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CLSI-based verification and de novo establishment of reference intervals for common biochemical assays in Croatian newborns.

This study aimed to examine whether the Canadian Laboratory Initiative on Pediatric Reference Intervals (CALIPER) reference intervals for 19 commonly used biochemical assays (potassium, sodium, chloride, calcium, magnesium, inorganic phosphorous, glucose, urea, creatinine, direct and total bilirubin, C-reactive protein (CRP), total protein, albumin, aspartate aminotransferase (AST), alanine aminotransferase (ALT), gamma-glutamyl transferase (GGT), alkaline phosphatase (ALP) and lactate dehydrogenase (LD)) could be applied to the newborn population of one Croatian clinical hospital. Reference interval verification was performed according to the CLSI EP28-A3c guidelines. Samples of healthy newborns were selected using the direct a posteriori sampling method and analyzed on the Beckman Coulter AU680 biochemical analyzer. If verification wasn't satisfactory, further procedure included de novo determination of own reference intervals by analyzing 120 samples of healthy newborns. After the first set of measurements, 14/19 tested reference intervals were adopted for use: calcium, inorganic phosphorous, glucose, urea, creatinine, total bilirubin, CRP, total protein, albumin, AST, ALT, GGT, ALP and LD. A second set of samples was tested for 5 analytes: potassium, sodium, chloride, magnesium and direct bilirubin. The verification results of the additional samples for sodium and chloride were satisfactory, while the results for potassium, magnesium and direct bilirubin remained unsatisfactory and new reference intervals were determined. The CALIPER reference intervals can be implemented into routine laboratory and clinical practice for the tested newborn population for most of the analyzed assays, while own reference intervals for potassium, magnesium and direct bilirubin have been determined.

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Comparison of lipemia interference created with native lipemic material and intravenous lipid emulsion in emergency laboratory tests.

This study aimed to investigate the effects of lipemia on clinical chemistry and coagulation parameters in native ultralipemic (NULM) and intravenous lipid emulsion (IVLE) spiked samples. The evaluation of biochemistry (photometric, ion-selective electrode, immunoturbidimetric method), cardiac (electrochemiluminescence immunoassay method) and coagulation (the viscosity-based mechanical method for prothrombin time (PT), activated partial thromboplastin time (APTT), fibrinogen and the immunoturbidimetric method for D-dimer) parameters were conducted. In addition to the main pools, five pools were prepared for both types of lipemia, each with triglyceride (TG) concentrations of approximately 2.8, 5.7, 11.3, 17.0 and 22.6 mmol/L. All parameters' mean differences (MD%) were presented as interferographs and compared with the desirable specification for the inaccuracy (bias%). Data were also evaluated by repeated measures of ANOVA. Prothrombin time and APTT showed no clinically relevant interference in IVLE-added pools but were negatively affected in NULM pools(P < 0.001 in both parameters). For biochemistry, the most striking difference was seen for CRP; it is up to 134 MD% value with NULM (P < 0.001) at the highest TG concentration, whereas it was up to - 2.49 MD% value with IVLE (P = 0.009). Albumin was affected negatively upward of 5.7 mmol/L TG with IVLE, while there was no effect for NULM. Creatinine displayed significant positive interferences with NULM starting at the lowest TG concentration (P = 0.028). There was no clinically relevant interference in cardiac markers for both lipemia types. Significant differences were scrutinized in interference patterns of lipemia types, emphasizing the need for careful consideration of lipemia interferences in clinical laboratories. It is crucial to note that lipid emulsions inadequately replicate lipemic samples.

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Quality assurance of add-on testing in plasma samples: stability limit for 29 biochemical analytes.

Clinical laboratories should guarantee sample stability in specific storage conditions for further analysis. The aim of this study is to evaluate the stability of plasma samples under refrigeration for 29 common biochemical analytes usually ordered within an emergency context, in order to determine the maximum allowable period for conducting add-on testing. A total of 20 patient samples were collected in lithium heparin tubes without gel separator. All analyses were performed using Alinity systems (Abbott Laboratories, Abbott Park, USA) and samples were stored at 2-8 °C. Measurements were conducted in primary plasma tubes at specific time points up to 48 hours, with an additional stability study in plasma aliquots extending the time storage up to 96 hours. The stability limit was estimated considering the total limit of change criteria. Of the 29 studied parameters, 24 demonstrated stabilities within a 48-hour storage period in primary plasma tubes. However, five analytes: aspartate aminotransferase, glucose, lactate dehydrogenase, inorganic phosphate and potassium evidenced instability at different time points (7.9 hours, 2.7 hours, 2.9 hours, 6.2 hours and 4.7 hours, respectively). The stability study in plasma aliquots showed that all parameters remained stable for 96 hours, except lactate dehydrogenase, with a stability limit of 63 hours. A reduced stability of primary plasma samples was observed for five common biochemical analytes ordered in an emergency context. To ensure the quality of add-on testing for these samples, plasma aliquots provide stability for a longer period.

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Common P-glycoprotein (ABCB1) polymorphisms do not seem to be associated with the risk of rivaroxaban-related bleeding events: Preliminary data.

Considering conflicting previous reports, we aimed to evaluate whether the common ABCB1 polymorphisms (rs1128503, rs2032582, rs1045642, rs4148738) affected the risk of bleeding in rivaroxaban-treated patients. We report preliminary data from a larger nested case-control study. Consecutive adults started on rivaroxaban for any indication requiring > 6 months of treatment were followed-up to one year. Patients who experienced major or non-major clinically relevant bleeding during the initial 6 months were considered cases, whereas subjects free of bleeding over > 6 months were controls. The polymorphisms of interest (rs1128503, rs2032582, rs1045642, rs4148738) were in a strong linkage disequilibrium, hence patients were classified regarding the "load" of variant alleles: 0-2, 3-5 or 6-8. The three subsets were balanced regarding a range of demographic, comorbidity, comedication and genetic characteristics. A logistic model was fitted to probability of bleeding. There were 60 cases and 220 controls. Raw proportions of cases were similar across the subsets with increasing number of ABCB1 variant alleles (0-2, N = 85; 3-6, N = 133; 6-8, N = 62): 22.4%, 21.8%, and 19.4%, respectively. Fully adjusted probabilities of bleeding were also similar across the subsets: 22.9%, 27.5% and 17.7%, respectively. No trend was observed (linear, t = -0.63, df = 273, P = 0.529; quadratic, t = -1.10, df = 273, P = 0.272). Of the 15 identified haplotypes, the completely variant (c.1236T_c.2677T(A)_c.3435T_c.2482-2236A) (40.7%) and completely wild-type (C_G_C_G) (39.5%) haplotypes prevailed, and had a closely similar prevalence of cases: 21.1% vs. 23.1%, respectively. The evaluated common ABCB1 polymorphisms do not seem to affect the risk of early bleeding in patients started on rivaroxaban.

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A survey on the practice of phlebotomy in Lithuania and adherence to the EFLM-COLABIOCLI recommendations: continuous training and clear standard operating procedures as tools for better quality.

The aim of this study was to determine the level of compliance of venous blood sampling (VBS) in Lithuania with the joint recommendations of the European Federation of Clinical Chemistry and Laboratory Medicine and the Latin American Confederation of Clinical Biochemistry (EFLM-COLABIOCLI) and to analyse possible causes of errors. A survey was conducted between April and September 2022. A self-designed questionnaire was distributed to the Lithuanian National Societies. Error frequencies and compliance score were computed. Differences between groups were analysed using Pearson's chi-square, Fisher's exact criterion, Mann-Whitney U (for two groups), or Kruskal-Wallis (for more than two groups) for categorical and discrete indicators. The association between ordinal and discrete variables was assessed using Spearman's rank correlation coefficient. Statistical significance was determined at P < 0.05. A total of 272 respondents completed the questionnaire. Median error rate and compliance score were 31.5% and 13/19, respectively. Significant differences were found among professional titles, standard operating procedures availability, training recency, and tourniquet purpose opinions. A negative correlation was noted between compliance and time since training (rs = - 0.28, P < 0.001). The findings of this study indicate that there is a significant need for improvement in compliance with the EFLM-COLABIOCLI recommendations on VBS among specialists in Lithuania. Essential measures include prioritizing ongoing phlebotomy training and establishing national guidelines. Harmonisation of blood collection practices across healthcare institutions is crucial.

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Prediction interval: A powerful statistical tool for monitoring patients and analytical systems.

Monitoring is indispensable for assessing disease prognosis and evaluating the effectiveness of treatment strategies, both of which rely on serial measurements of patients' data. It also plays a critical role in maintaining the stability of analytical systems, which is achieved through serial measurements of quality control samples. Accurate monitoring can be achieved through data collection, following a strict preanalytical and analytical protocol, and the application of a suitable statistical method. In a stable process, future observations can be predicted based on historical data collected during periods when the process was deemed reliable. This can be evaluated using the statistical prediction interval. Statistically, prediction interval gives an "interval" based on historical data where future measurement results can be located with a specified probability such as 95%. Prediction interval consists of two primary components: (i) the set point and (ii) the total variation around the set point which determines the upper and lower limits of the interval. Both can be calculated using the repeated measurement results obtained from the process during its steady-state. In this paper, (i) the theoretical bases of prediction intervals were outlined, and (ii) its practical application was explained through examples, aiming to facilitate the implementation of prediction intervals in laboratory medicine routine practice, as a robust tool for monitoring patients' data and analytical systems.

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