What has changed about hematology testing—and what hasn’t—since the advent of automated analyzers?
Healthcare professionals have been challenged in evaluating peripheral blood smears essentially since the invention of the microscope. When the first automated hematology analyzers appeared in clinical laboratories in the 1960s, they ushered in a welcomed workflow change for bench technologists. These automated analyzers replaced hemocytometers, though the need for differential counting remained.
This evolution in hematology workflows has continued to this day, with automated instruments performing ever more cellular analysis, resulting in more focused roles for technologists and pathologists. However, certain characteristics of peripheral blood morphology still do not lend themselves easily to evaluation by automated analyzers.
What types of cells do automated hematology analyzers have difficulty classifying?
One limitation that has remained constant from the earliest hematology analyzers to today’s cutting-edge flow cytometers is that a single cell still must pass through an aperture for analysis. In order to maintain laminar flow, the cell must also be sphered, which is most often accomplished with a proprietary sphering reagent. Consequently, automated instruments don’t easily analyze certain characteristics of red cells, such as shape. Most current analyzers measure parameters, such as red cell distribution width, that approximate anisocytosis—or abnormal variation in the size of red blood cells. But the exact classification of abnormally shaped red cells, such as sickle cell, target cell, schistocyte, etc., still requires morphologic review of stained slides.
Additionally, red cell and white cell inclusions—particularly infectious organisms such as malaria or histoplasmosis—can be seen in stained blood smears but are not routinely detected by most automated hematology analyzers. Because of the extensive morphologic variability of many circulating hematologic malignancies, automated systems cannot precisely characterize these cells. Most analyzers, however, aid in characterizing these cells by pre-classifying them as abnormal (through large unstained cell classification or flagging) and prompting manual review of slides.
Will emerging digital morphology solutions replace manual differential counts?
Analyzers that have digital morphology capabilities, such as Beckman Coulter’s CellaVision or Roche’s Bloodhound systems, are inaugurating a new era of cellular analysis. As these instruments’ algorithms continue to be refined, this technology might evolve from a pre-classifier method to a more enhanced and robust method for precise characterization.
The accuracy of an automated differential count depends on the analytical system used. However, given that most automated counters literally characterize thousands of white cells for each analysis, the classic 100-cell manual differential count in comparison falls short when it comes to precision.
The algorithms used in digital morphology systems are currently accurate enough to select which smears require manual review. The cell-by-cell images produced in this process, though, one day might eliminate today’s standard reflex review of slides.
To learn more, attend Dr. Flax’s Brown Bag session at the 69th AACC Annual Scientific Meeting & Clinical Lab Expo, “Examination of the Peripheral Blood Smear,” on Monday, July 31, from 7:30–8:30 a.m. or 12:30–1:30 p.m. in the San Diego Convention Center, San Diego.
Sherri D. Flax, MD, is a clinical associate professor of pathology at the University of Florida in Gainesville.+Email: firstname.lastname@example.org