Visual scoring versus histogram subtraction of in vivo binding of immunoglobulins against platelets after transfusion

The use of simultaneous confidence bands for comparison of single parameter fluorescent intensity data

Despite the utility of multiparameter flow cytometry for a wide variety of biological applications, comparing single parameter histograms of fluorescence intensity remains a mainstay of flow cytometric analysis. Even comparisons requiring multiparameter gating strategies often end with single parameter histograms as the final readout. When histograms overlap, analysis relies on comparison of mean or median fluorescence intensities, or determination of percent positive based on an arbitrary cutoff. Earlier attempts to address this problem utilized either simple channel-by-channel subtraction without statistical evaluation, or the Kolmogorov-Smirnov (KS) or Chi-square test statistics, both of which proved to be overly sensitive to small and biologically insignificant differences. Here we present a method for the comparison of two single-parameter histograms based on difference curves and their simultaneous confidence bands generated by bootstrapping raw channel data. Bootstrapping is a nonparametric statistical approach that can be used to generate confidence intervals without distributional assumptions about the data. We have constructed simultaneous confidence bands and show them to be superior to KS and Cox methods. The method constructs 95% confidence bands about the difference curves, provides a P value for the comparison and calculates the area under the difference curve (AUC) as an estimate of percent positive and the area under the confidence band (AUCSCB95), providing a lower estimate of the percent positive. To demonstrate the utility of this new approach we have examined single-color fluorescence intensity data taken from a cell surface proteomic survey of a lung cancer cell line (A549) and a published fluorescence intensity data from a rhodamine efflux assay of P-glycoprotein activity, comparing rhodamine 123 loading and efflux in CD4 and CD8 T-cell populations. SAS source code is provided as supplementary material.

The value of crossmatch tests and panel tests as a screening tool to predict the outcome of platelet transfusion in a non-selected haematological population of patients

BACKGROUND AND OBJECTIVES

Alloantibodies against platelets can be detected by using different laboratory tests. Most of these tests, which use panel cells or antigens as a target, perform poorly in non-selected haematological patients. In relation to these tests, a crossmatch test of transfused platelets and patient's serum may be viewed as the standard and may be superior in predicting donor platelet destruction by alloimmunization.

MATERIALS AND METHODS

In 95 randomly selected thrombocytopenic patients with haematological malignancies, who were receiving leucodepleted blood products, 184 serum samples were studied in an in vitro crossmatch test by using the technique of the platelet immunofluorescence test (crossmatch-PIFT), in an in vivo crossmatch test detecting in vivo binding of immunoglobulins to transfused platelets according to the PIFT technique (in vivo-PIFT), in the indirect PIFT using five random donors as a target (panel-PIFT) and in an enzyme linked immunosorbent assay using immobilized human leucocyte antigens (HLAs) of 100 standardized donors (ELIHLA). The results of all these methods were related to the recovery at 1 and 16 h post-transfusion.

RESULTS

The results of the crossmatch-PIFT were not associated with platelet recovery at 1 and 16 h after transfusion. Even in a subgroup of patients, in whom predefined clinical factors were excluded, no association with platelet recovery was found. The results of the crossmatch-PIFT correlated with those of the in vivo-PIFT (P = 0.02); however, 35 (19%) discrepant results were identified between these tests. The results of the crossmatch-PIFT were not related to the panel-PIFT (P = 0.25), but did relate to those of the ELIHLA (P = 0.02), still revealing 36 (20%) discrepant results. None of the in vivo-PIFT, the panel-PIFT or the ELIHLA was associated with platelet recovery after 1 h, whilst only a positive panel-PIFT was associated with poor platelet recovery at 16 h after transfusion (P = 0.03).

CONCLUSIONS

In a population at low risk for alloimmunization, the correlation of test outcome and platelet recovery is poor. None of these crossmatch tests or screening tests was identified as superior to any other in this population.