Within-individual Biological Variation Research Articles

On-Field Rule Out of Mild Traumatic Brain Injury: Can Blood-Based Biomarker Testing Change Outcome of Major Sporting Events?

Mild traumatic brain injury (mTBI) is defined as a Glascow Coma Score of between 13 and 15. The diagnosis and rule out of individuals suffering from mTBI on an acute basis is imperfect and involves subjective measures. Serum biomarkers that exhibit narrow within-individual biological variation can be used for the early rule-out of mTBI, when baseline levels are compared during health. This is a descriptive study that applies published biological variation data of serum mTBI biomarkers for early rule out of sports-related injury. Laboratory tests such as glial fibrillary acidic protein, fatty acid binding protein 7, and phosphorylated protein enriched in astrocytes have low within-individual variances and are potential candidates. Aldolase C also rises early in blood but the biological variation is of this marker is currently unknown. The use of blood-based biomarkers, measured in real time using point-of-care testing devices when compared to a pre-competition baseline instead of a population-based reference interval, can provide early rule out of mTBI, and possibly enable on-field evaluations and a medical decision for a return to competition.

Optimization and validation of patient-based real-time quality control procedure using moving average and average of normals with multi-rules for TT3, TT4, FT3, FT3, and TSH on three analyzers.

BackgroundWe have designed a patient‐based real‐time quality control (PBRTQC) procedure to detect analytical shifts and review analytical trends of measurement procedures.MethodsAll the nine months' patient results of total thyroxine (TT4), total triiodothyronine (TT3), free thyroxine (FT4), free triiodothyronine (FT3), and thyrotropin (TSH) measured by three identical analyzers were divided into three groups according to the source of inpatient patients, outpatient patients, and healthy people. The data in each group were truncated by optimized Box‐Plot method and normalized by Box‐Cox method if necessary. The z‐score charts of internal quality control (IQC) samples' results and PBRTQC data were drawn by IQC levels and groups, respectively. The analytical shifts and analytical trends were detected by multi‐rules of 2‐2S rules and moving average rules. The performances of PBRTQC were compared with the BIQC in which IQC samples were measurand only once per day at the beginning of the analytical batch. Twelve quality control cases were listed to validate the performances.ResultsAll the five analytes presented normal distributions when the parameter n of Box‐Plot method was 1.2. The percentages of excluded data ranged from 2.9% to 11.6%. 31 and 14 rejections triggered in PBRTQC and BIQC, respectively. 96.8% of the shift rejections in PBRTQC were trend‐related shifts and calibration‐related shifts, while the proportion was 85.7% in BIQC but 78.6% of the shift rejections in TSH. 25.7% and 8.6% of 105 calibration events which caused analytical shifts were detected by PBRTQC and BIQC, respectively. However, the performance of PBRTQC was not well in TSH because of its large coefficient of variation.ConclusionsThe optimized PBRTQC is high efficiency than BIQC in detecting analytical shifts, trends, and calibration events. The PBRTQC can be used as a low‐cost supplementary procedure to IQC every day, especially at the end of the analytical batch on that day when the within‐individual biological variation of analyte is not larger than its coefficient of variation in IQC. Further optimization and validation of PBRTQC are still needed.

Comparison of Application Biological Variation and Patient Data for Selecting Delta Check Limits.

This study aimed to evaluate delta check limits set by reference change value (RCV) and patient data. Patient data of 11 clinical chemistry analytes from June 2018 to May 2019 were collected. RCV with 95% or 99% levels of probability were calculated based on biological variation. The corresponding delta check limits for outpatients and inpatients were calculated by 95% or 99% central range of delta% which was the difference of two consecutive results within thirty days of the same patient for each analyte. Patient data in June 2019 were used to analyze the utility of delta check limits. In total, 434,927 paired results for these 11 analytes were included. The delta check limits were different between outpatients and inpatients, but were wider than those established by RCV. The difference between Glu's outpatient and inpatient boundaries was the largest, 95% central range from the outpatient (inpatients) was from -32.29% (-56.97%) to 38.78% (106.00%) while 99% central range from the outpatient (inpatients) was from -56.86% (-90.56%) to 89.96% (262.54%). The RCV is mainly determined by within-individual biological variation so that the RCV of each analyte varied from each other. As for RCV, Na had the lowest value and BUN had the highest one. In addition, the main reason for delta% exceeding delta check limits was a clinically significant change. Laboratories could use delta check procedure to find out errors in sample collection and monitor clinical significance. When delta% of patients exceed corresponding delta check limits in a short time, clinicians and personnel of clinical laboratory should pay more attention. Delta check limits should be reviewed regularly to check the utility of procedure.

Impact of analytical and biological variations on classification of diabetes using fasting plasma glucose, oral glucose tolerance test and HbA1c

Historically, diabetes is diagnosed by measuring fasting (FPG) and two-hour post oral glucose load (OGTT) plasma concentration and interpreting it against recommended clinical thresholds of the patient. More recently, glycated haemoglobin A1c (HbA1c) has been included as a diagnostic criterion. Within-individual biological variation (CVi), analytical variation (CVa) and analytical bias of a test can impact on the accuracy and reproducibility of the classification of a disease. A test with large biological and analytical variation increases the likelihood of erroneous classification of the underlying disease state of a patient. Through numerical simulations based on the laboratory results generated from a large population health survey, we examined the impact of CVi, CVa and bias on the classification of diabetes using fasting plasma glucose (FPG), oral glucose tolerance test (OGTT) and HbA1c. From the results of the simulations, HbA1c has comparable performance to FPG and is better than OGTT in classifying subjects with diabetes, particularly when laboratory methods with smaller CVa are used. The use of the average of the results of the repeat laboratory tests has the effect of ameliorating the combined (analytical and biological) variation. The averaged result improves the consistency of the disease classification.

A simple matrix of analytical performance to identify assays that risk patients using External Quality Assurance Program data

ObjectivesWe propose a simple way to reliably rank assays for improvement according to patient risk, based solely on EQA imprecision and biological variation data. Because the underlying technique aligns the imprecision class of an assay from EQA data, peer performance can be used to assess achievable imprecision and the risk ranking can not only prioritise improvement but also highlight laboratory QC operating parameters that are easy to manage and provide reliable, acceptable performance. Design and methodsA modified Failure Modes Effects Analysis (FMEA) is applied to produce an analyte risk rating based on three factors, each of which is graded: 1) the ease of detecting analytical errors based on the ratio of allowable limits of performance to imprecision (Assay Capability) compared to absolute standards and to peers, 2) the predicted frequency of errors in patient monitoring based on the ratio of within-individual biological variation to laboratory imprecision, and 3) the clinical importance of the assay as a surrogate marker for harm arising from an error. ResultsWe provide laboratory examples to illustrate these models. ConclusionThe proposed models using only EQA data can objectively identify assays at risk of failing against biological variation goals for monitoring patients and suggest parameters for reliable performance.

Derivation of pediatric within-individual biological variation by indirect sampling method: an LMS approach.

Pediatric within-individual biological variation (CVi) is a challenge to derive by direct sampling due to clinical, logistical, and ethical barriers. Laboratory results of 22 basic biochemistry tests performed on 9,356 children who visited primary care physicians more than once over a year were obtained from a large laboratory network in Australia. The CVi were calculated as (CVT (2) - CVa (2))(0.5), where CVT was the coefficient of variation between repeat measurements and CVa was the analytical imprecision. Smoothed 50th centile (median) CVi charts were derived using the LMS ChartMaker Light software (Medical Research Council, Cambridge, England) with L, M, and S parameters fixed at 3.0, 3.0, and 3.0 equivalent degrees of freedom, respectively. In general, the median CVi trends for this pediatric cohort remained relatively stable with increasing age. Only aspartate aminotransferase, globulin, phosphate, urea, and creatinine had differences between the highest and lowest median CVi of more than 30%. The differences between the child and adult CVi were relatively small. Nearly all the analytes had child to adult CVi ratios of 1.0 ± 0.5. The median CVi derived from patients with only two repeat biochemistry measurements may be considered reasonable estimates of CVi among children seeking treatment at primary care settings. The LMS approach allowed visualization of the continuous trends of CVi with age and extended the pediatric CVi estimation to younger than 4 years.

Estimation of long-term paediatric within-individual biological variation using a data mining approach

Estimation of long-term paediatric within-individual biological variation using a data mining approach

Standardized flow cytometry assay for identification of human monocytic heterogeneity and LRP1 expression in monocyte subpopulations: Decreased expression of this receptor in nonclassical monocytes

In this article, we present a flow cytometry assay by which human blood monocyte subpopulations-classical (CD14(++) CD16(-)), intermediate (CD14(++) CD16(+)), and nonclassical (CD14(+) CD16(++)) monocytes-can be determined. Monocytic cells were selected from CD45(+) leukocyte subsets by differential staining of the low-density lipoprotein receptor-related protein 1 (LRP1), which allows reducing the spill-over of natural killer cells and granulocytes into the CD16(+) monocyte gate. Percentages of monocyte subpopulations established by this procedure were significantly comparable with those obtained by a well-standardized flow cytometry assay based on the HLA-DR monocyte-gating strategy. We also demonstrated that LRP1 is differentially expressed at cell surface of monocyte subpopulations, being significantly lower in nonclassical monocytes than in classical and intermediate monocytes. Cell surface expression of LRP1 accounts for only 20% of the total cellular content in each monocyte subpopulation. Finally, we established the within-individual biological variation (bCV%) of circulating monocyte subpopulations in healthy donors, obtaining values of 21%, 20%, and 17% for nonclassical, intermediate, and classical monocytes, respectively. Similar values of bCV% for LRP1 measured in each monocyte subpopulation were also obtained, suggesting that its variability is mainly influenced by the intrinsic biological variation of circulating monocytes. Thus, we conclude that LRP1 can be used as a third pan-monocytic marker together with CD14 and CD16 to properly identify monocyte subpopulations. The combined determination of monocyte subpopulations and LRP1 monocytic expression may be relevant for clinical studies of inflammatory processes, with special interest in atherosclerosis and cardiovascular disease.

Biological variation of INR in stable patients on long-term anticoagulation with warfarin

Within-individual biological variation of INR (CVB) was assessed in 245 selected stable warfarin-treated patients monitored by three thrombosis centers. Selection criteria were: treatment period of six months or longer before the observation period; at least six consecutive INRs within the therapeutic range of 2.0 – 3.0; interval between consecutive INR measurements of two weeks or longer; no change in warfarin dose; no changes in the patient's circumstances which may influence the INR, such as intercurrent diseases, invasive procedures, starting or stopping drugs interacting with warfarin. The minimum, maximum and mean within-individual coefficient of variation CVB of the INR measurements in the 245 selected patients were 0.4%, 14.5%, and 9.0%, respectively Analytical performance goals for the INR measurement (imprecision) could be derived from the mean CVB. For a therapeutic range of 2.0 – 3.0 with warfarin, the desirable and optimum imprecision of INR determination is <4.5% CV and <2.25% CV, respectively. The biological variation and analytical performance goals have been derived using classic laboratory methods but should be applicable to point-of-care testing as well.

Improved Monitoring of Differences in Serial Laboratory Results

The results of investigations in laboratory medicine are used for many purposes. Most are performed for monitoring individuals in acute situations and following the improvement or deterioration of chronic disease. In diagnosis, case-finding, and screening, conventional population-based reference intervals are relevant, and they are partitioned if necessary according to age, sex, or other important characteristics. In spite of the advantages of fixed decision limits, these intervals remain the mainstay of interpreting numerical results. Such tools are of very limited use, however, in evaluating serial results obtained for an individual. Within-individual biological variation is well known to be much smaller than between-individual variation for nearly all substances assayed in laboratory medicine (1). Each individual has a range of values that span only a part of the reference interval. In consequence, individuals can have important changes in results when they all lie within the reference interval. Such changes will usually be considered unremarkable by both laboratory professionals and clinicians, and thus ignored. In addition, results can change from inside to outside the interval (and vice versa) without having clinical importance. Laboratories conventionally flag results outside the reference limits, probably provoking some unnecessary follow-up activity, if only a repetition of the investigation (2). Katzmann and coworkers in the current issue of the Journal have tackled the difficult problem of establishing criteria for monitoring changes in monoclonal protein concentrations (3). For some laboratory investigations, expert groups have recommended numerical criteria for interpreting changes. For the abnormal proteins in serum and urine investigated by Katzmann and colleagues (3), the guidelines from such expert groups state that reductions in the monoclonal protein in serum of at least 25% and 50% are considered minimal and partial responses, respectively, and the corresponding responses for urine monoclonal protein require at least 50% and 90% decreases (4, 5 …

Serum CrossLaps compared with other markers of bone turnover in severely malnourished children before and after refeeding.

In growing children, bone turnover is generally high. There are several biochemical markers of bone formation and resorption that have been validated in adults by bone histomorphometry and calcium kinetics. Markers of bone formation are all measured in plasma and include bone-specific alkaline phosphatase (ALP), osteocalcin, and the C-terminal propeptide of type I collagen (PICP). Their use as markers of growth and bone formation in children is now well established (1)(2). However, most markers of bone resorption are measured in urine, including total pyridinoline (Pyd) and the more bone-specific deoxypyridinoline (Dpd). The main problems associated with the urinary markers in pediatric practice are high within-individual biological variation and the need for either timed collections (frequently impossible in children) or, for random urine samples, expressing results as a ratio to creatinine. Creatinine is itself subject to biological variation and changes with muscle mass. Dpd:creatinine excretion in children is highly variable (3) and hence relatively insensitive to therapeutic interventions. Until recently, only one commercially available marker of bone resorption could be measured in plasma, the cross-linked telopeptide of type I collagen (ICTP). Plasma ICTP appears to be less sensitive than some of the other markers to changes in bone resorption in certain clinical situations (e.g., bisphosphonate treatment), perhaps because of metabolism by osteoclastic cathepsin K (4). There is now a new marker for bone resorption that can be measured in serum or plasma, serum CrossLapsTM (5), for which we have recently reported pediatric reference data (6). The CrossLaps assay is specific for the amino acid sequence EKAHD-β-GGR where the aspartic acid residue (D) is β-isomerized. It detects only cross-linked degradation products of C-terminal telopeptides of type I collagen and is therefore specific for bone resorption. It has been clinically evaluated in adults in whom changes in CrossLaps were inversely …

Serum CrossLaps: Pediatric Reference Intervals from Birth to 19 Years of Age

During childhood growth, bone undergoes extensive modeling involving separate osteoblastic and osteoclastic processes. Markers of bone formation and resorption circulate at higher concentrations in children than in adults, parallel the childhood growth curve, and correlate with height velocity (1)(2). Not only do these markers provide insight into the pathophysiology of bone turnover during growth, but they also give an early surrogate measure of its response to treatment. The markers of bone formation are all measured in plasma, and their use as markers of growth and bone formation in children is well established (1)(2). However, most markers of bone resorption have traditionally been measured in urine. In infants and children, the practical difficulties associated with urine collection are compounded by marked circadian variation and high within-individual biological variation in urinary markers. Results are generally expressed in relation to creatinine, itself subject to considerable biological variation and changing with age as muscle mass increases. There is therefore a need for a sensitive and specific marker of bone resorption that can be measured in plasma and directly compared with markers of bone formation measured in the same sample. Serum CrossLapsTM is a promising new marker for bone resorption (3), but its application in children has been hampered by lack of suitable reference data. Here we report age- and sex-related reference data for serum CrossLaps in children from birth to 19 years of age. Surplus plasma remaining after routine biochemical tests had been completed was retrieved from 59 neonates, infants, and children (37 males) who presented with various minor conditions that were considered not to have either a short- or long-term effect on growth. Children with systemic disease or intercurrent infections were specifically excluded. Samples were fully anonymized and stored at −70 °C until analysis. We analyzed stored plasma …

Biochemical markers of bone turnover

Background: Osteoporosis in many countries has reached epidemic proportions. This has stimulated the development of biochemical markers to assist in the assessment of osteoporotic risk and in monitoring the efficacy of treatment. Biochemical markers of bone turnover are products released from osteoblasts and osteoclasts or collagen breakdown products. Markers: Markers of bone formation include bone-specific alkaline phosphatase (BAP), osteocalcin (OC) and procollagen peptides. All of these can be measured easily by immunoassay techniques. Of these markers, OC has been extensively studied. However, OC undergoes in vitro degradation, thus, assay results are variable. BAP, on the other hand, is much more stable and shows less within-person biological variation. Bone resorption markers include tartrate-resistant acid phosphatase (TRAP) and collagen breakdown products, such as pyridinium cross-links, galactosyl hydroxylysine and cross-linked telopeptides, such as CTx and NTx. Of these, deoxypyridinium (DPD) has been extensively studied. DPD shows diurnal variation and the within-individual biological variation is large. Of the newer assays, NTx appear to show large differences at menopause. Conclusions: Thus, serum BAP and DPD or NTx are the current choice of bone markers.