Larkey, Nicholas E.

Abstract Background Point-of-care testing (POCT) has expanded rapidly across healthcare systems, increasing both program scale and operational complexity. However, quantitative benchmarking data describing how POCT programs are structured and supported across institutions remain limited. Methods We conducted a cross-sectional survey to characterize POCT program support, including program scope, staffing, operational practices, governance, and challenges. The survey was distributed electronically through professional networks, and 93 institutional responses were analyzed. Descriptive analyses were performed, and derived metrics, including sites per point-of-care testing coordinator (POCC), were used to evaluate the relationship between program scale and staffing. Results POCT programs demonstrated substantial variability in scale, with wide ranges in the number of testing sites and operator populations. Adoption spanned multiple test categories, and many institutions reported use of multiple platforms for the same assays. Training, competency assessment, and quality management activities were primarily supported by laboratory staff and POCCs, with documentation frequently partially electronic. Median POCC staffing increased with program size; however, sites per POCC also increased significantly across program scale tiers (Kruskal–Wallis P < 0.001). Governance structures and operational responsibilities varied across institutions, and common challenges included staffing limitations, training and compliance burden, multisite coordination, and IT/connectivity constraints. Conclusions POCT programs are expanding in scale and complexity, but workforce capacity and governance structures have not scaled proportionally. This mismatch creates a cumulative and multiplicative operational burden. These findings provide benchmarking data to inform scalable staffing models, governance strategies, and infrastructure development for sustainable POCT program management.

Abstract Background Low-density lipoprotein cholesterol (LDL-C) is directly associated with coronary artery disease (CAD) risk. Subfractionation of LDL enables differentiation between large-buoyant LDL (>20.5 nm) and small-dense LDL (≤20.5 nm). Small-dense LDL reportedly increases CAD risk, as do LDL-C and LDL particle (LDL-P) concentrations. Nuclear magnetic resonance spectroscopy (NMR) reports LDL-C, LDL-P, and LDL size (LDL-s). We investigated associations between these outputs and their agreement on CAD risk information. Methods Associations between LDL-P, LDL-C, and LDL-s measured by NMR were evaluated in serum from clinically ordered samples (n = 26 710), and a subset of patients with CAD evaluation from coronary angiography (n = 356). Correlations were determined using Spearman ρ, and clinical classifications were compared using the following thresholds for increased risk: LDL-C > 160 mg/dL, LDL-P > 1600 nmol/L, and LDL-S < 20.5 nm. Results The large laboratory NMR data set showed LDL-C was highly correlated with LDL-P (ρ = 0.87), and moderately with LDL-s (ρ = 0.51). Correlation between LDL-P and LDL-s was weakest (ρ = 0.21). When comparing pairwise high- and low-risk laboratory values, concordant values were observed in 99.8%, 43%, and 25% of cases for LDL-P/LDL-C, LDL-s/LDL-P, and LDL-C/LDL-s, respectively. In patients with angiography and NMR results, CAD was diagnosed in 40% (6/15) of patients with concordant high-risk LDL-C and small-dense LDL-s in the smaller patient cohort, but only 19% (6/31) of CAD-positive patients with high-risk LDL-C had small-dense LDL-s. Conclusions LDL-s and LDL-C are frequently discordant at established LDL-C risk cutoffs. CAD diagnosis was found in similar numbers regardless of LDL-s phenotype.

COVID-19 convalescent plasma (CCP) was one of the first therapeutic options available for the treatment of SARS-CoV-2 infections and continues to be used selectively for immunosuppressed patients. Given the emergence of novel SARS-CoV-2 variants which are resistant to treatment with available monoclonal antibody (MAb) therapy, CCP remains an important therapeutic consideration.

Abstract Background Antineutrophil cytoplasmic antibody (ANCA) testing by the indirect immunofluorescence assay (IFA) is important for the diagnosis of autoimmune vasculitis. A common analytical interference for ANCA-IFA is the presence of an antinuclear antibody (ANA), which can cause an apparent perinuclear ANCA (pANCA) result on ethanol-fixed neutrophils. Here, the association of ANA patterns, titers, and concentrations with pANCA interference is investigated. Methods Samples positive for ANA by IFA with homogeneous, speckled, dense fine speckled (DFS), and centromere patterns were tested for ANA by enzyme immunoassay (EIA)] and for ANCA by IFA on ethanol-fixed neutrophils. Titers and concentrations were determined for the ANA-IFA and EIA, respectively, and correlated with the frequency of pANCA interpretations. Results For ANA-EIA positive samples (≥1.1U), 20.0% led to a pANCA interpretation compared to 5.1% for negative samples (≤1.0U). For samples positive by ANA-IFA, 12.9% resulted in a pANCA interpretation. Interference on pANCA correlated with ANA-IFA titer, with ANA titers ≥1:1280 identified as pANCA positive in 20.9% of samples compared to 9.7% for titers <1:1280. There was also a correlation with ANA pattern, as homogeneous samples were most likely to be called positive for pANCA (31.7%), followed by speckled (8.8%), DFS (6.8%), and centromere (3.6%). Conclusions Positivity for ANA by EIA is associated with increased prevalence of pANCA interpretation. Samples positive for ANA by IFA also demonstrated this association, particularly with higher-titer, homogeneous patterns. Laboratories can use this information to determine an optimal workflow for when investigating potential pANCA interferences.

Abstract Reticulocyte hemoglobin content (Ret-He, the hemoglobin within reticulocytes or immature red blood cells) and immature reticulocyte fraction (IRF, the immature fraction of the absolute-reticulocyte-count) are tests that provide insight into erythropoiesis and iron status earlier than conventional iron studies offering the added benefit of not being acute-phase-reactants. Studies have shown that Ret-He is a diagnostic marker for iron-deficiency-anemia (IDA), but fewer studies have investigated IRF. Our laboratory is currently planning to report these parameters when reticulocyte is ordered. Since these are new parameters, we wanted to investigate their overall correlation with complete blood count (CBC) and other iron studies to gain a better appreciation of their utility in our patient population. The aim of this study was to compare the overall correlation of Ret-He and IRF with seven tests used in the evaluation of IDA. To our knowledge these parameters have not all been directly correlated within a single study. CBC and reticulocytes were quantified using XN 9000 hematology analyzers (Sysmex Corporation), ferritin (DXI 800, Beckman Coulter Inc.), and % iron-saturation (measured using total iron-binding-capacity (TIBC)=transferrin*1.18 on Cobas 6000, Roche Diagnostics). Two de-identified cohorts of patients undergoing physician-ordered reticulocyte testing were used for this analysis. Dataset 1 (DS1): (N=2026 from Mayo Clinic Florida) had Ret-He and IRF compared to absolute-reticulocyte-count (Ret), ferritin and % iron saturation. Dataset 2 (DS2): (N=3990 from Mayo Clinic Rochester) had Ret-He and IRF compared to the red-cell-indices of the CBC including hemoglobin (Hgb), mean-corpuscular-volume (MCV), mean-corpuscular-hemoglobin (MCH), and mean-corpuscular-hemoglobin-concentration (MCHC). Correlation coefficients were calculated using Spearman rank-order (ρ) wherein values below +/-0.39 are weak, between +/-0.40-0.59 are considered moderate, and values above +/-0.60 are considered strong. For DS1, Ret-He demonstrated the following correlations: Ret (ρ=0.01), ferritin (ρ=0.33), % iron saturation (ρ=0.63). IRF demonstrated: Ret (ρ=0.46), ferritin (ρ=-0.05), % iron saturation (ρ=-0.22). For DS2, Ret-He demonstrated the following correlations: Hgb (ρ=0.17), MCV (ρ=0.64), MCH (ρ=0.74), MCHC (ρ=0.56). IRF demonstrated Hgb (ρ=-0.41), MCV (ρ=0.10), MCH (ρ=0.04), MCHC (ρ=-0.11). Ret-He and IRF demonstrated different correlative profiles suggesting they may have differing uses. Ret-He was strongly positively-correlated with % iron saturation, MCV, MCH and moderately positively-correlated with MCHC. These positive-correlations are consistent with relationships established in the literature. Interestingly, Ret-He was only weakly correlated with ferritin, possibly owing to ferritin being an acute-phase-reactant. IRF had a moderate positive correlation with Ret and moderate inverse correlation with Hgb. Both of these IRF relationships are consistent with other reports, but both relationships have not been shown in the same study before, preventing direct comparison until now. The literature suggests IRF may have more potential in monitoring treatment than in diagnosis. One limitation of these datasets is their lack of clinical correlation such as established iron-deficiency, anemia status, or treatment information.

Abstract Plasma concentrations of low-density-lipoprotein cholesterol (LDL-C) are directly associated with risk for coronary artery disease (CAD). Multisociety guidelines define LDL-C>160mg/dL as a risk factor for CAD and LDL-C>190mg/dL as an indication for lipid lowering medication, regardless of other clinical factors. Subfractionation of LDL according to size (LDL-s) enables differentiation between two LDL phenotypes: large-buoyant LDL and small-dense LDL. The small-dense LDL phenotype reportedly conveys increased risk for CAD. Major societies do not recommend LDL subfractions be used for clinical decision making and most payers do not cover LDL subfraction testing. Despite these restrictions, LDL subfraction is routinely requested by clinicians. Nuclear magnetic resonance (NMR) spectroscopy measures LDL-C and LDL-s. Following inquiries regarding interpretation of conflicting LDL-C and LDL-s results, we investigated associations between LDL-C and LDL-s measured by NMR in order to determine how often they provide contradicting or additive information. Verification of NMR LDL-C accuracy was confirmed by ß-quantification in a subset of patient samples (n=250). The average bias was -4.5mg/dL and the correlation coefficient was 0.92. High-risk was defined as LDL-C>160mg/dL or LDL-s<20.5 nm (small-dense LDL); and low-risk was defined as LDL-C<70mg/dL or LDL-s>20.5nm (large-buoyant LDL). In 26,710 clinical NMR analyses, the median LDL-C was 94.0mg/dL (range:5-436mg/dL) with median LDL-s of 20.8 nm (range:19.4–23.0nm). LDL-s moderately correlated with LDL-C (Ï#129;=0.51;p<0.01). Small-dense-LDL was identified in only 18% (407/2,191) of patients with elevated LDL-C (>160mg/dL) and was more common (73.2% of 6,093) in patients with low LDL-C (<70mg/dL;p<0.001). Associations with CAD were investigated among patients without cholesterol-lowering medication treatment referred for angiography (n=356). CAD (defined as stenosis >50% in one or more coronary artery) was diagnosed in 14% (1/7) of subjects with low LDL-C (<70mg/dL) compared to 59% (47/80) of subjects with elevated LDL-C (p=0.01). When stratifying by LDL-s, CAD was diagnosed in 50% (57/115) of subjects with small-dense LDL compared to 43% (104/241) of subjects with large-buoyant LDL (p=0.2). Small-dense LDL was identified in only 33% (26/80) of cases with elevated LDL-C. Limiting to subjects with elevated LDL-C, CAD was diagnosed in 50% (13/26) of subjects with concordant (high-risk) small-dense LDL compared to 61% (33/54) of subjects with discordant (low-risk) large-buoyant LDL (LDL-s>20.5nm) (p=0.3). Our data confirm that LDL-s subfraction measured by NMR is reported discordantly in most cases when LDL-C is unequivocally high or low. Furthermore, CAD diagnosis was significantly associated with LDL-C, but not with LDL-s. Our data also show that in discrepant samples, elevated LDL-C correlates better with disease state compared to LDL-s. Therefore, LDL-s should not be used to justify treatment decisions in patients with elevated LDL-C. Laboratories should consider carefully whether or not to report LDL-s when it is known that misleading and discordant values will be reported in a majority of cases.