GFR estimation is complicated by a high incidence of non-steady-state serum creatinine concentrations at the emergency department

Background Acquiring a reliable estimate of glomerular filtration rate (eGFR) at the emergency department (ED) is important for clinical management and for dosing renally excreted drugs. However, renal function formulas such as CKD-EPI can give biased results when serum creatinine (SCr) is not in steady-state because the assumption that urinary creatinine excretion is constant is then invalid. We assessed the extent of this by analysing variability in SCr in patients who visited the ED of a tertiary care centre. Methods Data from ED visits at the University Medical Centre Utrecht, the Netherlands between 2012 and 2019 were extracted from the Utrecht Patient Oriented Database. Three measurement time points were defined for each visit: last SCr measurement before visit as baseline (SCr-BL), first measurement during visit (SCr-ED) and a subsequent measurement between 6 and 24 hours during admission (SCr-H1). Non-steady-state SCr was defined as exceeding the Reference Change Value (RCV), with 15% decrease or 18% increase between successive SCr measurements. Exceeding the RCV was deemed as a significant change. Results Of visits where SCr-BL and SCr-ED were measured (N = 47,540), 28.0% showed significant change in SCr. Of 17,928 visits admitted to the hospital with a SCr-H1 after SCr-ED, 27,7% showed significant change. More than half (55%) of the patients with SCr values available at all three timepoints (11,054) showed at least one significant change in SCr over time. Conclusion One third of ED visits preceded and/or followed by creatinine measurement show non-stable serum creatinine concentration. At the ED automatically calculated eGFR should therefore be interpreted with great caution when assessing kidney function.

Annual biological variation and personalized reference intervals of clinical chemistry and hematology analytes

Objectives A large number of people undergo annual health checkup but accurate laboratory criterion for evaluating their health status is limited. The present study determined annual biological variation (BV) and derived parameters of common laboratory analytes in order to accurately evaluate the test results of the annual healthcare population. Methods A total of 43 healthy individuals who had regular healthcare once a year for six consecutive years, were enrolled using physical, electrocardiogram, ultrasonography and laboratory. The annual BV data and derived parameters, such as reference change value (RCV) and index of individuality (II) were calculated and compared with weekly data. We used annual BV and homeostatic set point to calculate personalized reference intervals (RIper) which were compared with population-based reference intervals (RIpop). Results We have established the annual within-subject BV (CVI), RCV, II, RIper of 24 commonly used clinical chemistry and hematology analytes for healthy individuals. Among the 18 comparable measurands, CVI estimates of annual data for 11 measurands were significantly higher than the weekly data. Approximately 50% measurands of II were <0.6, the utility of their RIpop were limited. The distribution range of RIper for most measurands only copied small part of RIpop with reference range index for 8 measurands <0.5. Conclusions Compared with weekly BV, for annual healthcare individuals, annual BV and related parameters can provide more accurate evaluation of laboratory results. RIper based on long-term BV data is very valuable for “personalized” diagnosis on annual health assessments.

Biological Variability of Small Dense LDL, HDL3, and Triglyceride-Rich Lipoprotein Cholesterol

The guideline-recommended lipid panel for cardiovascular disease (CVD) risk assessment measures total cholesterol, high-density lipoprotein (HDL) cholesterol, triglycerides, and calculated low-density lipoprotein (LDL) cholesterol. Measured cholesterol in subfractions of HDL and LDL purportedly improve CVD risk prediction. Homogenous enzymatic methods are now available for measurement of the cholesterol within small dense LDL (sLDL), small dense HDL (HDL3), and triglyceride-rich lipoproteins (TRL). For meaningful interpretation of these measurements, an understanding of the potential sources and extent of result variability is needed. The smallest difference between serial measurements within a patient that likely reflects a change in clinical status is called the reference change value (RCV). Biological variability and reference change values (RCV) are well-characterized for basic lipids but there is limited information for sLDL, HDL3 or TRL. The objective of this study was to determine intra- and inter-individual variability for sLDL, HDL3, and TRL in a healthy reference population. Serum samples were collected from 24 healthy subjects (n=14 female/10 male) daily for three days (non-fasting), daily for five days (fasting), weekly for four weeks (fasting), and monthly for 7 months (fasting). sLDL, HDL3, and TRL cholesterol were measured in duplicate by enzymatic colorimetric assays (Denka, Japan) on a Roche Cobas c501. Each source of variability (between subject, within subject, and analytical) was calculated using random-effects regression models to estimate each variance component including the overall variation, standard deviation (SD), coefficient of variation (CV), and proportion of total variance (between-subject, within-subject, and analytical). Using these analytical and biological variances, the reference change value (RCV), index of individuality (IoI), and intraclass correlation coefficient (ICC) were determined. Analytic variability (CVa) from monthly testing was 1.2%, 1.1%, and 1.5% for sLDL, HDL3, and TRL, respectively. Monthly within-subject variability (CVw) was 17.1% for sLDL, 7.4% for HDL3 and 25.7% for TRL. Monthly between-subject variability (CVb) was 32.2%, 13.93%, and 33.4% for sLDL, HDL3, and TRL, respectively. Most of the monthly variation was attributed to between-subject variation for all three tests. Within-subject variation accounted for 37% of TRL variation and 22% for both sLDL and HLD3. Within-subject RCVs for monthly measurements were 16.9mg/dL for sLDL, 5.3mg/dL for HDL3, and 15.1mg/dL for TRL. IoIs for monthly testing were 0.81 for TRL, 0.57 for sLDL, and 0.61 for HDL3. Our data demonstrate that sLDL, HDL3, and TRL show low analytical variability, moderate within-subject variability, but high between-subject variability when measured by homogenous assays in a healthy population. The IoI value (&gt;0.6) for TRL suggests use of a reference interval is appropriate for result interpretation. Conversely, clinical cut-points may be more useful than reference intervals for sLDL and HDL3 which had IoIs ~0.6. These findings may be useful for clinical interpretation, particularly when comparing successive measurements of these analytes.

Biological variation data for kidney function related parameter: serum beta trace protein, creatinine and cystatin C from 22 apparently healthy Turkish subjects

Objectives Biological variation is defined as the variation in analytical concentration between and within individuals, and being aware of this biological variation is important for understanding disease dynamics. The aim of our study is to calculate the within-subject (CVI) and between-subject (CVG) biological variations of serum creatinine, cystatin C and Beta trace protein (BTP), as well as the reference change value (RCV) and individuality indexes (II), which are used to calculate the glomerular filtration rate while evaluating kidney damage. Methods Blood samples were collected from 22 healthy volunteers for 10 consecutive weeks and stored at −80 °C until the day of analysis. While the analysis for serum creatinine was performed colorimetrically with the kinetic jaffe method, the nephelometric method was employed for cystatin C and BTP measurements. All analyses were carried out in a single session for each test. Results Analytical coefficient of variation (CVA) for serum creatinine, cystatin C and beta trace protein was 5.56, 3.48 and 5.37%, respectively. CVI and CVG: for serum creatinine: 3.31, 14.50%, respectively, for cystatin C: 3.15, 12.24%, respectively, for BTP: 9.91, 14.36%, respectively. RCV and II were calculated as 17.94%, 0.23 for serum creatinine, 13.01%, 0.26 for cystatin C, 31.24%, 0.69 for BTP, respectively. Conclusions According to the data obtained in our study, serum creatinine and cystatin C show high individuality, therefore we think that the use of RCV instead of reference ranges would be appropriate. Although II is found to be low for BTP, more studies are needed to support this finding.

Interferograms plotted with reference change value (RCV) may facilitate the management of hemolyzed samples

Background: The European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Working Group for Preanalytical Phase (WG-PRE) have recommended an algorithm based on the reference change value (RCV) to evaluate hemolysis. We utilized this algorithm to analyze hemolysis-sensitive parameters. Methods: Two tubes of blood were collected from each of the 10 participants, one of which was subjected to mechanical trauma while the other was centrifuged directly. Subsequently, the samples were diluted with the participant's hemolyzed sample to obtain the desired hemoglobin concentrations (0, 1, 2, 4, 6, 8, and 10 g/L). ALT, AST, K, LDH, T.Bil tests were performed using Beckman Coulter AU680 analyzer. The analytical and clinical cut-offs were based on the biological variation for the allowable imprecision and RCV. The algorithms could report the values directly below the analytical cut-off or those between the analytical and clinical cut-offs with comments. If the change was above the clinical cut-off, the test was rejected. The linear regression was used for interferograms, and the hemoglobin concentrations corresponding to cut-offs were calculated via the interferograms. Results: The RCV was calculated as 29.6% for ALT. Therefore, ALT should be rejected in samples containing >5.9 g/L hemoglobin. The RCVs for AST, K, LDH, and T.Bil were calculated as 27.9%, 12.1%, 19.2%, and 61.2%, while the samples' hemoglobin concentrations for test rejection were 0.8, 1.6, 0.5, and 2.2 g/L, respectively. Conclusions: Algorithms prepared with RCV could provide evidence-based results and objectively manage hemolyzed samples.

Biological variation and reference change value data for serum copper, zinc and selenium in Turkish adult population

Objectives Calculation of biological variation (BV) components is very important in evaluating whether a test result is clinically significant. The aim of this study is to analyze BV components for copper, zinc and selenium in a cohort of healthy Turkish participants. Methods A total of 10 serum samples were collected from each of the 15 healthy individuals (nine female, six male), once a week, during 10 weeks. Copper, zinc and selenium levels were analyzed by atomic absorption spectrometer. BV parameters were calculated with the approach suggested by Fraser. Results Analytical variation (CVA), within-subject BV (CVI), between-subject BV (CVG) values were 8.4, 7.1 and 4.3 for copper; 4.2, 9.1 and 13.7 for zinc; 7.6, 2.5 and 6.9 for selenium, respectively. Reference change values (RCV) were 30.46, 27.56 and 22.16% for copper, zinc and selenium, respectively. The index of individuality (II) values were 1.65, 0.66 and 0.36 for copper, zinc and selenium, respectively. Conclusions According to the results of this study, traditional reference intervals can be used for copper but we do not recommend using it for zinc and selenium. We think that it would be more accurate to use RCV value for zinc and selenium in terms of following significant changes in recurrent results of a patient.

Comparative study of Chemical Pathology sample collection tubes at the largest hospital in South Africa

Background: BarricorTM lithium heparin plasma tubes are new blood tubes that have been introduced to overcome the effects of gel in serum separator tubes (SST) and the shortcomings of standard lithium heparin plasma. We aimed to evaluate BarricorTM tubes as an alternative to serum separator tubes and compare the stability between the tubes. Methods: Forty-four paired samples were collected using both BarricorTM and SST. We compared five analytes at baseline (<6h) and after every 24h using the Passing-Bablok and Bland-Altman plots. Aspartate aminotransferase (AST), potassium (K), phosphate (PO4), lactate dehydrogenase (LDH), and creatinine were analysed in both tubes. We calculated the percentage difference for each analyte between the baseline and time intervals to assess analyte stability. The percentage difference was compared to the desirable specification for bias and reference change value (RCV). Results: All analytes were comparable at baseline. Statistical differences (p<0.001) became evident after 24h. PO4, K, and creatinine had a mean difference that exceeded the desirable specification for bias (-9.59%, -9.35%, and -4.59%, respectively). Potassium was stable up to 24h in both tubes. LDH showed better stability in SST (144h vs. 96h). PO4 concentrations were more stable in both tubes with the SST (96h vs. 72h). Creatinine and AST had the longest stability in both tubes compared to other analytes (144h). Conclusions: Data demonstrated variability and similarities in analyte concentrations and stability, respectively in both tubes