Clinical Chemistry Analytes Research Articles

Icteria interference for 34 clinical chemistry analytes on different analytical platforms: Method or analyzer dependent?

ObjectivesIn this study, we aimed to investigate the effect of increasing bilirubin concentration on 34 commonly measured clinical chemistry analytes on four different analytical platforms. We hypothesized that differences in icteria interference are not only method dependent, but also analyzer dependent. MethodsSerum pool was prepared using leftover samples after routine laboratory blood testing. Serum pool was then spiked with dissolved bilirubin stock. Measurements were performed on all four locations at the same time. All measurements were done in duplicate. Mean value was calculated as: (value1 + value2)/2. Those values were multiplied by corresponding dilution factors obtained during the preparation of icteric samples. For each icteric sample (Ix), bias against native (I0) sample was calculated as ((value Ix– valueI0)/ valueI0) × 100 %. Bias was calculated with actual average values. Obtained bias values were compared against acceptance criteria according to External quality assurance (EQA) providers. Difference in bilirubin concentration across platforms was tested using Friedman ANOVA. P values < 0.05 were considered statistically significant. Data are collected and analyzed in MS Excel 2016 (Microsoft, Redmond, Washington) and MedCalc® Statistical Software version 20.015 (MedCalc Software Ltd, Ostend, Belgium). ResultsMany of the tested parameters demonstrated low sensitivity to icterus interference. The highest sensitivity to icterus was observed for triglycerides, cholesterol, and urate. ConclusionsOur results indicate that while some common icteric interferences were consistent across all tested platforms, others were specific to the analyzer used, even when employing the same analytical methods.

B-023 Workflow evaluation of the new Siemens Atellica CI Analyzer

Abstract Background The Atellica® CI Analyzer, Model 1900 is an integrated clinical chemistry and immunoassay analyzer that has the same user interface, general workflow, and planned test menu to that of the larger Atellica® Solution. Atellica CI Analyzer also shares the same reagents and consumables as the Atellica Solution. The purpose of this study was to evaluate workflow performance of the Atellica CI Analyzer in a routine-like clinical laboratory setting. Methods The study was conducted at four international laboratory facilities over a four-month period. Basic throughput of the Atellica CI Analyzer was evaluated at each site by testing quality control (QC) and remnant anonymized patient samples using an 8:2 mixture of chemistry (ion selective electrolytes [IMT], clinical chemistry analytes) and immunochemistry assays. Additionally, the impact of interrupting routine testing for STAT high-sensitivity cardiac troponin samples was also evaluated. Daily, weekly, and monthly maintenance activities were observed, and data collected through direct observations, time and motion studies, and system run logs. Results The Atellica CI Analyzer aspirated patient samples in as little as two minutes (mins) with the time to first result for the mixed assay worklist ranging from 1 to 12 mins from the time of first barcode read. Across the four sites, the time to last result for the mixed assay worklist (average n=495 test results per site) ranged from 52 to 65 mins. The average turn around time (TAT) for high-sensitivity troponin was 9 mins from barcode read. Daily maintenance and QC activities for the Atellica CI Analyzer are comprised of both automated and manual activities. The average daily maintenance hands-on time was 3.6 min, ranging from 1.6 to 6.4 mins across the four sites. Daily QC preparation required &amp;lt;5 mins of hands-on time. The total average hands-on time to complete weekly maintenance for the Atellica CI Analyzer was 7.5 min. Conclusions The Atellica CI Analyzer demonstrated consistent system performance across all four study sites. Implementation of the Atellica CI Analyzer has the potential to increase laboratory efficiency given the minimal hands-on time required for daily and weekly maintenance activities and short TATs.

A-097 Body Fluid Validation for 11 Analytes on a Chemistry Analyzer

Abstract Background Most clinical chemistry assays are intended for use in serum, plasma, urine, and/or CSF with no performance characteristics on body fluids. However, testing of body fluids can be medically necessary for certain diagnoses. Such as determining the cause of fluid buildup in peritoneal spaces, pleural effusions, whether effusions are transudates or exudates, and even diagnosing some joint disorders. To provide these medically necessary tests, the burden is on the laboratory to perform regulatory-required validations. Herein is the validation of 11 clinical chemistry analytes in several common body fluids. Methods Mixing and stability studies were performed for the following analytes on the Roche cobas c702 (Roche Diagnostics), using the methods intended for use with serum/plasma samples: Albumin, Amylase, Bilirubin, Urea Nitrogen, Cholesterol, Creatinine, Glucose, Lactate Dehydrogenase, Lipase, Protein, and Triglycerides. Six individual fluid samples were mixed with the intended use matrix in 3 ratios: 1:3, 1:1, and 3:1. The following matrices were evaluated: pleural, peritoneal, JP drainage, synovial, and pancreatic cyst fluids. Stabilities were tested for 7 days at room temperature (RT) and 2-8°C. A maximum dilution study was also performed for Amylase. Results Mixing Study: The results are shown in the table below: Stability: For most body fluids, analytes were stable for 7 days at RT and 2-8 °C. The following analytes displayed shorter stability at RT: Bilirubin (peritoneal: 2 days, pleural: 1 day, JP drainage: 1 day), Urea Nitrogen (JP drain fluid: 2 days), Lipase (peritoneal fluid 5 days), and Triglycerides (peritoneal fluid: 5 days). Lactate dehydrogenase in pleural fluid showed a stability of 5 days at RT and 2 days at 2-8 °C. Maximum Dilution A 1:505 compounded manual and automated dilution displayed a &amp;lt;7% bias. Conclusions No sample matrix interferences were observed for any of the body fluids-analyte pairs tested allowing for this medically necessary testing.

Comparison of two different technologies measuring the same analytes in view of the In Vitro Diagnostica Regulation (IVDR)

Abstract Objectives This study systematically compared the performance and comparability of two medical laboratory analytical instruments, the conventional wet chemistry analyzer (cobas) and the dry slide technology (Vitros), across various clinical chemistry assays. Methods The evaluation focused on assessing imprecision, inaccuracy, recovery, and method comparison using leftover patient serum samples. Results The results indicated good to very good agreement for most clinical chemistry analytes, with larger differences observed for comparison of serum patient samples on albumin and protein. Conclusions Understanding and acknowledging method-specific variations, are crucial for accurate result interpretation in clinical laboratories. This study contributes valuable insights to ongoing discussions on method standardization.

Association of Serum Metabolites with Serum Indices and Preanalytical Factors of Biobanked Serum Samples.

Background: Serum indices (hemolysis, icterus, and lipemia; HIL) are known to impact clinical chemistry assay results. This study aimed to investigate the impact of HIL indices on serum metabolite profiles and the association of serum metabolite levels with pre-analytical factors of serum samples. Methods: A cohort of serum samples (n = 12,196) from the Korean Genome and Epidemiology Study (KoGES) was analyzed for HIL indices and the pre-analytical variables (SPRECs) which were generated in the process of serum collection. We further performed targeted metabolomics on a subset comprising hemolyzed (n = 60), icteric (n = 60), lipemic (n = 60) groups, and a common control group of non-HIL samples (n = 60) using the Absolute IDQ p180 kit. Results: We found 22 clinical chemistry analytes significantly associated with hemolysis, 25 with icterus, and 24 with lipemia (p < 0.0001). Serum metabolites (n = 27) were associated with all of hemolysis, icterus, and lipemia (p < 0.05). The PC ae C36 2 had exhibited a significant association with pre-analytical factors corresponding to the third (pre-centrifugation delay between processing) and sixth (post-centrifugation) elements of the SPREC. Conclusions: This study showed the association of the serum index and pre-analytical factors with serum metabolite profiles. In addition, the association of pre-analytical factors with serum metabolite concentrations would corroborate the utility of SPRECs for the quality control of biobanked serum samples.

Measurement uncertainty in clinical chemistry: ISO 20914 versus nordtest or intermediate precision versus bias

Aim Measuring uncertainty (MU) is crucial to ensure the accuracy and precision of laboratory results. This study compares the ISO 20914 and Nordtest guidelines to analyze the MU values for 20 clinical chemistry analytes over six months. Methods The researchers calculated MU components, including within-laboratory reproducibility (Rw), laboratory analytical performance bias (u(bias)), and combined standard uncertainty (u c), based on internal quality control and external quality assessment data. The final expanded uncertainty (U) values were determined by multiplying the combined uncertainty with a coverage factor (k = 2 for 95% Confidence Interval), following each guideline’s respective procedures. Clinical chemistry analytes were analyzed on Roche Cobas 6000 c501 auto analyzer (Roche Diagnostics, Mannheim, Germany) and manufacturer’s kits were used analysis. Results The results show that 11 out of 20 clinical chemistry analytes met the targeted maximum allowable measurement uncertainty (MAU) values when calculated according to ISO 20914 guideline. Also, 11 out of 20 clinical chemistry analytes’ MU values met the MAU values with the Nordtest guideline’s recommended calculations. However, some tests met the MAU in the ISO 20914 approach but not in the Nordtest guideline, and vice versa. Conclusions The study found that intermediate precision (u Rw) in the ISO 20914 approach and performance bias (u(bias)) in the Nordtest approach significantly impacted MU values. The research highlights the importance of standardization in MU calculation approaches across clinical laboratories. These findings have implications for patient care and clinical decision-making, emphasizing the importance of selecting appropriate laboratory guidelines for routine use.

End-user verification results of two serum separator tubes for clinical chemistry analytes according to CLSI GP34-A and CLSI GP41-A6

Tube manufacturers use different composition of gels and blood clot activator formulations in serum tube production. Our aim was to investigate the within-tube (repeatability) and between-tube variation, concordance between comparison results of BD and VacuSEL tubes. Blood samples were collected from control subjects (n = 20) and patients (n = 30) in accordance with the CLSI GP41-A6 and CLSI GP34-A guidelines. Twenty-three clinical chemistry parameters were analysed via Roche Cobas C702 Chemistry Analyzer on T0 (0 hour) and T24 (24 hour). Mean differences % were compared with Wilcoxon matched pair test. Clinical significance was evaluated based on desirable bias according to total allowable error (TEa). VacuSEL tubes demonstrated acceptable performance for the results of 20 parameters with regards to desirable bias % limits. Lactate dehydrogenase (LD) [mean difference % (%95 confidence intervals (CI) values of BD and VacuSEL tubes at T0 [6.41% (4.80–8.01%)]; sodium (Na) and total protein (TP) at T24 [-0.27% (-0.46 to −0.07%) and −1.39% (-1.87 to −0.91), respectively] were over the desirable bias limits (LD: 4.3%, Na: 0.23% and TP: 1.36%, respectively) but not exceeding total biological variation CV % [Na: 0.5 (0.0–1.0) % and TP: 2.6 (2.3–2.7) %). %95 confidence intervals (CI) of T0 LD values overlap with within-subject biological variation % (CI) limits (LD: 5.2 (4.9–5.4) %). The differences between two tubes were not medically significant and necessarily conclusive. VacuSEL serum tubes presented comparable performance with BD serum tubes.

Evaluation of measurement uncertainty for 19 clinical chemistry analytes and comparison with analytical performance specifications

Evaluation of measurement uncertainty for 19 clinical chemistry analytes and comparison with analytical performance specifications

Analysis of plasma metabolomes from 11 309 subjects in five population-based cohorts

Plasma metabolomics holds potential for precision medicine, but limited information is available to compare the performance of such methods across multiple cohorts. We compared plasma metabolite profiles after an overnight fast in 11,309 participants of five population-based Swedish cohorts (50–80 years, 52% women). Metabolite profiles were uniformly generated at a core laboratory (Metabolon Inc.) with untargeted liquid chromatography mass spectrometry and a comprehensive reference library. Analysis of a second sample obtained one year later was conducted in a subset. Of 1629 detected metabolites, 1074 (66%) were detected in all cohorts while only 10% were unique to one cohort, most of which were xenobiotics or uncharacterized. The major classes were lipids (28%), xenobiotics (22%), amino acids (14%), and uncharacterized (19%). The most abundant plasma metabolome components were the major dietary fatty acids and amino acids, glucose, lactate and creatinine. Most metabolites displayed a log-normal distribution. Temporal variability was generally similar to clinical chemistry analytes but more pronounced for xenobiotics. Extensive metabolite-metabolite correlations were observed but mainly restricted to within each class. Metabolites were broadly associated with clinical factors, particularly body mass index, sex and renal function. Collectively, our findings inform the conduct and interpretation of metabolite association and precision medicine studies.

Clinical Chemistry and Haematology Values of a Captive Population of Humboldt Penguins (Spheniscus humboldti).

Reference intervals for clinical chemistry and haematology analytes are essential for the interpretation of blood results, but limited data are available for Humboldt penguins (Spheniscus humboldti) in zoological collections as well as in the wild. The aim of the present study was therefore to establish reference intervals for a series of clinical chemistry and haematology analytes in a single zoological population of Humboldt penguins. Furthermore, possible variations of the analytes influenced by the age and sex of the individuals were investigated. Lithium heparinized plasma and whole blood samples from 39 animals were analysed and statistically evaluated. The following clinical chemistry analytes were significantly higher in females: glutamate dehydrogenase (p = 0.0003), alanine transaminase (p = 0.0005), alkaline phosphatase (p = 0.0245), aspartate aminotransferase (p = 0.0043), creatine kinase (p = 0.0016), lactate dehydrogenase (p < 0.0001), inorganic phosphorus (p = 0.0223), and sodium (p = 0.0415). No significant differences between males and females were found for any haematology analyte. Cholesterol (p = 0.0233; -0.39), white blood cell count (p = 0.0208; -0.40), and absolute heterophiles (p = 0.0148; -0.42) had significant negative correlations with the age of the penguins. The results indicate that the factors of sex and age should be considered when interpreting the results of haematological and clinical biochemistry assays, and further studies are needed to determine whether there are more differences in other age groups or seasons.

Biological Variation in Biochemistry Analytes in Laboratory Guinea Pigs (Cavia porcellus).

Biological variation (BV) describes the physiological random fluctuation around a homeostatic set point, which is a characteristic of all blood measurands (analytes). That variation may impact the clinical relevance of the changes that are observed in the serial results for an individual. Biological variation is represented mathematically by the coefficient of variation (CV) and occurs within each individual (CVI) and between individuals in a population (CVG). Biological variation data can be used to assess whether population-based reference or subject-based reference intervals should be used for the interpretation of laboratory results through the calculation of the index of individuality (IoI). This study aimed to determine the biological variations, calculate the IoI and reference change values (RCV) of clinical chemistry analytes in an outbred strain colony of Hartley guinea pigs (GPs), and set the quality specifications for clinical chemistry analytes. Blood was collected from 16 healthy adult laboratory colony GPs via jugular venipuncture at weekly intervals over six weeks. All the samples were frozen and analyzed in a single run. Analytical, CVI, and CVG biological variations, together with the IoI and RCV, were calculated for each measurand. Based on the estimated BV, the calculated IoI was low for glucose, so individual reference intervals (RCV) should be used. The majority of the measurands should be interpreted using both population-based and subject-based reference intervals as the IoIs were intermediate.

A-380 Concordance of Truvian's Point-of-Care Blood Testing Analyzer to Central Laboratory Testing

Abstract Background Truvian has developed a fully automated blood testing analyzer (TruAnalyzer) capable of running a panel of clinical chemistry, immunoassay and hematology assays from a low-volume blood sample. Truvian's compact instrument was designed to deliver a convenient and affordable alternative to off-site traditional labs, providing lab-accurate rapid insights from a single, small sample of blood with no upfront pre-processing required to inform health decisions at the point of care. Here, we evaluate the precision and accuracy of a comprehensive wellness panel tested on the TruAnalyzer and compare its performance to FDA cleared devices. Methods TruAnalyzer performance was assessed for both precision and accuracy. For the precision studies, two commercially available controls (one for plasma-based assays and one for blood-based assays) were added to the Truvian consumables and loaded into the TruAnalyzer. Six replicates were run on each of the ten instruments (N = 60). Coefficient of variance (CV) analysis was performed and compared against pre-established requirements. For method comparison studies, 50 unique donors were consented and enrolled in an IRB approved study and 68 donor samples were collected over a 3-day period. For each donor, three 4-mL tubes of blood were drawn: 1) a Lithium Heparin (LiHep) sample for the TruAnalyzer clinical chemistry and hematology panel, 2) a LiHep gel tube for the Roche Cobas c311 clinical chemistry assays, and 3) an EDTA sample for the Sysmex PocH-100i complete blood count with 3-part differential. All samples were processed within 1 hour of collection. Using only 300 µL sample volume, the TruAnalyzer reported results for 23 analytes including CMP (no electrolyes), Lipid, HbA1c, and CBC with 3-part differential. Concordance to comparator devices was determined by Bland-Altman analysis. Results For the precision study, 13 out of 14 (93%) chemistry analytes had a between-system CV of ≤5.8%. For hematology, 5 out of 5 (100%) analytes had a between-system CV of ≤6%. For the method comparison study, RBC counts showed 98% of samples tested being within 6% of comparator values. WBC and platelet counts had 97% of samples tested being within 10% and 15% of comparator values respectively. 95% of hematocrit samples were within 6% of comparator values and 98% of hemoglobin samples were within 7% of comparator values. All hematology analytes showed equivalency to Sysmex PocH-100i as determined by Bland-Altman equivalency analysis. For clinical chemistry analytes, 14 out of 15 (93%) analytes showed equivalency to Roche Cobas c311 as determined by Bland-Altman equivalency analysis when CLIA ATE guidelines were applied. 7 out of 15 clinical chemistry analytes had ≥90% of results within ATE. 5 out of 15 clinical chemistry analytes had ≥80% of results within ATE. Conclusion The Truvian blood testing analyzer is the first multi-modality system capable of running hematology, clinical chemistry and immunoassays from a single, low-volume blood sample. Results from this study demonstrate good precision for the majority of panel analytes and excellent concordance to FDA cleared comparators for assays evaluated in this study.

Derivation of sex and age-specific reference intervals for clinical chemistry analytes in healthy Ghanaian adults

Derivation of sex and age-specific reference intervals for clinical chemistry analytes in healthy Ghanaian adults

Influence of reduced centrifugation time on clinical chemistry analytes and literature review

Abstract Objectives Centrifugation is a time-consuming step which increases the turnaround time (TAT) in laboratories. A few studies have addressed the effect of altering centrifugation settings on analytical quality for clinical chemistry analytes, and most of these studies have used collection tubes with gel separators. However, gel separator tubes may be unsuitable for some laboratories because they are slightly more expensive than tubes without gel separators and are not appropriate for some special tests. The aim of this study was to investigate the effect of centrifugation conditions on clinical chemistry analytes. Methods We compared centrifugation times of 7 min at 2,200×g and 5 min at 2,750×g with the manufacturer’s protocol of 10 min at 1,300×g as the reference condition. Twenty general chemistry analytes were studied in lithium heparin plasma tubes without gel separators. Results For all analytes except carbon dioxide (CO2), no significant differences in analyte results were observed when the centrifugation time was reduced. Deming regression and Bland–Altman plots demonstrated an acceptable clinical concordance within the limits of total allowable error for all analytes between the two rapid centrifugation conditions with the reference centrifugation condition. Conclusions Our results confirmed that alternate centrifugation conditions for either 7 min at 2,200×g or 5 min at 2,750×g of samples collected in lithium heparin tubes without gel are acceptable for clinical chemistry analytes. Our data support using centrifugation at higher speeds for shorter times to improve TAT without altering the quality of the analytical results.

Reduced Fasting Duration in Cynomolgus Monkeys Enhances Animal Welfare During Toxicology Studies.

During toxicology studies, fasting animals prior to clinical pathology blood collection is believed to reduce variability in some clinical chemistry analytes. However, fasting adds stress to animals that are already stressed from the administration of potentially toxic doses of the test article. The purpose of this study was to assess the impacts of different fasting durations on cynomolgus monkeys' welfare during toxicology studies. To this end, we assessed the cynomolgus monkeys traditional and ancillary clinical pathology endpoints at different fasting times. We showed that most clinical pathology endpoints were largely comparable between different fasting times suggesting that cynomolgus monkeys could be fasted for as little as 4 hours for toxicology studies, as longer fasting times (up to 20 hours) resulted in stress, dehydration, and significant decreases in blood glucose- changes that impacts animal welfare. Shorter fasting times were associated with higher triglycerides variability among individual animals. Therefore, we propose that shorter fasting time (i.e., 4 hours) should be adequate for most toxicology studies except when: (1) parameters that could be affected by non-fasting conditions are important for safety and pharmacodynamic assessments (i.e., glucose and lipids) and (2) fasting would be needed for the bioavailability of an orally administered test article.

Performance evaluation of cobas pure integrated solutions at multiple sites in Europe and Asia

Abstract Objectives We evaluated analytical performance, functionality, reliability, and comparability of cobas® pure integrated solutions (Roche Diagnostics) under routine-like conditions. Methods The study was conducted in Europe and Asia (five sites). Seventy-six applications covering ion selective electrolytes (ISE), clinical chemistry (CC), and immunochemistry (IC) analytes were assessed using Elecsys® immunochemistry assays (Roche Diagnostics). Samples included control material and pseudonymized residual samples (plasma/serum/urine). Analytical performance, functionality, and system reliability were evaluated. An inter-laboratory survey, routine simulation imprecision (RSI) and method comparison experiments, and dedicated workflow runs were conducted. Results Most coefficients of variation (CVs) for repeatability were &lt;1 % for ISE, ≤2 % for CC, and &lt;2.5 % for IC assays; for intermediate precision were ≤2 % for ISE and CC, and &lt;2.5 % for IC assays; and for reproducibility were ≤3 % for ISE and CC, and &lt;2.5 % for IC assays. Most RSI reference (94 %) and random part (93 %) CVs were ≤2 %; 99 % of runs completed without system-related interruption. 218 method comparisons generated median Passing–Bablok slope of 1.00, median bias at the medical decision point of −0.1 %, and median Pearson’s r of 0.998. Conclusions cobas pure integrated solutions demonstrated precise and accurate results under routine-like conditions and comparable results vs. commercial analyzers, supporting implementation into routine practice.

Biological Variations of Seven Clinical Chemistry Analytes and Trolox Equivalent Antioxidant Capacity within Salivary Constituents.

Data on biological variation in saliva samples are quite limited. This study aimed to obtain well-defined biological variation data for seven common clinical chemistry analytes and Trolox equivalent antioxidant capacity (TEAC) in saliva. Unstimulated whole saliva and blood samples were collected from thirty-two healthy volunteers of both genders without any history of disease or metabolic syndrome under standard conditions at six different times within three weeks. The seven clinical chemistry analytes and TEAC, analyzed by photometric methods using automated analyzers, were planned for biological variation analysis. The components of nested analysis of variance were used to perform the biological variation data analysis. The within-subject and between-subject biological variations (CVG and CVI, respectively) for unstimulated whole saliva samples, respectively, were determined to be 19.3% and 25.1% for α-amylase, 25.1% and 51.1% for aspartate aminotransferase, 31.0% and 22.3% for lactate dehydrogenase, 19.0% and 20.8% for uric acid, 16.6% and 23.4% for total calcium, 12.9% and 13.7% for inorganic phosphate, 13.1% and 19.7% for total protein, and 14.9% and 20.0% for TEAC. In addition, the CVI and CVG were 3.4% and 6.3% for serum TEAC. Considering the evidence that saliva samples can be used to diagnose and monitor oral or non-oral diseases, these biological variation data will contribute to how to use subject-based reference values or population-based reference intervals of these analytes and TEAC.

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

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

Calculation of measurement uncertainty of 20 Clinical Chemistry Analytes according to the practical ISO approach

Purpose: Measurement Uncertainty (MU) is a valuable tool for evaluating analytical performance and interpreting results in clinical laboratories. The International Organization for Standardization (ISO) has proposed a practical approach for MU calculation in its ISO/TS 20914:2019 guide. This study aimed to calculate the MU values of 20 clinical chemistry analyses per the ISO guideline and compare them with the Maximum expanded allowable measurement uncertainty (MAU) values.&#x0D; Methods: The study was performed using 6-month internal quality control (IQC) values (uRw) and calibrator uncertainty (ucal) in line with the recommendations of the ISO/TS 20914:2019 guideline. The common MU value was calculated for 20 clinical chemistry tests on two identical devices, Roche Cobas 6000 c501 (Roche Diagnostics, Mannheim, Germany) analyzers. The calculated MU values for the tests were compared with the current MAU values in the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Biological Variation database (the current Clinical Laboratory Improvement Amendments/CLIA recommendation for Ethanol has been selected). &#x0D; Results: MU values for Alanine aminotransferase, C-reactive Protein, Iron, Ethanol, Total Bilirubin, Triglyceride, and Blood urea nitrogen remained within the MAU limits. The MU values of the other 13 tests (excluding Aspartate aminotransferase, Glucose, and Potassium Level 2 IQC) exceeded the MAU values. &#x0D; Conclusion: It was observed that the uRw value affected the MU value the most. Close monitoring and evaluation of uRw and thus IQC and implementation of corrective and preventive actions may reduce MU.

Evaluating Interference of Lipemia on Routine Clinical Biochemical Tests.

Objective Lipemia is an important cause of preanalytical errors in laboratory results. They affect the specimen integrity and trustworthiness of laboratory results. The present study was to assess the impact of lipemia on routine clinical chemistry analytes. Methods Anonymous leftover serum samples with normal levels of routine biochemical parameters were pooled. Twenty such pooled serum samples were used for the study. The samples were spiked with commercially available intralipid solution (20%) to produce lipemic concentrations of 0, 400 (mild, 20 μL), 1,000 (moderate, 50 μL), and 2,000 mg/dL (severe, 100 μL). Glucose, renal function test, electrolytes, and liver function test were estimated in all the samples. Baseline data without the effect of interference was considered as true value and percentage bias for the spiked samples was calculated. Interference was considered significant if the interference bias percentage exceeded 10%. Result Parameters like glucose, urea, creatinine, direct bilirubin, sodium, potassium, and chloride showed negative interference at mild and moderate lipemic concentration and positive interference at severe lipemic concentration. Parameters like aspartate transaminase (AST) and alanine transaminase (ALT) showed negative interference at mild and positive interference at moderate and severe lipemic concentration. Whereas uric acid, total protein, albumin, total bilirubin, alkaline phosphatase, gamma-glutamyl transferase, calcium, magnesium, and phosphorous showed positive interference at all concentrations. Significant interference (> 10%) was shown for magnesium (mild lipemia), albumin, direct bilirubin, ALT, and AST at moderate lipemic concentration. All parameters showed significant interference at severe lipemic concentration. Conclusion All the study parameters are affected by lipemic interference at varying levels. Laboratory-specific data regarding lipemic interference at various concentrations on the clinical biochemistry parameters is needed.