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Automated differential leukocyte counts.

Authors: E E, Morse; A, Nashed; L, Spilove;

Automated differential leukocyte counts.

Abstract

Automated differential counts have the advantage of precision, efficiency, safety, and economy. They could potentially serve effectively in 90 percent of patients with normal counts or in 75 percent of patients with anemia only (64 percent of the total in this study). Even patients with increased white blood cell counts and major population shifts (toward granulocytes or lymphocytes) could be followed with automated differential counts. Such a tactic would decrease turnaround time for results, be less expensive, and reduce exposure of technologists to direct contact with patients' blood. However, presently available instruments fail to detect patients' blood samples with small numbers of abnormal cells, e.g., blasts in early relapse of acute leukemia, atypical lymphocytes in viral diseases such as infectious mononucleosis, eosinophils in allergic or parasitic disease, and band forms in early infectious diseases. Clinical judgment should be used in selectively ordering manual differential counts for these patients. While automated differential counts can be very useful in screening general medical and surgical patients in the ambulatory setting, in referral centers where hematologic abnormalities are more prevalent, the manual differential count and further examination of a smear is particularly necessary at least on initial presentation. Selective manual differential counts may improve efficiency, economy, and safety while not compromising patient care. Further studies of the correlation of clinical disease with automated differential counts are necessary.

Related Organizations
Keywords

Leukocyte Count, Autoanalysis, Evaluation Studies as Topic, Humans, Electronics, Flow Cytometry

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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
6
Average
Top 10%
Average
Related to Research communities
Cancer Research
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