Biochemia medica最新文献

Introduction: 25-hydroxyvitamin D (25-OH-D) is essential for calcium homeostasis and bone health, with increasing evidence suggesting associations with non-skeletal diseases. However, the lack of consensus on optimal concentrations and laboratory variability has led to clinical uncertainty and excessive testing. This study evaluates the impact of demand management strategies and revised cut-off points on test volumes, unperformed determinations, and cost savings.

Material and methods: A retrospective study (January 2015-May 2024) analyzed all 25-OH-D requests. Concentrations of 25-OH-D were measured using electrochemiluminescence assays on a Cobas C8000. An annual trend analysis of 25-OH-D test requests was performed to evaluate changes in demand. In 2018, vitamin D deficiency prevalence was assessed according to three cut-off values (75, 50 and 30 nmol/L). We assessed the impact of demand management rules, implemented in May 2022, to reduce unnecessary testing. The follow-up testing rate was calculated as the proportion of repeat tests within 12 months after determination.

Results: There was 25-OH-D testing increased from 10,830 in 2015 to nearly 85,000 in 2023. Demand management strategies led to 12,406 rejections in 2022 (from May onwards), 16,809 in 2023, and 7566 in 2024 (until May), saving €85,600. Follow-up testing rates dropped from ~15% before 2022 to ~5% afterward. Lowering the deficiency threshold from 75 to 50 nmol/L reduced deficiency diagnoses from > 70% to < 50%; at 30 nmol/L, rates could drop to ~10-11%.

Conclusions: Demand management strategies effectively reduce unnecessary testing and healthcare costs. Establishing appropriate reference values prevents overestimation of vitamin D deficiency, optimizing clinical and economic outcomes.

Introduction: Serum neuron specific enolase (NSE) is used as neuroendocrine tumor and central nervous system damage marker. It is present in variable concentrations in erythrocytes and hemolysis interferes in serum NSE quantification. Our aim was to develop a correction formula for moderate hemolysis, based on repeated patient samples instead of artificial sample doping with hemolysates.

Materials and methods: We searched in laboratory informatics system for patients with sample pairs obtained within 24 h, for NSE quantification. We registered NSE and hemolytic index (NSE1 and HI1) from the first moderate hemolyzed sample (HI: 15-80), and from the second non-hemolyzed sample obtained afterwards (NSE2 and HI2). In a development cohort (N = 41), we obtained the formula NSE_calc = NSE1 - (0.354 x (HI1 - HI2)) - 0.162, which was later used in the validation cohort (N = 26) to calculate NSE corrected concentrations (NSE_calc).

Results: Concentrations of NSE2 differed from NSE1 (P = < 0.001) but not from NSE_calc (P = 0.291). In 84% samples, NSE1 had a relative bias from NSE that exceeded the 14% limit of total error allowable, with a median relative bias of 22.5%. Meanwhile, the bias between NSE2 concentrations and NSE_calc was - 0.4 µg/L (95% confidence interval = - 3.8 to 4.5), the relative bias was 8.3% and only 23% of samples exceeded the 14% limit. Formula usefulness was limited to moderate hemolytic samples.

Conclusions: In summary, with this innovative approach, the NSE_calc bias is low enough to have clinical significance, so re-drawings of blood samples might be avoided. This approach also opens the possibility to correct the estimation of other magnitude concentrations affected by in vitro hemolysis.

Introduction: Defining trustworthy reference intervals (RIs) for serum folate (FOL) or serum cobalamin (VITB12) is a difficult task. The purpose of this study is to use an indirect approach from the laboratory information's system to indirectly generate RIs for FOL and VITB12.

Materials and methods: A retrospective observational study was performed at a tertiary-care laboratory's hospital during 12 months. All FOL and VITB12 tests were measured using a Cobas8000 e801 system (Roche Diagnostics, Mannheim, Germany). The RIs were calculated using a non-parametric approach. The RIs established in the present study were verified by calculating the fraction of RIs that fell outside the new RIs, in two validation cohorts sampled using the direct and indirect method.

Results: A total of 19,214 (FOL) and 27,420 (VITB12) results were obtained. The RIs were 4.5 nmol/L (90% confidence intervals (CI) 4.4-4.6) to 38.4 nmol/L (CI 38.3-38.5) for FOL and 140 pmol/L (CI 139-141) to 659 pmol/L (CI 657-660) for VITB12. The verification included 8,798 FOL results and 7,365 VITB12 results. For both magnitudes was acceptable since only 0.1% of FOL and 0.02% of VITB12 results fell outside the RIs. Finally, the RIs were verified using a direct method with twenty individuals. For FOL 20/20 cases and 19/20 of VITB12 cases fell within the estimated RIs.

Conclusions: In summary, the use of an indirect data approach has enabled us to calculate RIs for FOL and VITB12. The RIs obtained in our study are lower than those proposed by the manufacturer for both FOL and VITB12.

This case report investigates the occurrence of green discoloration in serum and citrate plasma samples collected from a male adult patient following a multivisceral organ transplant. In collected samples, it was necessary to investigate the influence of sample discoloration on the results of laboratory tests and to determine the appropriate approach to sample management. Hematology, coagulation and blood gas analysis showed no flags, but the biochemical lipemia index was susceptible to positive interference, necessitating dilution of the native sample. Despite the green discoloration, both native and diluted samples exhibited minimal interference on routine clinical chemistry analyses, demonstrating the reliability of the laboratory test results. This case report underscores the influence of preanalytical factors on the results of laboratory tests, the need for a thorough assessment of the sample adequacy for laboratory testing and the strict application of appropriate guidelines in the sample management in order to make an accurate diagnosis and ensure optimal patient care.

Autovalidation is a computerised postanalytical tool that uses a sequence of procedures to verify laboratory test results without manual intervention. The Working Group for Post-analytics of the Croatian Society for Medical Biochemistry and Laboratory Medicine has prepared procedures for the implementation of autovalidation in routine laboratory work, which complement the existing national recommendations and aim to clarify the procedures of autovalidation. Before implementation, it is necessary to determine the need for the introduction of autovalidation in routine laboratory work, and then appoint the autovalidation team, whose task is to decide in which area of laboratory work autovalidation should be introduced, create the algorithm and supervise the verification of autovalidation. Standard rules included in the algorithm are patient data, messages from the analyzer, values of interference indices, autovalidation range and delta check. All criteria defined in the autovalidation algorithm have to be documented and approved by the laboratory manager. This autovalidation procedure shows the basic rules of autovalidation that can be used by any laboratory in the initial phase. The justification for using autovalidation will depend on the number and complexity of laboratory tests, the size of the laboratory personnel, and the available financial and material resources. Autovalidation avoids the subjective evaluation of laboratory test results as it is based on the same rules and is standardised to a certain extent, which further increases the quality of laboratory test results.

Introduction: Subclinical hypothyroidism (SCH) is an independent risk factor for cardiovascular diseases due to endothelial dysfunction and atherosclerosis development. The aim of this study was to determine whether the levothyroxine therapy could impact the concentrations of endothelial dysfunction blood markers, namely endothelin-1 (ET-1), asymmetric dimethylarginine (ADMA) and endocan, in patients with a mild form of SCH (thyroid-stimulating hormone (TSH) ≤ 10 mIU/L).

Materials and methods: In this case-control prospective study, SCH patients and healthy controls were recruited during their regular health examinations. Medical specialists prescribed levothyroxine to SCH patients if necessary. The endothelial dysfunction markers, as well as other biochemical markers, were measured in all subjects at baseline, and after 6 months of levothyroxine treatment following the euthyroidism.

Results: Our study showed higher ADMA (248.00 (168.78-540.20) vs. 166.30 (140.60-243.40) μg/L, P = 0.002), endocan (114.30 (63.45-194.65) vs. 67.26 (50.80-126.10) ng/L, P = 0.004), low-density lipoprotein cholesterol (LDL) (3.3 ± 0.6 vs. 3.7 ± 0.9 mmol/L, P = 0.043) and non-high-density lipoprotein cholesterol (non-HDL) (3.8 ± 0.7 vs. 4.2 ± 1.0 mmol/L, P = 0.020) concentrations in patients with a mild form of SCH in comparison with healthy subjects. In SCH patients, after 6 months of levothyroxine treatment following the euthyroidism, we observed a significant decrease in endocan (91.47 (61.88-200.03) vs. 97.90 (55.18-154.88) ng/L, P = 0.004), and total cholesterol concentrations (CHOL) (6.2 ± 1.0 vs. 5.8 ± 1.0 mmol/L, P = 0.039).

Conclusions: A mild form of SCH is associated with higher concentrations of endocan, ADMA, LDL and non-HDL. The potential benefits of levothyroxine therapy were shown through the significant decrease of endocan and CHOL concentrations in SCH patients, thus contributing the atherosclerosis prevention.

Introduction: Research on delta check limits (DCLs) for hormones is limited, yet some laboratories apply arbitrary DCLs. We aimed to propose DCLs for commonly requested hormones.

Materials and methods: This study analyzed 59,657 paired results for adrenocorticotropic hormone (ACTH), cortisol, parathyroid hormone (PTH), prolactin, insulin, testosterone, and thyroglobulin from five Korean university hospitals. Delta check limits were established using the absolute delta difference (absDD) and absolute delta percent change (absDPC) with 5% cutoff for inpatients/emergencies (IE), outpatients (O) and both (combined; mean of them). Proportions outside the DCLs were compared across groups.

Results: Using absDD and absDPC, each group's DCLs showed 4.3% to 6.4% of values outside the DCLs, aligning with the 5% cutoff (excluding group IE for insulin, testosterone, and thyroglobulin due to < 1000 data pairs). Delta check limits of absDD differed between groups for ACTH, cortisol, PTH, and prolactin, while for absDPC, differences were seen only for ACTH and prolactin. Cross-validation revealed IE and O groups differed outside DCLs of absDD for ACTH, cortisol, and PTH, but only ACTH with absDPC. Combined DCLs of absDD showed ACTH and cortisol exceeded limits in 7.2% and 9.0% in IE, but only 2.6% and 0.6% in O. With absDPC, ACTH differed (10.4% in IE, 2.8% in O), while cortisol, PTH, and prolactin ranged from 4.0% to 6.1%.

Conclusions: Combined DCLs of absDPC are recommended for cortisol, PTH, and prolactin, while ACTH requires separate DCLs on clinical settings. These DCLs from real-world data provide a foundation for establishing DCLs of hormones in clinical laboratories.

Introduction: Knowledge and systematic evaluation of analytical errors is the task of internal analytical quality control management. The aim of this study was to assess whether the Westgard rules proposed by Bio-Rad's Westgard Advisor software are more efficient in the monitoring of analytical performance than those previously in use.

Materials and methods: The study was carried out on the nephelometer Atellica NEPH630 (Siemens Healthineers, Erlangen, Germany). Five parameters were chosen: serum immunoglobulin A (IgA), alpha 1 - antitrypsin (AAT), prealbumin, lipoprotein (a) (Lp(a)) and ceruloplasmin. The study was divided into 4 phases (A, B, C, D): phase A - old rules used (1_3s, R_4s and 2_2s); phase B - first introduction of new rules (30 days), (1_3s/2_2s for IgA; 1_3s/2_2s/R_4s/4_1s/10_x for the remaining parameters); Phase C - second intervention (after 60 days) 1_3s/2_2s/R_4s/4_1s for IgA and Lp(a), 1_3s/2_2s/R_4s/4_1s/8_x for prealbumin and ceruloplasmin and 1_3s/2_2s/R_4s/4_1s/10_x for AAT; and Phase D - values at the end of the study (1_3s for IgA, 1_3s/2_2s/3_2s/R_4s/3_1s/12_x for AAT and ceruloplasmin, 1_3s/2_2s/R_4s/4_1s/8_x for prealbumin and 1_3s/2_2s/R_4s/4_1s/10_x for Lp(a).

Results: At the end of the study the coefficient of variation (CV%), bias (%) and sigma for IgA were 2.55%, - 1.09% and 5.33, respectively; for AAT 3.88, - 2.21 and 3.25; for prealbumin 3.99, - 0.14 and 2.95; for Lp(a) 8.02, - 0.34 and 3.81; for ceruloplasmin 2.48, - 3.65 and 3.49.

Conclusions: By using newly suggested rejection rules, we did not observe an improvement in monitoring of analytical performance.

Introduction: This study compared analytical and technical performance of Atellica UAS 800 and UAS 60 and assessed potential patient risks if results were not reviewed by laboratory personnel.

Materials and methods: The study included 463 urine samples collected from February to March 2024, analyzed on both analyzers within 2 hours by two laboratory operators. Results from the UAS 800, recorded after operator review, were considered as the reference and compared to UAS 60 results obtained before and after review. Data were evaluated using weighted kappa (kappa ≥ 0.6 considered acceptable). Technical comparison was based on operator assessment. For risk analysis 23 errors and four severity levels were defined.

Results: After automatic image evaluation strong agreement was observed for calcium oxalate and yeasts (kappa: 0.83, 0.94), moderate agreement for red and white blood cells and epithelial cells (kappa: 0.75, 0.78, 0.75), weak agreement for bacteria, mucus and non-squamous epithelial cells (kappa: 0.57, 0.59, 0.40), and poorest agreement for hyaline and pathological casts and total crystals (kappa: 0.23, 0.07, 0.36). After review, kappa was acceptable for all parameters. Risk analysis identified 15 errors, with unrecognized total crystals and mucus being the most frequent (30.0%, 17.1%). Three errors were classified as intermediate risk (missing to report total crystal +1, mucus +1 and pathological casts ≥ +1), with none in high risk area. UAS 800 offers higher throughput and automatic sample aspiration, while UAS 60 uses manual aspiration.

Conclusions: Atellica UAS 60 provides results comparable to UAS 800, quality of reported results remaining uncompromised even without operator review. It is suitable for low- to mid-volume laboratories and can serve as a backup in larger laboratories.

Introduction: Biological variation (BV) data are necessary for interpretation of test results and assessment of analytical performance. We aimed to determine the BV estimates for thyroid stimulating hormone (TSH), free triiodothyronine (fT3) and free thyroxine(fT4) in healthy subjects in Turkey and compare them with the literature findings.

Materials and methods: A total of 21 Turkish healthy volunteers (12 males and 9 females) were included in the study. Blood samples were collected once a week for five weeks, and the analysis was performed using the chemiluminescent immunoassay method on an Advia Centaur XP (Siemens Diagnostic, Tarrytown, USA). Analytical variation (CV_A), within-subject BV (CV_I) and between-subject BV (CV_G) were calculated. Analytical goals, individuality index (II) and reference change value (RCV) were derived from these data. Statistical analysis was performed using BioVar: BV analysis tool v.1.0.

Results: For TSH, fT3 and fT4, CV_A (confidence interval, CI) were 3.3% (2.9 to 3.8), 1.7% (1.5 to 1.9) and 2.7% (2.4 to 3.1); CV_I (CI) were 22.3% (19.3 to 26.3), 4.4% (3.8 to 5.3) and 5.1% (4.3 to 6.1); CV_G (CI) were 26.6% (19.2 to 39.8), 9.2% (6.9 to 13.6) and 8.2% (6.1 to 12.1), respectively. For TSH, fT3 and fT4, desirable total errors were 27.1%, 6.2% and 6.6%; II values were calculated as 0.84, 0.48 and 0.61; and RCV% values (decrease; increase) were - 40.3;67.6, - 10.4;11.6 and - 12.7;14.5, respectively.

Conclusions: Our study provides updated BV data for thyroid function tests (TFTs) in healthy subjects in Turkey. As TFTs have shown a high degree of individuality, RCV should be preferred rather than population-based reference ranges in the assessment of serum concentrations. Our BV estimates were compatible with European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) BV meta-analysis data obtained using different immunoassay methods in different populations.