Short- and Long-Term Biological Variability of Small Dense LDL, HDL3, and Triglyceride-Rich Lipoprotein Cholesterol.

BACKGROUND: Measurement of cholesterol within lipoprotein subfractions may aid in cardiovascular disease prediction. Simple, homogenous enzymatic assays for the direct measurement of lipoprotein subfractions have been developed to measure small dense low-density lipoprotein cholesterol (sdLDL-C), high-density lipoprotein-3 cholesterol (HDL3-C), and triglyceride-rich lipoprotein (TRL-C) cholesterol. The objective of this study was to determine biological variability for sdLDL-C, HDL3-C, and TRL-C in a healthy reference population to facilitate interpretation of these analytes. METHODS: Serum samples were collected from 24 healthy subjects (n = 14 female/10 male) daily for 3 days while non-fasting, and daily for 5 days, weekly for 4 weeks, and monthly for 6 months after overnight fasting. sdLDL-C, HDL3-C, and TRL-C cholesterol were measured by homogenous enzymatic assays. Sources of variability (between-subject, within-subject, and analytical) were calculated using random-effects regression models. Reference change value (RCV) and index of individuality (II) for each time period were determined from the variance components. RESULTS: Analytic variability (daily, weekly, and monthly CVA) was <3% for each analyte. Monthly within-subject variability (CVI) was 17.1% for sdLDL-C, 7.4% for HDL3-C, and 25.7% for TRL-C. Most of the monthly variation was attributed to between-subject variation for all 3 analytes. Overall RCVs for monthly measurements were 18.1 mg/dL for sdLDL-C, 6.1 mg/dL for HDL3-C, and 16.0 mg/dL for TRL-C. IIs were <0.6 for sdLDL-C and HDL3-C, and 0.81 for TRL-C. CONCLUSIONS: sdLDL-C, HDL3-C, and TRL-C showed moderate within-subject variability, but high between-subject variability, in a healthy reference population. Given the high individuality of each analyte, population-based reference intervals may be inadequate to detect clinically significant changes.

Estimating short- and long-term reference change values and index of individuality for tests of platelet function.

BACKGROUND: In order to manage risks of bleeding and thrombosis after some surgical procedures, platelet function is often measured repeatedly over days or weeks using laboratory tests of platelet function. To interpret test results in the perioperative period, it is necessary to understand analytical, biological and between-person variation. METHODS: We collected three separate blood specimens from 16 healthy volunteers on the first study day, and one additional specimen from each volunteer 1, 2, and 3 months later. Arachidonic acid-induced and adenosine diphosphate (ADP)-induced platelet function were measured in duplicate by whole blood impedance aggregometry using Multiplate (ASPI/ADP tests) and VerifyNow (Aspirin Reaction Units [ARU] and P2Y12 Reaction Units [PRU]). The analytical variation (CVA), within-subject variation (CVI), between-subject variation (CVG), index of individuality (II), and reference change values (RCV) were calculated. RESULTS: VerifyNow ARU demonstrated the smallest short-term and long-term variability (CVA, CVI, and CVG ~1%), resulting in short- and long-term RCV values <5%. II was also higher (1.92) for VerifyNow ARU than other platelet function tests. Multiplate ASPI and ADP tests had the highest RCV both short-(19.0% and 25.2%, respectively) and long-term (32.1% and 39.6%, respectively) due to increased CVA (>5%) and CVI (3.9-13.1%). VerifyNow PRU had a lower RCV than Multiplate ADP; but was the only test with II <0.6. CONCLUSIONS: VerifyNow ARU results can be interpreted relative to a fixed cut-off or population-based reference interval; or relative to small changes in an individual's previous values. VerifyNow PRU and Multiplate ASPI and ADP tests should only be interpreted based upon relative change; and can only distinguish relatively large (>23%) changes over several weeks.

Comparing analytical outliers and the percent of emergency department patients with results above the 99th percentile upper reference limit for 2 conventional and one high sensitivity troponin assay.

OBJECTIVES: We compared rates of analytical outliers, and percent of emergency department (ED) patients with cardiac troponin (cTn) values above the 99th percentile upper reference limit (URL), for two conventional and one high sensitivity cTn assay. METHODS: We measured 3008 samples from 1931 ED patients by Roche e411 4th generation Troponin T (cTnT); and Abbott STAT Troponin I (cTnI) and high sensitivity troponin I (hscTnI) on an Architect i2000. Within 24h of initial measurement, samples were aliquoted, re-centrifuged, and repeated in duplicate by all methods. Outliers were defined as one or both replicates exceeding initial value by a critical difference (CD): where CD=z×2×SDanalytical (z=3.29 at a probability of 0.0005), and at least one replicate on a different side of 99th percentile URL compared to initial value. We also assessed percent of ED patients with values >99th percentile by all methods (excluding outliers), using both sex-neutral and sex-specific hscTnI URL. RESULTS: The outlier rate for cTnI (3.66%) was significantly higher than the outlier rate for either cTnT (0.33%) or hscTnI (0.47%) (p<0.0001). More ED patients (33%) had elevated cTnT values compared to either cTnI (25%) or hscTnI (29%). Application of sex-specific URL did not change the percent of ED patients with >99th percentile hscTnI values. CONCLUSION: Abbott STAT cTnI had more analytic outliers than Roche cTnT or Abbott hscTnI. Compared to cTnT, use of hscTnI will significantly decrease the percent of ED patients with elevated cTn values without increasing analytical outliers.