Donato, Leslie J.

Abstract Background The accurate measurement of blood lipids and lipoproteins is crucial for the clinical management of atherosclerotic disease risk. Despite progress in standardization, there are still significant variations in pre-analytical requirements, methods, nomenclature, and reporting work flows. Content The guidance document aims to improve standardization of clinical lipid testing work flows. It provides recommendations for the components of the lipid panel, fasting requirements, reporting of results, and specific recommendations for non-high-density lipoprotein cholesterol (non-HDL-C), low-density lipoprotein cholesterol (LDL-C), lipoprotein(a) [Lp(a)], apolipoprotein B (apo B), point-of-care lipid testing, and LDL subfraction testing. Summary Lipid panels should always report non-HDL-C and LDL-C calculations if possible. Fasting is not routinely required except in specific cases. Modern equations should be utilized for LDL-C calculation. These equations allow for LDL-C reporting at elevated concentrations of triglycerides and obviate the need for direct measured LDL-C in most cases.

Abstract Objectives The aims of this study were to (1) establish the maximum allowable interference limits for hemolysis, lipemia, and icterus for chemistry analytes tested in body fluid samples and (2) assess the effectiveness of serial dilution to mitigate spectral interferences. Methods Residual body fluids from clinically ordered testing were mixed (<10% by volume) with stock solutions of interferent (spiked) and compared with a control spiked with an equal volume of 0.9% saline. The analytes were measured on the Roche cobas c501 instrument. Difference and percentage difference were calculated and compared with allowable total error limits. A subset of samples were serially diluted with 0.9% saline. Mean (SD) difference and percentage difference were calculated. Results The interference thresholds were lower than the package insert for lactate dehydrogenase, cholesterol, triglycerides, and total protein for hemolysis; amylase, cholesterol, and total protein for icterus; and albumin for lipemia. Only cholesterol and triglyceride results returned to baseline upon dilution of icteric samples. Conclusions Interference thresholds in body fluids were lower than blood for 6 analytes. Diluting interferences that surpass these limits does not produce reliable results that are comparable to the baseline results before spiking in the interferent.

Abstract Background Turnaround time (TAT), defined as time of collection to time of result verification, is monitored monthly for cardiac Troponin (cTnT). Within our STAT laboratory, our TAT goal is 85% of cTnT results verified within 45 min. In October 2021, our lab began observing an increase in TAT. A quality improvement project was launched to identify a root cause and possible solutions. Methods cTnT TAT data from the Hospital Clinical Laboratory (STAT lab) at Mayo Clinic, Rochester, MN was extracted from our Laboratory Information System (LIS) using Tableau (Salesforce Inc., Seattle, WA). The data were categorized into three components: collection time, in lab processing and testing time, and time to result verification. Goal metrics for each component are: collection <10 min, in lab processing and testing <25 min, and result verification to LIS <5 min. The average time for each component and percentage not meeting TAT goals were calculated in Microsoft Excel. TAT while performing manual verification of results (January 2022) and after autoverification was implemented (December 2022) were compared. Results Prior to implementation of cTnT autoverification, total orders for January 2022 were 1408 with 82.0% meeting TAT of 45 min. Of the 18.0% not meeting TAT, 207 (14.7%) orders exceeded goals for one or more components (percentages do not total 100%): collection delays 131 (63.0%), in lab processing and testing delay 175 (85.0%), and delay in result verification to LIS 80 (39.0%). For those orders not meeting the 45 min TAT goal, mean times were 17.6, 40.3, and 8 min for collection time, in lab processing and testing, and result verification to LIS, respectively. Post implementation of autoverification, total orders for December 2022 were 1444 with 88.6% meeting TAT of 45 min. Of the 11.4% not meeting TAT, 93 (6.4%) orders were outside goals for multiple components: collection delays 51 (55.0%), in lab processing and testing delay 67 (72.0%), and result verification to LIS 0 (0.0%) orders. For those orders not meeting the 45 min TAT goal, mean times 16.6, 39.7, and 0 min for collection time, in lab processing and testing, and result verification to LIS, respectively. Conclusion Achieving TAT goals agreed upon by the laboratory and clinical practice is critical to quality patient care. An investigation into our delayed TAT for cTnT highlighted several opportunities for intervention within the control of the lab. We chose to implement autoverification of results to easily and consistently eliminate the time required for manual result verification. TAT was improved by 6.6% (82.0% to 88.6%) through autoverification of results, allowing technologists to focus on other clinical work.

Abstract Background Analytical outliers (fliers) occur with most troponin methods and can adversely affect patient management. We compared the analytical outlier rates for the Roche 5th generation Troponin T STAT (cTnT) reformulated (biotin-insensitive) reagent using a Cobas e601 analyzer (Indianapolis, IN) to rates observed in a previous study using the biotin-sensitive reagent on a Cobas e411 analyzer. Methods Plasma separator tubes (PST) were collected from hospital patients with orders for clinical cTnT testing. PST samples were centrifuged for 3 min at 4000g and analyzed using the biotin-insensitive cTnT Gen 5 assay on a Roche Cobas e601 immunoassay system. If samples displayed measurable cTnT Gen 5 (≥6 ng/L), samples were stored at 2–8 °C within 2 h of initial measurement (N = 2555). Within 24 h of initial analysis, samples were warmed to room temperature, aliquoted, re-centrifuged at 4000g for 3 min, and cTNT was re-measured on the same analyzer. Outlier rates were compared to a previous study (N = 1997) using nearly identical study design but using biotin-sensitive Roche 5th Gen cTnT reagent on an e411 analyzer. We defined analytical outliers by three methods: Repeat value (after aliquoting and re-centrifugation) that differed from the initial value for all samples with measurable cTnT by a critical difference (CD): where CD = z × √2 × SDanalytical, with a z-value of 3.719 and using SD from best fit line of precision vs cTnT concentration. Precision experiments were performed using four plasma pools in 5 × 5 precision experiment on the e411 analyzer. Using the same CD but considering only initial cTnT values <100 ng/L, those are most likely to be clinically significant or lead to misinterpretation. Using CD for initial values <100 ng/L, and a fixed 10% difference between replicates for initial values ≥100 ng/L, accounting for our practice of reporting the cTnT “delta” in percent (rather than absolute) for values ≥100 ng/L. Comparisons of outlier rates between the biotin-sensitive and biotin-insensitive reagents were performed using Pearson’s chi-squared test. Results Using the CD the total number of outliers that were observed for the biotin-insensitive reagent on the e601 was 208/2555 (8.1%), compared to 68/1997 (3.4%) on the biotin-sensitive reagent measured on the e411 (P < 0.0001). Outliers were observed in 138 (6.4%) of samples with initial values < 100 ng/L using the biotin-insensitive reagent on the e601 compared to 45 (2.6%) of such samples on the e411 (P < 0.0001). Using the Z score method to define outliers for initial values <100 ng/L and a fixed 10% to define outliers for initial values ≥100 ng/L, the outlier rate was 151 (5.9%) for the biotin-insensitive reagent on the e601 compared to 53 (2.7%) on the e411 (P < 0.0001). Conclusions Analytical outliers occurred more frequently using the reformulated (biotin-insensitive) reagent on the Roche Cobas e601 analyzer, compared to outlier rates observed for biotin-sensitive reagent on the Cobas e411.

Abstract Background Low-density lipoprotein cholesterol (LDL-C) is the primary biomarker for assessing risk for atherosclerotic cardiovascular disease (ASCVD). However, multiple large studies have demonstrated greater accuracy using Apolipoprotein B (apoB). One obstacle to its widespread use is the lack of guideline endorsed apoB treatment targets. A recently published solution has proposed using population percentiles to calculate LDL-C using apoB values (PMID: 36366949). The LDL-C concentration correlated to an individual’s ApoB percentile may more appropriately reflect ASCVD risk compared to lipid panel LDL-C. Here we evaluated this approach using a cohort where LDL-C was measured with the gold-standard beta-quantification method (LDL-CBQ). Method Results from clinically ordered apoB, LDL-CBQ, and lipid panel were included (n = 18 852). Samples with triglycerides >1000 mg/dL or LpX were excluded. Total cholesterol, high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), and apoB were measured using Roche Cobas c501 analyzers. LDL-CBQ was performed using Beckman LE-80K ultracentrifuge and dextran sulfate-Mg/Cl precipitation. ApoB equivalent LDL-C (eqLDL-CapoB) was calculated according to linear regression of LDL-C percentiles against apoB percentiles described by the equation eqLDL-CapoB = 1.45(apoB)-25. Concordance was determined based on clinical thresholds for LDL-CBQ and eqLDL-CapoB. Results Median (IQR) apoB and LDL-CBQ was 98 (79−119) mg/dL and 120 (94−150) mg/dL, respectively. LDL-CBQ and eqLDL-CapoB were concordant in 60% of samples overall. Discordance between eqLDL-CapoB and LDL-CBQ shifted from primarily overestimating to underestimating as LDL-CBQ increased. Discordant eqLDL-CapoB was directly associated with TG and inversely associated with HDL-C. Conclusion The eqLDL-CapoB calculation is a useful tool to present apoB results in a more familiar context of LDL-C-equivalent units. The concordance findings largely confirm previously published results using eqLDL-CapoB. Furthermore, the pattern of HDL-C and TG suggests that a discordantly elevated eqLDL-CapoB is an indicator of a higher risk lipid profile. More investigation is needed to determine if eqLDL-CapoB is appropriate for widespread adoption.

Abstract Background Glycerol kinase deficiency (GKD) is a rare X-linked recessive disorder biochemically characterized by hyperglycerolaemia. Adults with GKD are usually asymptomatic. However, they often present with pseudohypertriglyceridemia because most clinical triglyceride methods measure enzymatically generated glycerol from triglycerides. Currently, assays for measuring plasma glycerol are lacking. We describe a preliminary validation of glycerol measured by nuclear magnetic resonance spectroscopy (NMR). Methods Serum glycerol was measured by NMR (Numares AG, Regensberg, Germany) using a Ascend 600 NMR (Bruker, Billerica, MA). Stability and matrix effects were determined by repeat measurement of 10 freshly collected samples following various storage conditions. Normal donors were recruited for reference interval (n = 143). Analytical specificity was assessed by spiking red cell lysate, bilirubin and intralipid. Precision was assessed by measuring three pools five times for five days. Accuracy was performed by spiking known concentrations of glycerol. Accuracy was assessed using an enzymatic method (Randox Laboratories, Ltd. County Antrim, UK) on a Roche Cobas c501 (Roche Diagnostics, Indianapolis, IN) as reference. Results Measured glycerol increased over time in both serum and plasma, however, prepared samples were stable on the instrument for up to 24 hours. Measured glycerol was not impacted by hemoglobin (1000 mg/dL) or bilirubin (625 mg/dL), however, spiking Intralipid® prevented any measurement. NMR and enzymatic assays strongly correlated, with a constant bias (Table 1). Repeatability and reproducibility were <10% CV and % recovery from glycerol standard were >80% for all concentrations. Conclusion Pre-analytical stability, imprecision and accuracy were acceptable for clinical measurement of glycerol using NMR. While there is a significant systematic bias with enzymatic methods, further studies in normal and disease populations will be necessary to determine clinical impact.

Abstract Background There is growing evidence that low-density lipoprotein-triglyceride (LDL-TG) may be a better biomarker to define atherosclerotic cardiovascular disease (ASCVD) risk than LDL-cholesterol (LDL-C), which is currently used to stratify risk and as a treatment goal. Because LDL-TG typically requires additional advanced lipid testing, we developed an equation for estimating LDL-TG (eLDL-TG) from the results of the standard lipid panel. Methods Results from a general dyslipidemic population (N = 40 202), which were obtained with beta-quantification (BQ) reference method, were randomly divided into a training and validation datasets and used to develop the equation for estimating LDL-TG shown in Fig. 1A. Association of Sampson-NIH-LDL-C (NIH-LDL-C) and eLDL-TG with ASCVD risk markers was done in the National Heart and Nutrition Examination Survey (NHANES) (N = 37 053) and with ASCVD events in the Atherosclerosis Risk in Communities (ARIC) study (N = 14 749). Results The comparison between eLDL-TG and measured LDL-TG was only modest (R2 = 0.601, slope = 0.611), but eLDL-TG (Fig. 1B) showed better risk stratification than NIH-LDL-C (Fig. 1C) by survival curve analysis in ARIC. Moreover, receiver-operating characteristic (ROC) analysis showed that eLDL-TG was better than all other lipid-marker tests for ASCVD events (AUC: eLDL-TG = 0.633, HDL-C = 0.627, sdLDL-C = 0.621, remnant-cholesterol = 0.613, NonHDL-C = 0.613, TG = 0.603, apoB = 0.598, NIH-LDL-C = 0.590, TC = 0.568). In NHANES, eLDL-TG was more strongly associated with hypertriglyceridemia, obesity, diabetes, metabolic syndrome and increased high-sensitivity C-reactive protein than NIH-LDL-C. Inclusion of eLDL-TG in a Cox-Proportional Hazards Model along with other conventional risk factors (age, sex, race, diabetes, systolic blood pressure, smoking, HDL-C, and total cholesterol) did not further improve risk prediction. Conclusions Like LDL-C, LDL-TG can also be estimated from the results of the standard lipid panel. It is a better univariate risk marker than LDL-C and hence can be useful for initial ASCVD risk stratification.

Abstract Background Low-density lipoprotein cholesterol (LDL-C) is used to assess atherosclerotic cardiovascular disease (ASCVD) risk and to manage lipid-lowering therapy. Proprotein convertase subtilisin/kexin (type 9) serine protease (PCSK9) inhibitors decrease LDL-C by up to 70% when used in conjunction with statins. Due to cost, insurance companies restrict their use. To be eligible for PCSK9 therapy, a patient must have pre-existing ASCVD and LDL-C > 70 mg/dL, while on a maximally tolerated dose of statins. Most clinical laboratories calculate LDL-C by the Friedewald Equation (FWLDL-C), which utilizes results from the standard lipid panel (total cholesterol (TC), triglycerides (TG), and high-density lipoprotein cholesterol (HDL-C)). However, this often leads to an underestimation of LDL-C, especially when TG concentrations are elevated. More accurate equations have recently been developed, such as the Martin (MLDL-C), Sampson-NIH (SLDL-C) and extended Martin (ext-MLDL-C) equations. Using LDL-C determined by the beta-quantification reference method (BQ), we developed the following enhanced version of the Sampson-NIH Equation (eSLDL-C), which includes apoB as an independent variable: eSLDL-C = TC/1.15-(HDL-C)/1.25-TG/6.98-(TG × NonHDL-C)/1115 + TG2/8903.23 + (TG × ApoB)/1237 + ApoB/4.54–4.73 Methods Results for clinically ordered ApoB, BQ, and lipid panels were used. To test the accuracy of the various equations, we compared BQLDL-C to FWLDL-C, MLDL-C, extMLDL-C, SLDL-C and eSLDL-C by regression analysis (N = 12 101, TG < 800). Accuracy of each equation was compared to the BQ reference method for classifying patients (LDL-C ≤ 150, n = 9374) as either being below or above the 70 mg/dL treatment decision threshold for PCSK9 therapy. Results The eSLDL-C equation performed substantially better than the other equations for estimating low LDL-C (BQLDL-C ≤ 100 mg/dL, TG 5–800 mg/dL, n = 4115), with a mean absolute difference (MAD) of 3.80 mg/dL (compared to SLDL-C: 6.05; FWLDL-C: 8.71; MLDL-C: 6.43; extMLDL-C: 6.39). It also had the best overall normalized Matthew’s Correlation Coefficient (nMCC) for the best balance of sensitivity and specificity for identifying patients that are above the 70 mg/dL treatment threshold (eSLDL-C-94.7% (92.2% spec, 98.5% sen), SLDL-C-90.7% (94.8% spec, 95.5% sen), FWLDL-C-88.2% (98.0% spec, 92.7% sen), MLDL-C-90.3% (88.9% spec, 96.5% sen), and extMLDL-C-90.1% (88.4% spec, 96.5% sen)). In our dataset with 1125 subjects being below the 70 mg/dL threshold by BQLDL-C, FWLDL-C misclassified an additional 601 subjects below this threshold (Negative Predictive Value 65%). MLDL-C and extMLDL-C misclassified 289 subjects falsely low, whereas eSLDL-C only has 126 subjects falsely classified lower than the 70 mg/dL threshold. Conclusion We show that the enhanced Sampson-NIH equation, which includes apoB, estimates low LDL-C more accurately than other equations. The use of the new equation would improve access to those patients who might benefit from PCSK9 therapy but have falsely low LDL-C by the FWLDL-C equation or other LDL-C equations. The new equation is also more specific than the MLDL-C and extMLDL-C equations and would, therefore, also reduce the overutilization of PCSK9 therapy by these particular equations for those patients who do not require it, according to the reference BQ method and current guidelines.

Abstract Background Elevated Lp(a) is an independent risk factor for atherosclerotic (ASCVD) events and associates with aortic stenosis. Accurate immunochemical measurement of Lp(a) is complicated by heterogeneity of Lp(a) molecular size. The objective of this study was to validate the analytical performance of an electrophoretic assay for measurement of ApoB-containing lipoprotein particles including Lp(a)-P, LDL-P, and VLDL-P and to compare results to other standard clinical methods. Method Lp(a)-P, LDL-P, and VLDL-P were quantified by electrophoretic serum separation followed by immuno-staining with an ApoB antibody using the SPIFE Touch (Helena Laboratories, Beaumont, TX). Stability was evaluated by comparison to results at time zero (n = 10). Precision was assessed by 5 measurements twice daily for five days. Limit of quantitation (LoQ) was determined by 20 replicate measurements. Linearity was assessed by mixing high and low concentrations at various ratios. Accuracy was assessed by split sample comparison (n = 126) with Helena Laboratories. Lp(a)-P and LDL-P results were compared to nuclear magnetic resonance (NMR) using a Bruker Ascend 600 NMR (Bruker, Billerica, MA) with AXINON software (numares, AG, Regensberg, Germany) (n = 114). Lp(a)-P is included with LDL-P by NMR. Therefore, the SPIFE Lp(a)-P + LDL-P was compared to NMR LDL-P. SPIFE Lp(a)-P was compared directly to Roche cobas c501 Lp(a) immunoassay calibrated to molar units (Roche Diagnostics, Indianapolis, IN) (n = 126). Results The analytical performance of Lp(a)-P, LDL-P, and VLDL-P measured on the SPIFE Touch met all acceptance criteria. Bias to the reference method was <20% with R2 > 0.98. All analytes were stable 14 days refrigerated and 14 days frozen. Lipoprotein particle measurements on SPIFE Touch correlated to those obtained by both NMR and Roche assays. Conclusion The analytical performance of the SPIFE Touch for measuring the concentration of ApoB-containing lipoproteins is acceptable. Further studies are needed to determine clinical implications and utility compared to NMR and Roche assays.

New more effective lipid-lowering therapies have made it important to accurately determine Low-density lipoprotein-cholesterol (LDL-C) at both high and low levels. LDL-C was measured by the β-quantification reference method (BQ) (N = 40,346) and compared to Friedewald (F-LDL-C), Martin (M-LDL-C), extended Martin (eM-LDL-C) and Sampson (S-LDL-C) equations by regression analysis, error-grid analysis, and concordance with the BQ method for classification into different LDL-C treatment intervals. For triglycerides (TG) < 175 mg/dL, the four LDL-C equations yielded similarly accurate results, but for TG between 175 and 800 mg/dL, the S-LDL-C equation when compared to the BQ method had a lower mean absolute difference (mg/dL) (MAD = 10.66) than F-LDL-C (MAD = 13.09), M-LDL-C (MAD = 13.16) or eM-LDL-C (MAD = 12.70) equations. By error-grid analysis, the S-LDL-C equation for TG > 400 mg/dL not only had the least analytical errors but also the lowest frequency of clinically relevant errors at the low (<70 mg/dL) and high (>190 mg/dL) LDL-C cut-points (S-LDL-C: 13.5%, F-LDL-C: 23.0%, M-LDL-C: 20.5%) and eM-LDL-C: 20.0%) equations. The S-LDL-C equation also had the best overall concordance to the BQ reference method for classifying patients into different LDL-C treatment intervals. The S-LDL-C equation is both more analytically accurate than alternative equations and results in less clinically relevant errors at high and low LDL-C levels.