Biological variation and reference change values: an essential piece of the puzzle of laboratory testing

Coffee intake one hour prior to phlebotomy produces no clinically significant changes in routine biochemical test results

Introduction

Although current guidelines recommend not drinking coffee prior to phlebotomy, our hypothesis is that drinking coffee does not affect the clinical interpretation of biochemical and haematological test results.

Materials and methods

Twenty-seven volunteers were studied in basal state (T0) and 1h after (T1) drinking coffee. Routine haematological (Sysmex-XN1000 analyser) and biochemistry parameters (Vitros 4600 analyser) were studied. Results were compared using the Wilcoxon test (P < 0.05). A clinical change was considered when mean percent difference (MD%) was higher than the reference change value (RCV).

Results

Coffee intake produced statistically, but not clinically, significant: i) increases in haemoglobin (P = 0.009), mean cell haemoglobin concentration (P = 0.044), neutrophils (P = 0.001), albumin (P = 0.001), total protein (P = 0.000), cholesterol (P = 0.025), high density lipoprotein cholesterol (P = 0.007), uric acid (P = 0.011), calcium (P = 0.001), potassium (P = 0.010), aspartate aminotransferase (P = 0.001), amylase (P = 0.026), and lactate dehydrogenase (P = 0.001), and ii) decreases in mean cell volume (P = 0.002), red cell distribution width (P = 0.001), eosinophils (P = 0.002), and lymphocytes (P = 0.001), creatinine (P = 0.001), total bilirubin (P = 0.012), phosphorus (P = 0.001), magnesium (P = 0.007), and chloride (P = 0.001).

Conclusion

Drinking a cup of coffee 1 hour prior to phlebotomy produces no clinically significant changes in routine biochemical and haematological test results.

Estimation of inter-laboratory reference change values from external quality assessment data

Introduction

It is common for patients to switch between several healthcare providers. In this context, the long-term follow-up of medical conditions based on laboratory test results obtained from different laboratories is a challenge. The measurement uncertainty in an inter-laboratory context should also be considered in data mining research based on routine results from randomly selected laboratories. As a proof-of-concept study, we aimed at estimating the inter-laboratory reference change value (IL-RCV) for exemplary analytes from publicly available data on external quality assessment (EQA) and biological variation.

Materials and methods

External quality assessment data of the Reference Institute for Bioanalytics (RfB, Bonn, Germany) for serum creatinine, calcium, aldosterone, PSA, and of whole blood HbA1c from campaigns sent out in 2019 were analysed. The median CVs of all EQA participants were calculated based on 8 samples from 4 EQA campaigns per analyte. Using intra-individual biological variation data from the EFLM database, positive and negative IL-RCV were estimated with a formula based on log transformation under the assumption that the analytes under examination have a skewed distribution.

Results

We estimated IL-RCVs for all exemplary analytes, ranging from 13.3% to 203% for the positive IL-RCV and - 11.8% to - 67.0% for the negative IL-RCV (serum calcium - serum aldosterone), respectively.

Conclusion

External quality assessment data together with data on the biological variation – both freely available – allow the estimation of inter-laboratory RCVs. These differ substantially between different analytes and can help to assess the boundaries of interoperability in laboratory medicine.

Biological Variation: The Effect of Different Distributions on Estimated Within-Person Variation and Reference Change Values

BACKGROUND

Good estimates of within-person biological variation, CVI, are essential for diagnosing and monitoring patients and for setting analytical performance specifications. The aim of the present study was to use computer simulations to evaluate the impact of various measurement distributions on different methods for estimating CVI and reference change value (RCV).

METHOD

Data were simulated on the basis of 3 models for distributions of the within-person effect. We evaluated 3 different methods for estimating CVI: standard ANOVA, ln-ANOVA, and CV-ANOVA, and 3 different methods for calculating RCV: classic, ln-RCV, and a nonparametric method. We estimated CVI and RCV with the different methods and compared the results with the true values.

RESULTS

The performance of the methods varied, depending on both the size of the CVI and the type of distributions. The CV-ANOVA model performed well for the estimation of CVI with all simulated data. The ln-RCV method performed best if data were ln-normal distributed or CVI was less than approximately 12%. The nonparametric RCV method performed well for all simulated data but was less precise.

CONCLUSIONS

The CV-ANOVA model is recommended for both calculation of CVI and the step-by-step approach of checking for outliers and homogeneity in replicates and samples. The standard method for calculation of RCV should not be used when using CVs.

A new device to relieve venipuncture pain can affect haematology test results

BACKGROUND

In vitro diagnostic tests play a key role in patients' management (e.g., guiding red blood cell transfusions). The aim of this study was to evaluate the impact of an innovative device (Buzzy®) which is claimed to be able to relieve venipuncture pain by means of cold and vibration. This device was applied during collection of venous blood by venipuncture for conventional haematology testing.

MATERIALS AND METHODS

Blood was drawn from 100 volunteers by a single, expert phlebotomist. A vein was located in the left forearm without applying a tourniquet but using a subcutaneous tissue transilluminator device, so that venous stasis was avoided. Blood samples were collected with a 20G straight needle directly into 4 mL K3EDTA vacuum tubes. In sequence, external cold and vibration was established by Buzzy® on the right forearm -5 cm above the venipuncture site- for 1 minute before venipuncture and continued until the end of the same procedure already performed in the left forearm. Conventional haematological tests were performed using the same instrument (Sysmex® XE-2100D) in all cases.

RESULTS

When Buzzy® was applied before drawing blood, erythrocyte counts and associated parameters (i.e., haemoglobin and haematocrit) were higher, whereas platelet number, leucocyte count and differential were lower. Statistically and clinically significant differences (P<0.001) were observed for erythrocytes, haemoglobin and haematocrit.

DISCUSSION

From a practical perspective, cold-induced haemoconcentration promotes the efflux of water, diffusible ions and low molecular weight molecules from the vessel, thus increasing the concentration of other blood analytes at the puncture site. These variations may influence test results, especially for erythrocytes, haemoglobin and haematocrit. The novel Buzzy® device should, therefore, be used with caution when collecting blood for conventional haematological testing because of the observed bias introduced in some parameters.

Biological variability of lymphocyte subsets of human adults' blood

BACKGROUND

Alterations in lymphocyte subpopulations are present in several immune diseases, and clinicians and researchers recognise the importance of investigating the distribution and changes in lymphocyte subsets over relatively long periods of time in order to evaluate the effectiveness of treatment and follow the course of disease. Yet further insight is required on the biological variability (BV) of lymphocyte subsets, which is crucial to the correct interpretation of longitudinal changes and provides essential information for setting desirable quality specifications and defining the usefulness of reference values.

METHODS

Four-colour-flow cytometry was used to investigate the BV of lymphocyte populations (LP) in the peripheral blood of 20 healthy adults recruited from our laboratory staff and followed for three months. The total lymphocyte count was measured, and the relative frequencies determined for T-cells (CD3+), T-helper cells (CD3+CD4+), cytolytic T-cells (CD3+CD8+), B-cells (CD3-CD19+), NK-cells (CD3-CD16+/56+), non-MHC restricted cytolytic T-cells (CD3+CD56+) and activated T-cells (CD3+HLA-DR+).

RESULTS AND CONCLUSIONS

Data on the components of BV were applied to set quality specifications for allowable precision, bias and total error. Analytical performances were established, and they were more than desirable for all the markers considered in our study. By comparing within-subject and between-subjects BV, we were able to define the uselessness of reference ranges in the evaluation of changes in CD serial results. Data on within-subject BV and analytical precision were thus used to determine the reference change values, in order to identify the significance of changes in serial results. The findings made in the present study provide further evidence of the relevance of BV in the evaluation of immunological markers of LP.

Evaluation of biological variation of glycated albumin (GA) and fructosamine in healthy subjects

BACKGROUND

Glycated albumin (GA) and fructosamine are nonenzymatically glycated proteins still frequently utilized for monitoring glycemic control in diabetics. To investigate the analytical variation and the degree of individuality of these glycemic markers, we have performed an experimental study under a well designed and standardized protocol.

METHODS

We collected five specimens from each of 18 apparently healthy subjects (9 men and 9 women, ages 26-52 years), on the same day, every two weeks for two months. Samples were stored at -80°C until analysis and assayed in duplicate in a single analytical run. GA and fructosamine were measured using enzymatic (Lucica®GA-L, Asahi Kasei Pharma, AKP, Tokyo, Japan) and colorimetric assays, respectively, on a Modular P Roche system (Roche Diagnostics GmbH, Mannheim, Germany). Data were analyzed by ANOVA.

RESULTS

Analytical coefficient of variation (CVA) was 1.7%, 2.3% and 2.8% for GA, albumin and fructosamine, respectively. Within-subject (CVW) and between-subject (CVG) coefficients of variation were 2.1% and 10.6% for GA, 2.3% and 2.9% for albumin, and 2.3% and 6.3% for fructosamine. The estimated critical difference (CD) was 7.5% for GA, 9% for albumin and 10% for fructosamine.

CONCLUSIONS

The good quality achieved by the analytical method for GA assessment and the reduced within-subject biological variation would allow to recommend this test in clinical practice for evaluation of glycemic control along with measurement of glycated hemoglobin.

Repeating tests: different roles in research studies and clinical medicine

Researchers often decide whether to average multiple results in order to produce more precise data, and clinicians often decide whether to repeat a laboratory test in order to confirm its validity or to follow a trend. Some of the major sources of variation in laboratory tests (analytical imprecision, within-subject biological variation and between-subject variation) and the effects of averaging multiple results from the same sample or from the same person over time are discussed quantitatively in this article. This analysis leads to the surprising conclusion that the strategy of averaging multiple results is only necessary and effective in a limited range of research studies. In clinical practice, it may be important to repeat a test in order to eliminate the possibility of a rare type of error that has nothing to do analytical imprecision or within-subject variation, and for this reason, paradoxically, it may be most important to repeat tests with the highest sensitivity and/or specificity (i.e., ones that are critical for clinical decision-making).

Quality impact on diagnostic blood specimen collection using a new device to relieve venipuncture pain

A new device called Buzzy(®) has been recently presented that combines a cooling ice pack and a vibrating motor in order to relieve the venipuncture pain. The aim of this study was to evaluate the impact of Buzzy(®) use during diagnostic blood specimen collection by venipuncture for routine immunochemistry tests. Blood was collected from 100 volunteers by a single, expert phlebotomist. A vein was located on the left forearm without applying tourniquet, in order to prevent any interference from venous stasis, and blood samples were collected using a 20-G straight needle directly into 5 mL vacuum tubes with clot activator and gel separator. In sequence, external cold and vibration by Buzzy(®) was applied on the right forearm-5 cm above the chosen puncture site-for 1 min before venipuncture and continued until the end of the same procedure already done in the left forearm. The panel of tests included the following: glucose, total cholesterol, HDL-cholesterol, triglycerides, total protein, albumin, c-reactive protein, urea, creatinine, uric acid, alkaline phosphatase, amylase, AST, ALT, g-glutamyltransferase, lactate dehydrogenase, creatine kinase, total bilirubin, phosphorus, calcium, magnesium, iron, sodium, potassium, chloride, lipase, cortisol, insulin, thyroid-stimulating hormone, total triiodothyronine, free triiodothyronine, total thyroxine, free thyroxine and haemolysis index. Clinically significant differences between samples were found only for: total protein, albumin and transferrin. The Buzzy(®) can be used during diagnostic blood specimens collection by venipuncture for the majority of the routine immunochemistry tests. We only suggest avoiding this device during blood collection when protein, albumin and transferrin determinations should be performed.

Preanalytical management: serum vacuum tubes validation for routine clinical chemistry

Introduction

The validation process is essential in accredited clinical laboratories. Aim of this study was to validate five kinds of serum vacuum tubes for routine clinical chemistry laboratory testing.

Materials and methods:

Blood specimens from 100 volunteers in five diff erent serum vacuum tubes (Tube I: VACUETTE^®, Tube II: LABOR IMPORT^®, Tube III: S-Monovette^®, Tube IV: SST^® and Tube V: SST II^®) were collected by a single, expert phlebotomist. The routine clinical chemistry tests were analyzed on cobas^® 6000 <c501> module. The significance of the diff erences between samples was assessed by paired Student’s t-test after checking for normality. The level of statistical significance was set at P < 0.005. Finally, the biases from Tube I, Tube II, Tube III, Tube IV and Tube V were compared with the current desirable quality specifications for bias (B), derived from biological variation.

Results and conclusions:

Basically, our validation will permit the laboratory or hospital managers to select the brand’s vacuum tubes validated according him/her technical or economical reasons, in order to perform the following laboratory tests: glucose, total cholesterol, high density lipoprotein-cholesterol, triglycerides, total protein, albumin, blood urea nitrogen, uric acid, alkaline phosphatise, aspartate aminotransferase, gamma-glutamyltransferase, lactate dehydrogenase, creatine kinase, total bilirubin, direct bilirubin, calcium, iron, sodium and potassium. On the contrary special attention will be required if the laboratory already performs creatinine, amylase, phosphate and magnesium determinations and the quality laboratory manager intend to change the serum tubes. We suggest that laboratory management should both standardize the procedures and frequently evaluate the quality of in vitro diagnostic devices.

Influence of a regular, standardized meal on clinical chemistry analytes

Background

Preanalytical variability, including biological variability and patient preparation, is an important source of variability in laboratory testing. In this study, we assessed whether a regular light meal might bias the results of routine clinical chemistry testing.

Methods

We studied 17 healthy volunteers who consumed light meals containing a standardized amount of carbohydrates, proteins, and lipids. We collected blood for routine clinical chemistry tests before the meal and 1, 2, and 4 hr thereafter.

Results

One hour after the meal, triglycerides (TG), albumin (ALB), uric acid (UA), phosphatase (ALP), Ca, Fe, and Na levels significantly increased, whereas blood urea nitrogen (BUN) and P levels decreased. TG, ALB, Ca, Na, P, and total protein (TP) levels varied significantly. Two hours after the meal, TG, ALB, Ca, Fe, and Na levels remained significantly high, whereas BUN, P, UA, and total bilirubin (BT) levels decreased. Clinically significant variations were recorded for TG, ALB, ALT, Ca, Fe, Na, P, BT, and direct bilirubin (BD) levels. Four hours after the meal, TG, ALB, Ca, Fe, Na, lactate dehydrogenase (LDH), P, Mg, and K levels significantly increased, whereas UA and BT levels decreased. Clinically significant variations were observed for TG, ALB, ALT, Ca, Na, Mg, K, C-reactive protein (CRP), AST, UA, and BT levels.

Conclusions

A significant variation in the clinical chemistry parameters after a regular meal shows that fasting time needs to be carefully considered when performing tests to prevent spurious results and reduce laboratory errors, especially in an emergency setting.