Ness, Karl M.

Abstract Peanut allergy is a leading cause of food-induced allergy (prevalence of ∼1%). The diagnosis relies on evaluating clinical symptoms consistent with peanut exposure, supported by the detection of total peanut specific IgE (sIgE) and peanut component IgE antibodies (Ara h1, Ara h2, Ara h3, Ara h6, Ara h8, Ara h9 and Bet v2) through serum or skin testing. However, sensitization patterns across different ages and regions in the United States remain largely unknown. In this study, we investigated regional and demographic patterns of peanut component sensitization in a large U.S. population tested for suspected peanut allergy. Retrospective data were collected from 13,702 individuals tested at Mayo Clinic Laboratories. sIgEs were measured using the ImmunoCAP system (Thermo Fisher Scientific). Samples with detectable total peanut sIgE (≥0.1 kU/L) were reflexed to testing for seven peanut component IgEs. Statistical analyses including linear regression and correlation matrix analysis were conducted using R Studio. Regional differences were assessed using care provider zip code information. Among the study population, 92.0% (12,604 of 13,702) had detectable total peanut sIgE, with the Western U.S. region exhibiting the highest detectable rate (95.6%), and the Southeast the lowest (85.6%) (p < 0.001). On average, 3.38 different component IgEs were detectable per person with a detectable total peanut sIgE; 7.1% (892) had no detectable component IgEs, possibly due to antibodies against other peanut components. Correlation analysis revealed strong associations between Ara h2 and Ara h6 (r = 0.94), with moderate correlations observed between Ara h2, Ara h3, and Ara h6 to Ara h1 (r ranging from 0.72 to 0.77). Age-stratified analysis indicated higher total peanut sIgE detectable rates in children and adolescents (95.2%, ≤18 years) compared to adults (76.5%, >18 years). Interestingly, adults were more likely than children to have negative results for all component IgEs. Sensitization rates to different peanut components varied, with Ara h2 (77.5%) and Ara h6 (73.2%) being the most frequently detected, followed by Ara h1 (56%), and Ara h3 (47.3%). Lower detectable rates were observed for Ara h8, Ara h9, and Bet v2 (34.8%, 26.1% and 22.7%, respectively). Regional analysis showed consistently high detectable rates for Ara h2 and Ara h6 (>70%) across most areas (p < 0.001) with slightly lower rates in the Southwest (<63%). Bet v2, despite its overall low prevalence (<24%), had a relatively higher detectable rate in the Southwest (29%). This study provides valuable insights into potential demographic and geographic factors influencing peanut allergy sensitization. Our study demonstrates that peanut sensitization patterns vary by age and geographic location. Children and adolescents exhibit higher detectable rates for total peanut sIgE and component IgEs compared to adults, with Ara h2 and Ara h6 as the most frequently detected and highly correlated components.

Abstract Background Multianalyte assays with algorithmic analysis (MAAAs), such as the Prostate Health Index (phi), are increasingly utilized for generating disease risk scores. Currently, imprecision and bias in phi are not directly monitored by quality control (QC) assessment of the index but rather by QC assessment of individual components. This may not be adequately controlling for imprecision and bias in the calculated multicomponent phi value itself. Methods Inter- and intra-assay phi precision was compared to precision of the individual component assays. QC measurements from total prostate-specific antigen (PSA), free PSA, and p2PSA were used to calculate a single calculated phi QC metric (PHIc). The frequency of QC failure of PHIc, relative to individual components QC by Westgard rules (13S and 22S), was determined. The effects of varying analyte component assay bias on the resulting PHIc metric were also examined. Results Average measured phi imprecision (6.7% CV) was higher than individual phi analyte component imprecision (3.9–4.5% CV) across 2 Beckman Coulter Unicel DxI 800 instruments. A retrospective examination of PHIc QC over 84 quality control determinations was concurrently carried out for both PHIc and component assay failure patterns, which were dependent on SDs utilized for Westgard evaluation. Finally, reinforcing nonlinear changes in PHIc were observed in select cases of introduced simulated bias of individual component measurements. Conclusions An additional calculated phi QC measure can be introduced to monitor MAAA precision/bias, and in principle calculated index controls may represent a complementary supplemental QC method that could be applied to other MAAA indices.

Abstract Introduction The CLSI EP15-A3 guideline “User Verification of Precision and Estimation of Bias” provides a simple experimental approach to estimate a method’s imprecision and bias. The objective is to determine if the laboratory precision performance of repeatability (SR) and within-laboratory imprecision (SWL) are in accordance to the manufacturer specification claims (MSCs). Objectives Evaluate the utility of the EP15-A3 protocol to verify method precision during a troubleshooting investigation and after major instrument maintenance, using a carcinoembryonic antigen (CEA) immunoassay as an example. Methods CEA was performed on the Beckman Coulter DxI (Beckman Coulter, Brea, CA). Quality control (QC) levels (L1: 2.89; L2: 21.10; L3: 39.10 ng/mL) (Bio-Rad Laboratories, Irvine, CA) were used. Each QC level was measured before and after instrument maintenance as follows: five replicates per run, one run per day, and during 5 days. Imprecision estimates (IEs) for SR (%CVR) and SWL (%CVWL) were calculated by one-way analysis of variance using Microsoft Excel Analyse-it software. Estimated imprecision was compared to MSC and desirable imprecision specifications based on biological variation (BV). Results A change in the analytical performance of CEA was detected by a decreased sigma-metric indicator. After a bias problem was ruled out, the observed %CVR for L1, L2, and L3 were 7.2%, 3.7%, and 4.8%, respectively. The %CVWL were 8.3%, 5.0%, and 5.5%, which exceeded the MSC of %CVWL~4.0% to 4.5%. After a laboratory investigation, major instrument maintenance was performed by the manufacturer. The %CVR and %CVWL estimates for L1, L2, and L3 after maintenance were 3.2%, 3.8%, 3.5% and 3.9%, 4.2%, 4.0%, respectively. After maintenance, the CEA performance was consistent with the MSC for each of the levels analyzed and within the BV impression goal of %CV ≤6.4. Conclusion CLSI EP15-A3 guideline is an alternative troubleshooting tool that can be used to investigate and verify method precision performance before and after significant instrument maintenance.