Donato, Leslie J.

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 Dipeptidyl peptidase 3 (DPP3) is a peptidase released from dying cells. It cleaves proteins in the renin–angiotensin pathway, which can result in hemodynamic instability. At elevated concentrations DPP3 is associated with worse outcomes, particularly in patients with shock. Herein we describe the assay performance of a DPP3 assay (4TEEN4 Pharmaceuticals GmbH) in human plasma. Methods DPP3 concentration was measured using the DPP3 immunoluminometric assay (4TEEN4 Pharmaceuticals GmbH) and the signal was read using a luminometer (Berthold Centro LB963). Analytical performance was established for precision, linearity, accuracy, detection limit, analytical specificity, reference interval, kit lot-to-lot comparison, specimen type, and sample stability. Results Limit of detection was verified at 1.6 ng/mL in EDTA plasma with a coefficient of variation (CV) of <10%. Precision studies revealed a CV ≤ 6% at 28.5 ng/mL and 59.4 ng/mL and comparability with a manufacturer performed assay was demonstrated between 7.7 and 195.2 ng/mL. An upper 97.5% limit of 22 ng/mL without age or sex associations was verified in healthy donors. The assay was not susceptible to interference from lipemia or bilirubin. However, measured DPP3 concentrations increased linearly with increasing hemolysis. DPP3 concentrations are stable in EDTA plasma for up to 24 h and at least 11 months when stored ambient or at −80°C, respectively. Conclusions DPP3 can be measured precisely in EDTA plasma using the immunoluminometric DPP3 assay. Given the potential clinical use of DPP3 in critical care patients, caution should be taken to avoid inducing pre-analytical hemolysis during sample collection.

Abstract Background Insulin resistance leads to changes in lipid and lipoprotein metabolism prior to developing prediabetes, including an increase in the concentration of large very-low-density lipoprotein particles (LVLDL-p), the mean size of VLDL (VLDL-s), and the concentration of small low-density lipoprotein particles (sLDL-p). Conversely, there is a decrease in the concentration of large high-density lipoprotein particles (LHDL-p), mean size of HDL (HDL-s), and mean size of LDL (LDL-s). Clinically validated studies show that proton nuclear magnetic resonance (NMR) can measure these six parameters and derive a lipoprotein insulin resistance (LP-IR) score, ranging from 0 to 100, with higher scores indicating greater risk for insulin resistance. The reference method for calculating LP-IR relies on a proprietary NMR spectrometer, spectral deconvolution method, and algorithm. This study seeks to determine if lipoprotein subfraction parameters measured from a commercially available NMR system can produce LP-IR scores comparable to the reference method. Methods Serum lipoprotein NMR spectra were collected using a Bruker Ascend 600 MHz magnet, Bruker Avance NEO 500 spectrometer, and the Numares AXINON lipoFIT System to report the six LP-IR score parameters between January 2023 and January 2025 (Cohort A, n = 2,474). During the same period, LP-IR score and parameters were collected using the Vantera Clinical Analyzer (400 MHz) with the LipoProfile LP4 method (Cohort B, n = 2,959). Linear regression of decile-matched data for each parameter yielded adjustment equations to align lipoFIT values with the LipoProfile method. LP-IR scores were then calculated for both adjusted and unadjusted values using a previously published LP-IR scoring algorithm. Finally, LP-IR scores were compared for sera with measures from both instruments was (Cohort C, n = 108). Results The median concentrations of sLDL-p, LDL-s, VLDL-s, HDL-S matched between Cohort A and B (<1% difference). Significant differences were noted for LVLDL-p and LHDL-p, with percent differences of 21.5% and 23.5%, respectively. Despite closely matching medians, the distributions varied greatly, with a mean interquartile range (IQR) percent difference across measures of 31.2% (Table 1). After distribution matching, all parameter median and mean IQR percent differences fell to <5% and <10%, respectively, between Cohort A and Cohort B. In Cohort C the LP-IR score matched well (y=1.1x-15, r=0.92; 95%CI 0.88, 0.94). Following distribution matching adjustment of lipoFIT values for Cohort C, new calculated LP-IR scores matched with reduced bias (y=0.94x-1.2) while correlation was slightly reduced (r=0.88 [0.83, 0.92]). By comparison, adjustments made via scaling lipoFIT values proportionally by the ratio of the medians between Cohort A and Cohort B yielded calculated LP-IR scores in Cohort C that maintained a similar bias prior to adjustments (y=1.2x-21), highlighting the ability of distribution matching to reduce bias. Conclusion Adjustments to measured lipoprotein parameters from a commercially available NMR spectrometer can be achieved via distribution matching. These adjustments greatly reduce the bias between a commercially available method and algorithm compared to the reference method, allowing for wider adoption of LP-IR score reporting.

Abstract Background Direct measurement of low-density lipoprotein cholesterol (LDL-C) is widely used and recommended by professional society guidelines despite its potential limitations in patients with hypertriglyceridemia and low LDL-C. This study evaluated the performance of 3 direct LDL-C (LDL-CD) assays, 2 modern LDL-C calculation methods [LDL-C Martin (LDL-CM), LDL-C modified Sampson (LDL-CS)] and the conventional Friedewald (LDL-CF) method against the reference method, beta-quantification (LDL-CBQ). Methods A total of 181 remnant sera from patients with standard lipid panel orders or from patients with LDL-CBQ orders with triglycerides (TG) ≥ 400 mg/dL (4.5 mmol/L), or with TG ≥ 150 mg/dL (1.69 mmol/L) and LDL-C < 70 mg/dL (1.8 mmol/L) were included. LDL-CD and lipid panel data were gathered from Abbott Alinity, Roche Cobas, and Siemens Atellica platforms. Results LDL-CD among the 3 platforms showed a median CV of 11.2%. In patients with TG <400 mg/dL, LDL-CM and LDL-CS demonstrated less bias and less misclassification at the clinical decision LDL-C levels than LDL-CF or LDL-CD. In the 400 to 800 mg/dL (9.0 mmol/L) TG group, LDL-CS was superior to LDL-CD or LDL-CM in accuracy. When TG is ≥ 800 mg/dL, LDL-CD (Roche) showed substantial bias from LDL-CBQ while LDL-CS (Roche) showed smaller but significant bias. Conclusions In summary, LDL-CD or LDL-CF showed little advantage over the 2 modern LDL-C calculation methods. LDL-CS showed the best overall correlation with LDL-CBQ and therefore is recommended to replace LDL-CF and potentially LDL-CD when making clinical decisions in patients with low LDL-C and hypertriglyceridemia.

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.

Abstract Background β-Quantification (BQ) is the reference method for low-density lipoprotein cholesterol (LDL-C) determination. It is not widely available, making it challenging for laboratories to assess the accuracy of LDL-C methods. Our goal was to develop an indirect graphical approach for comparing LDL-C test results to the BQ reference method. Methods BQ results from Mayo Medical Laboratories (n = 39 969) and the National Institutes of Health (n = 17 825) were used to investigate the interrelationships between lipid panel tests. A plot of LDL-C/non high-density lipoprotein cholesterol (nonHDL-C) vs (triglyceride (TG)/nonHDL-C)0.5 (lipid ratio plot) resulted in a negative linear regression line (y = −34.2x + 115). Based on simulation analysis, the minimum sample size for calculating its slope and intercept with a coefficient of variation of about 7.5% was 80. Results The regression lines for the lipid ratio plot of LDL-C calculated by the Sampson–NIH (y = −37.6x + 118) and enhanced Sampson–NIH (y = −33.1x + 116) equations closely overlapped with the BQ method. In contrast, the Martin–Hopkins equation showed a positive bias with an increasing TG/nonHDL-C ratio and exceeded the recommended bias limit of 4% on hypertriglyceridemic samples (y = −25.5x + 107). The Friedewald equation showed an even larger negative bias with hypertriglyceridemia (y = −47.5x + 126). Lipid ratio plots of the Roche direct assay revealed a fixed positive bias of approximately 4% (y = −33.9x + 120), whereas a much larger proportional positive bias was observed with increasing triglycerides for the Beckman direct assay (y = −14.6x + 97.8). Conclusions The lipid ratio plot is a simple graphical approach that can be readily performed by clinical laboratories for investigating the accuracy of LDL-C assays by calculation methods or direct assays.

Abstract Background Cardiovascular guidelines have long recommended low-density lipoprotein-cholesterol (LDL-C) as the primary target for lipid-lowering therapy. Recent guidelines have emphasized the importance of achieving low LDL-C levels; hence, the accurate measurement of low LDL-C is increasingly clinically relevant. Methods Using lipid panel test results from the Mayo Clinic (n = 24 590) and the FOURIER clinical trial of evolocumab (n = 9605), the following modified Sampson equation was developed by least-squares regression to match LDL-C (mg/dL) by the β-quantification reference method, by combining terms into non High Density Lipoprotein Cholesterol (nonHDLC = Total Cholesterol – High Density Lipoprotein Cholesterol) and forcing the coefficient to be one: Results The modified Sampson equation demonstrated significant improvement in its concordance to the reference method compared to other equations (the Lin Concordance Correlation Coefficient 0.992, P < 0.001). By overall kappa analysis, it showed the best agreement to the reference method at the 55 mg/dL cutpoint (1.4 mmol/L, 0.98 [P < 0.001], Sampson–NIH: 0.96, Martin–Hopkins: 0.96, Friedewald: 0.94) and the 70 mg/dL cutpoint (1.8 mmol/L, 0.97 [P < 0.001], Sampson–NIH: 0.94, Martin–Hopkins: 0.95, Friedewald: 0.92). The false classification rate of the modified Sampson equation was also significantly lower compared to the other equations at 55 mg/dL (15%, [P < 0.001], Sampson–NIH: 29%, Martin–Hopkins: 28%, Friedewald: 37%) and 70 mg/dL (18%, [P < 0.001]; Sampson–NIH: 30%, Martin–Hopkins: 28.%, Friedewald: 34%). The new equation increases the percentage of correctly classified patients with low LDL-C by approximately 10% to 20% over the other equations based on its net reclassification index. Conclusions The modified Sampson equation shows improved accuracy compared to other equations for low LDL-C. It more accurately identifies high-risk patients, who are not at their LDL-C goals and could benefit from more intensive lipid-lowering therapy.