Imprecision of high-sensitivity cardiac troponin assays at the female 99th-percentile.

BACKGROUND: An analytical benchmark for high-sensitivity cardiac troponin (hs-cTn) assays is to achieve a coefficient of variation (CV) of ≤ 10.0 % at the 99th percentile upper reference limit (URL) used for the diagnosis of myocardial infarction. Few prospective multicenter studies have evaluated assay imprecision and none have determined precision at the female URL which is lower than the male URL for all cardiac troponin assays. METHODS: Human serum and plasma matrix samples were constructed to yield hs-cTn concentrations near the female URLs for the Abbott, Beckman, Roche, and Siemens hs-cTn assays. These materials were sent (on dry ice) to 35 Canadian hospital laboratories (n = 64 instruments evaluated) participating in a larger clinical trial, with instructions for storage, handling, and monthly testing over one year. The mean concentration, standard deviation, and CV for each instrument type and an overall pooled CV for each manufacturer were calculated. RESULTS: The CVs for all individual instruments and overall were ≤ 10.0 % for two manufacturers (Abbott CVpooled = 6.3 % and Beckman CVpooled = 7.0 %). One of four Siemens Atellica instruments yielded a CV > 10.0 % (CVpooled = 7.7 %), whereas 15 of 41 Roche instruments yielded CVs > 10.0 % at the female URL of 9 ng/L used worldwide (6 cobas e411, 1 cobas e601, 4 cobas e602, and 4 cobas e801) (CVpooled = 11.7 %). Four Roche instruments also yielded CVs > 10.0 % near the female URL of 14 ng/L used in the United States (CVpooled = 8.5 %). CONCLUSIONS: The number of instruments achieving a CV ≤ 10.0 % at the female 99th-percentile URL varies by manufacturer and by instrument. Monitoring assay precision at the female URL is necessary for some assays to ensure optimal use of this threshold in clinical practice.

Maternal and child biomonitoring strategies and levels of exposure in western Canada during the past seventeen years: The Alberta Biomonitoring Program: 2005-2021.

The Alberta Biomonitoring Program (ABP) was created in 2005 with the initial goal of establishing baseline levels of exposure to environmental chemicals in specific populations in the province of Alberta, Canada, and was later expanded to include multiple phases. The first two phases focused on evaluating exposure in pregnant women (Phase One, 2005) and children (Phase Two, 2004-2006) by analyzing residual serum specimens. Phase Three (2013-2016) employed active recruitment techniques to evaluate environmental exposures using a revised list of chemicals in paired serum pools from pregnant women and umbilical cord blood. These three phases of the program monitored a total of 226 chemicals in 285 pooled serum samples representing 31,529 individuals. Phase Four (2017-2020) of the ABP has taken a more targeted approach, focusing on the impact of the federal legalization of cannabis on the exposure of pregnant women in Alberta to cannabis, as well as tobacco and alcohol using residual prenatal screening serum specimens. Chemicals monitored in the first three phases include herbicides, neutral pesticides, metals, metalloids, and micronutrients, methylmercury, organochlorine pesticides, organophosphate pesticides, parabens, phthalate metabolites, perfluoroalkyl substances (PFAS), phenols, phytoestrogens, polybrominated compounds, polychlorinated biphenyls (PCBs), dioxins and furans, polycyclic aromatic hydrocarbons (PAHs), and tobacco biomarkers. Phase Four monitored six biomarkers of tobacco, alcohol, and cannabis. All serum samples were pooled. Mean concentrations and 95% confidence intervals (CIs) were calculated for the chemicals detected in ≥25% of the sample pools. cross the first three phases, the data from the ABP has provided baseline exposure levels for the chemicals in pregnant women, children, and newborns across the province. Comparison within and among the phases has highlighted differences in exposure levels with age, geography, seasonality, sample type, and time. The strategies employed throughout the program phases have been demonstrated to provide effective models for population biomonitoring.

Transformation of Sequential Hospital and Outpatient Laboratory Data into Between-Day Reference Change Values.

BACKGROUND: Serial differences between intrapatient consecutive measurements can be transformed into Taylor series of variation vs time with the intersection at time = 0 (y0) equal to the total variation (analytical + biological + preanalytical). With small preanalytical variation, y0, expressed as a percentage of the mean, is equal to the variable component of the reference change value (RCV) calculation: (CVA2 + CVI2)1/2. METHODS: We determined the between-day RCV of patient data for 17 analytes and compared them to healthy participants' RCVs. We analyzed 653 consecutive days of Dartmouth-Hitchcock Roche Modular general chemistry data (4.2 million results: 60% inpatient, 40% outpatient). The serial patient values of 17 analytes were transformed into 95% 2-sided RCV (RCVAlternate), and 3 sets of RCVhealthy were calculated from 3 Roche Modular analyzers' quality control summaries and CVI derived from biological variation (BV) studies using healthy participants. RESULTS: The RCVAlternate values are similar to RCVhealthy derived from known components of variation. For sodium, chloride, bicarbonate calcium, magnesium, phosphate, alanine aminotransferase, albumin, and total protein, the RCVs are equivalent. As expected, increased variation was found for glucose, aspartate aminotransferase, creatinine, and potassium. Direct bilirubin and urea demonstrated lower variation. CONCLUSIONS: Our RCVAlternate values integrate known and unknown components of analytic, biologic, and preanalytic variation, and depict the variations observed by clinical teams that make medical decisions based on the test values. The RCVAlternate values are similar to the RCVhealthy values derived from known components of variation and suggest further studies to better understand the results being generated on actual patients tested in typical laboratory environments.

Cord-Blood Derived Chemistry Reference Values in Preterm Infants for Sodium, Chloride, Potassium, Glucose, and Creatinine.

OBJECTIVES: International guidelines recommend that preterm infants should be supported to maintain their serum electrolytes within "normal" ranges. In term babies, cord blood values differed in pathological pregnancies from healthy ones. STUDY DESIGN: We examined cord blood sodium, chloride, potassium, glucose, and creatinine to derive maturity-related reference intervals. We examined associations with gestational age, delivery mode, singleton versus multiple, and prenatal maternal adverse conditions. We compared preterm cord values to term, and to adult reference ranges. RESULTS: There were 591 infants, 537 preterm and 54 term. Preterm cord glucose levels were steady (3.7 ± 1.1 mmol/L), while sodium, chloride, and creatinine increased over GA by 0.17, 0.14 mmol/L/week, and 1.07 µmol/L/week, respectively (p < 0.003). Average preterm cord potassium and chloride were higher than the term (p < 0.05). Compared with adult reference intervals, cord preterm reference intervals were higher for chloride (100-111 vs. 98-106 mmol/L), lower for creatinine (29-84 vs. 62-115 µmol/L), and more variable for potassium (2.7-7.9 vs. 3.5-5.0 mmol/L) and sodium (130-141 vs. 136-145 mmol/L). Cesarean section was associated with higher potassium and lower glucose, multiple births with higher chloride and creatinine and lower glucose, and SGA with lower glucose. CONCLUSIONS: Cord blood values varied across the GA range with increases in sodium, chloride, and creatinine, while glucose remained steady. Average preterm reference values were higher than term values for potassium and chloride. Preterm reference values differed from published adults' reference values. The changes across GA and by delivery mode, SGA, and being a multiple, which may have direct implications for neonatal care and fluid management. KEY POINTS: · Cord blood electrolyte, creatinine, and glucose values vary across neonatal gestational age.. · Average preterm cord values of potassium and chloride were higher than term values.. · Cord reference values differ by delivery mode, growth, and multiple impacting neonatal care decisions..

N-Acetylcysteine Interference with a Glucose Dehydrogenase Linked Glucose Meter.

BACKGROUND: Our objective was to determine the effect of therapeutic concentrations of N-acetylcysteine, following intravenous infusion, on the measurement of blood glucose using a Roche Diagnostics glucose dehydrogenase-linked glucose meter compared to hospital laboratory methods. METHODS: N-acetylcysteine was added to aliquots of blood, with glucose promptly measured by the glucose meter, blood gas analyzer (glucose oxidase comparative method) and following centrifugation, plasma glucose measured with a hexokinase spectrophotometric comparative method. Glucose results were evaluated with linear regression and Bland Altman plots. RESULTS: In the presence of NAC, at concentrations greater than 5 mg/dL (0.31 mmol/L), positively biased glucose meter results were compared to the clinical laboratory results. Multivariate linear regression revealed that NAC-mediated meter results are influenced by NAC and glucose concentrations. CONCLUSIONS: The addition of therapeutic concentrations of NAC to blood produces statistically significant positive biases when measured with the glucose dehydrogenase linked glucose meter device.

COVID-19 Pandemic Planning: Simulation Models to Predict Biochemistry Test Capacity for Patient Surges.

BACKGROUND: Patient surges beyond hospital capacity during the initial phase of the COVID-19 pandemic emphasized a need for clinical laboratories to prepare test processes to support future patient care. The objective of this study was to determine if current instrumentation in local hospital laboratories can accommodate the anticipated workload from COVID-19 infected patients in hospitals and a proposed field hospital in addition to testing for non-infected patients. METHODS: Simulation models predicted instrument throughput and turn-around-time for chemistry, ion-selective-electrode, and immunoassay tests using vendor-developed software with different workload scenarios. The expanded workload included tests from anticipated COVID patients in 2 local hospitals and a proposed field hospital with a COVID-specific test menu in addition to the pre-pandemic workload. RESULTS: Instrumentation throughput and turn-around time at each site was predicted. With additional COVID-patient beds in each hospital, the maximum throughput was approached with no impact on turnaround time. Addition of the field hospital workload led to significantly increased test turnaround times at each site. CONCLUSIONS: Simulation models depicted the analytic capacity and turn-around times for laboratory tests at each site and identified the laboratory best suited for field hospital laboratory support during the pandemic.

Quality assessment of RNA in long-term storage: The All Our Families biorepository.

BACKGROUND: The All Our Families (AOF) cohort study is a longitudinal population-based study which collected biological samples from 1948 pregnant women between May 2008 and December 2010. As the quality of samples can decline over time, the objective of the current study was to assess the association between storage time and RNA (ribonucleic acid) yield and purity, and confirm the quality of these samples after 7-10 years in long-term storage. METHODS: Maternal whole blood samples were previously collected by trained phlebotomists and stored in four separate PAXgene Blood RNA Tubes (PreAnalytiX) between 2008 and 2011. RNA was isolated in 2011 and 2018 using PAXgene Blood RNA Kits (PreAnalytiX) as per the manufacturer's instruction. RNA purity (260/280), as well as RNA yield, were measured using a Nanodrop. The RNA integrity number (RIN) was also assessed from 5-25 and 111-130 months of storage using RNA 6000 Nano Kit and Agilent 2100 BioAnalyzer. Descriptive statistics, paired t-test, and response feature analysis using linear regression were used to assess the association between various predictor variables and quality of the RNA isolated. RESULTS: Overall, RNA purity and yield of the samples did not decline over time. RNA purity of samples isolated in 2011 (2.08, 95% CI: 2.08-2.09) were statistically lower (p<0.000) than samples isolated in 2018 (2.101, 95% CI: 2.097, 2.104), and there was no statistical difference between the 2011 (13.08 µg /tube, 95% CI: 12.27-13.89) and 2018 (12.64 µg /tube, 95% CI: 11.83-13.46) RNA yield (p = 0.2964). For every month of storage, the change in RNA purity is -0.01(260/280), and the change in RNA yield between 2011 and 2018 is -0.90 µ g / tube. The mean RIN was 8.49 (95% CI:8.44-8.54), and it ranged from 7.2 to 9.5. The rate of change in expected RIN per month of storage is 0.003 (95% CI 0.002-0.004), so while statistically significant, these results are not relevant. CONCLUSIONS: RNA quality does not decrease over time, and the methods used to collect and store samples, within a population-based study are robust to inherent operational factors which may degrade sample quality over time.

Estimated Risk for Insulin Dose Error Among Hospital Patients Due to Glucose Meter Hematocrit Bias in 2020.

CONTEXT.­: Glycemic control requires accurate blood glucose testing. The extent of hematocrit interference is difficult to assess to assure quality patient care. OBJECTIVE.­: To predict the effect of patient hematocrit on the performance of a glucose meter and its corresponding impact on insulin-dosing error. DESIGN.­: Multilevel mixed regression was conducted to assess the extent that patient hematocrit influences Roche Accu-Chek Inform II glucose meters, using the Radiometer ABL 837 as a reference method collected during validation of 35 new meters. Regression coefficients of fixed effects for reference glucose, hematocrit, an interaction term, and random error were applied to 4 months of patient reference method results extracted from the laboratory information system. A hospital inpatient insulin dose algorithm was used to determine the frequency of insulin dose error between reference glucose and meter glucose results. RESULTS.­: Fixed effects regression for method and hematocrit predicted biases to glucose meter results that met the "95% within ±12%" for the US Food and Drug Administration goal, but combinations of fixed and random effects exceeded that target in emergency and hospital inpatient units. Insulin dose errors were predicted from the meter results. Twenty-eight percent of intensive care unit, 20.8% of hospital inpatient, and 17.7% of emergency department results were predicted to trigger a ±1 insulin dose error by fixed and random effects. CONCLUSIONS.­: The current extent of hematocrit interference on glucose meter performance is anticipated to cause insulin error by 1-dose category, which is likely associated with low patient risk.