Citations

Abstract Background Hemoglobin S (HbS) causes sickling disorders. In the laboratory, confirmation of HbS in patient samples with varying percentages is essential. Assessment is routinely performed using capillary electrophoresis (CE) and/or high-performance liquid chromatography. Sickle solubility (SS) exploits decreased solubility of HbS under reducing conditions and is a confirmatory test. SS is unreliable with low HbS, elevated HbF, severe anemia, erythrocytosis, hyperglobulinemia, extreme leukocytosis, hyperlipidemia, or increased serum proteins. Infant samples commonly have low percentages of HbS, high HbF, and limited sample volume. Intact globin chain Mass Spectrometry (MS) is used to identify abnormal hemoglobin variants by detecting the mass change (normal α/β/γ mass =15127/15867/15996 amu), respectively). MS has potential advantages in sensitivity and efficiency over SS and requires lower input sample volume. Herein we compare the utility of MS versus SS for confirmation of HbS. Methods Twenty-three EDTA-anticoagulated blood samples containing low percentages of HbS were identified. SS was performed on 30μL washed packed cells (requires 200μL whole blood) per standard methods. Intact Quadrupole Time-of-Flight MS (Accurate-Mass Q-TOF LC/MS 6520, Agilent Technologies, California) was performed using 10μL whole blood sample in acetonitrile solution reagent and results were compared. HbS and HbF levels were assayed by capillary electrophoresis (Sebia, France) per standard methods. Results Select results are summarized in Table 1. SS detected HbS percentages to 4.8% when HbF was low but not 6% HbS when Hb F is high. MS showed no HbF interference and identified HbS peaks (at 15837 amu) down to 3.1%. HbS percentages <3.1 were undetected by both methods. Conclusions Overall, MS was superior to SS for confirming HbS, particularly in samples with low variant percentage and/or elevated HbF. MS is also optimal when sample volume is minimal, such as for infants. Percentages of HbS <3% require methods such as IEF or DNA sequencing for confirmation.

Background: Knowing the predictive factors of the variation in a center-level continuous outcome of interest is valuable in the design and analysis of parallel-arm cluster randomized trials. The symbolic two-step method for sample size planning that we present incorporates this knowledge while simultaneously accounting for patient-level characteristics. Our approach is illustrated through application to cluster randomized trials in cancer care delivery research. The required number of centers (clusters) depends on the between- and within-center variance; the within-center variance is a function of estimates obtained by regressing the log within-center variance on predictive factors. Obtaining accurate estimates of the components needed to characterize the within-center variation is challenging. Methods: Using our previously derived sample size formula, our objective in the current research is to directly account for the imprecision in these estimates, using a Bayesian approach, to safeguard against designing an underpowered study when using the symbolic two-step method. Using estimates of the required components, including the number of centers that contribute to those estimates, we make formal allowance for the imprecision in these estimates on which a sample size will be based. Results: The mean of the distribution for power is consistently smaller than the single point estimate that the sample size formula yields. The reduction in power is more pronounced in the presence of increased uncertainty about the estimates with the reduction becoming more attenuated with increased numbers of centers that contribute to the estimates. Conclusions: Accounting for imprecision in the estimates of the components required for sample size estimation using the symbolic two-step method in the design of a cluster randomized trial yields conservative estimates of power.