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Related Experiment Videos

Manual differential cell counts help predict bacterial infection. A multivariate analysis.

M J Wile1, L D Homer, S Gaehler

  • 1Cascade Pathology Group, Legacy Portland Hospitals, Department of Pathology, Emanuel Hospital and Health Center, 2801 N Gantenbein Ave, Portland, OR 97221, USA.

American Journal of Clinical Pathology
|May 11, 2001
PubMed
Summary
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Combining automated and manual blood counts improves bacterial infection prediction. Manual differentials are most beneficial for patients with low neutrophil counts, warranting careful selection due to cost.

Area of Science:

  • Clinical pathology
  • Infectious disease diagnostics
  • Hematology

Background:

  • Accurate prediction of bacterial infection is crucial for timely treatment.
  • Complete blood count (CBC) with differential is a standard diagnostic tool.
  • Automated CBCs offer efficiency, but manual differentials provide detailed cell information.

Purpose of the Study:

  • To develop and evaluate logistic regression models for predicting bacterial infection.
  • To compare the predictive performance of models using automated CBC data alone versus combined automated and manual differential counts.
  • To identify patient subgroups who benefit most from manual differential analysis.

Main Methods:

  • Logistic regression models were developed using data from patients with proven bacterial infection and uninfected controls.

Related Experiment Videos

  • Models incorporated parameters from automated CBC and manual 100-cell differential counts.
  • Model performance was assessed using receiver operating characteristic (ROC) curve analysis.

    • Main Results:

    • A 4-variable model (automated neutrophils, manual bands, immature granulocytes) achieved an ROC area of 89%.
    • A more practical 2-variable model (automated neutrophils, manual bands) showed comparable performance with an ROC area of 86%.
    • An automated neutrophil count-only model had a lower ROC area of 78%.

    Conclusions:

    • Combined automated and manual differential counts enhance bacterial infection prediction accuracy compared to automated counts alone.
    • Manual differential counts provide valuable supplemental information, particularly for patients with neutrophil counts ≤ 8,000/microL.
    • Cost-effectiveness necessitates careful patient selection for manual differential analysis, with further research needed to define optimal patient populations.