test readings.

This study evaluates the effects of temperature, agitation, storage duration, and blood volume anticoagulant ratio on complete blood count reliability using a repeated-measures factorial design. A total of 224 CBC runs were performed on the samples of eight participants. Samples were tested at baseline T0 and again at 4, 12, and 24 hours after storage at either room temperature (22°C) or refrigeration (4°C), with and without agitation, and at both optimal (2 mL) and suboptimal (1 mL) blood volumes.

The duration of storage was the main factor determining CBC accuracy. After 12 h, WBC counts decreased significantly (p < 0.001, η² = 0.129). Platelet counts also declined (p < 0.001, η² = 0.73), with greater loss at 4°C (-8.1%) than at 22°C (-5.2%) by 24 h. MCV remained stable at 4°C but increased at 22°C (p < 0.001). Hemoglobin decreased at 22°C (-2.3%, p < 0.05), and RBC counts dropped modestly (-2.2%, p < 0.05). MPV showed only a minor change (p > 0.05). Neither agitation nor suboptimal blood volume had significant effects (p > 0.05).

Specimens should ideally be tested within 12 hours or stored at 4°C to prevent degradation of the test variables, particularly white blood cell counts and platelet counts. The findings suggest that routine handling and transport conditions are appropriate, as agitation during transport and a decreased blood volume-to-anticoagulant ratio appear to have negligible effects. Incorporating measurement uncertainty benchmarks improves the distinction between genuine sample deterioration and assay variability, thereby enhancing diagnostic accuracy.