Commercial controls tell only part of a quality story. While they detect analytical drift and estimate precision, they are often run when an instrument is in a pristine, "ready" state (after maintenance, before sample runs, etc.). And because they are not fresh whole blood samples, they don't react quite the same as your daily workload.
Many laboratories also run patient sample controls as a cost-effective way to check instrument performance throughout the day. A previously analyzed sample is run on the same or different analyzer, or in a different sampling mode. But tracking a moving average of red blood cell indices (MCV, MCH, MCHC) of all samples is another approach in hematology. One algorithm, proposed by Dr. Brian Bull of Loma Linda University of California in 1974, does just that.
Red Cell Indices
Red blood cells are biconcave disks able to squeeze, bend and twist through blood vessels. Mature RBCs lack nuclei, mitochondria and other organelles but are packed with hemoglobin, a complex protein containing iron. Hemoglobin picks up oxygen across diffuse membranes in lung sacs called alveoli.1
RBC survival rates range from 15 days (abnormal) to 2-3 weeks (donor transfusion) to normal life span (120 days).2 A four-month life span means a stable population of cells in the blood stream. Thus, physiological ratios (e.g., the amount of hemoglobin in a red cell) do not vary significantly in patients without anemia or hemoglobin variants. The most commonly measured ratios are RBC indices.
RBC indices classify anemias by how red cells are structurally altered. Too little hemoglobin, for example, results in smaller than normal cells (decreased MCV, or microcytic appearance). Microcytic anemias can be caused by iron deficiency, thallasemia, chronic infection, etc. RBC indices are an important part of a differential diagnosis.3
On the bench, they can identify analytical error. RBC indices are described in Table 1, along with suggested action if change is observed, in Table 2. A change in MCH alone, for example, suggests a problem in hemoglobin measurement.
RBC indices are not the only ratios in use to differentiate anemias (see Table 3).
Bull's Moving Averages
"Smoothing" is a statistical technique used by financial forecasters to remove short-term market fluctuations.4 In the laboratory, fluctuations are abnormal patient results (anemia, polycythemia, microcytosis, macrocytosis, etc.). Smoothing algorithms include truncation (arbitrarily discarding low or high values), logarithmic transformation, reducing the weight of a small sample mean by a multiplier, or a combination of these. Software such as Microsoft Excel can perform smoothing with added functions (Excel's "smoothed line" option creates a Bezier curve for aesthetic purposes).5 6
Bull designed a weighted moving average for the Coulter Model S, starting with expected population normals (90 fL for MCV, etc.). His smoothing algorithm, designed to track normal patients, is described by Dr. Roy Barnett in his book Clinical Laboratory Statistics.7
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