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

We developed logistic regression models that combine information from the automated CBC and manual 100-cell differential counts to predict bacterial infection. The logistic models were fitted from a case group of 116 patients with proven bacterial infection and a control group of 930 presumably uninfected outpatients. A 4-variable, 15-parameter model, which includes automated absolute neutrophil, manual band, and manual immature granulocyte counts, performed best with a receiver operating characteristic (ROC) curve area of 89%. A more practical 2-variable model including automated absolute neutrophil and manual band counts performed almost as well with an ROC curve area of 86%. The automated neutrophil count-only model is less informative with an ROC curve area of 78%. The combined information from automated and manual differential cell counts more accurately predicts bacterial infection than automated counting alone. Despite these modest improvements, the high cost of manual differential cell counts dictates careful patient selection. The supplemental information gained from manual differential counts is most useful for patients with low to normal neutrophil counts (8,000/microL [8.0 x 10(9)/L] or less). Further studies are indicated to determine the characteristic patient populations deriving maximal benefit from this information.