Scandinavian Journal of Clinical & Laboratory Investigation最新文献

Understanding biological variation (BV) is crucial for accurate clinical decision-making and for establishing analytical quality standards. This study established the BV of low-density lipoprotein cholesterol (LDL-C), assessed using both direct measurement and calculated values obtained from the Friedewald and Martin-Hopkins formulas in healthy individuals. A total of twenty-six healthy Turkish subjects (15 females and 11 males) underwent fasting LDL-C measurement, along with calculated LDL-C derived from serum cholesterol, triglycerides, and high-density lipoprotein cholesterol, using samples collected concurrently on 10 weekly occasions. All measurements were conducted in duplicate by the enzymatic colorimetric method. Within-subject (CV_I) and between-subject (CV_G) BV estimates, with 95% confidence intervals (CI), were determined by CV-ANOVA following evaluation of trends, homogeneity of variance, and outlier removal. No significant gender-related differences were observed in the BV components for either direct or calculated LDL-C. According to direct LDL-C, Friedewald LDL-C, and Martin Hopkins LDL-C, CV_I values were 8.7%, 9.3% and 9.0%, and the CV_G values were 14.7% for direct LDL-C, 18.5% for Friedewald LDL-C, 18.6% for Martin Hopkins LDL-C. These values supported the use of updated analytical performance specifications and reference change values (RCV). All LDL-C exhibited marked individuality (II < 0.6). By applying a rigorously standardized experimental protocol, the inter-individual variability observed supports the preferred use of RCVs over conventional population-based reference intervals for serial monitoring. These results have important implications for enhancing the clinical utility of LDL-C measurements, regarding cardiovascular risk assessment and individualized therapeutic decision-making.

Aim: Neuron-specific enolase (NSE) is an acknowledged biomarker for prognosticating neurological outcome after cardiac arrest, with elevated concentrations associated with poor outcome. This study assesses and compares the prognostic performance of NSE measured in serum and plasma for 1-year all-cause mortality among patients resuscitated from out-of-hospital cardiac arrest (OHCA).

Methods: This investigation is based on post hoc analyses of the Blood Pressure and Oxygenation Targets After Out-of-Hospital Cardiac Arrest (BOX) trial, performed in patients remaining comatose after resuscitation. NSE was measured 48 h after admission using three distinct methods; 1) Serum-NSE measured in fresh serum samples, 2) frozen-plasma-NSE, measured in freeze-thaw EDTA-plasma from stored biobank samples, and 3) in a subset of the samples also as frozen-serum-NSE, measured in freeze-thaw serum from stored biobank samples.

Results: A total of 381 comatose OHCA patients were included, with an overall one-year mortality of 33.1%. Serum-NSE concentrations were significantly higher than frozen-plasma-NSE, with median concentrations of 21.2 µg/L (IQR: 15.7-45.5) versus 19.1 µg/L (IQR: 11.2-39.6), p < 0.001, respectively. Notably, the difference between serum-NSE and frozen-plasma-NSE increased with higher NSE concentrations. The mean difference was 65.8 µg/L with 95% limits of agreement of +/- 125.75 µg/L among patients with NSE > 60 µg/L. For predicting 1-year all-cause mortality, the AUROC for serum-NSE was 0.93 and 0.83 for frozen-plasma-NSE with a significant difference in AUROC of 0.10 (CI: 0.06; 0.14), p < 0.001. In a sub-group analysis (n = 67), there was no significant difference when comparing AUROC between serum-NSE and frozen-serum-NSE (difference of 0.03 [CI: -0.04; 0.09], p = 0.44). However, within this sub-group, frozen-serum-NSE performed better than frozen-plasma-NSE with an AUROC difference of 0.08 (CI: -0.15; -0.01), p = 0.02.

Conclusions: Serum-NSE had greater accuracy in predicting 1-year mortality than frozen-plasma-NSE.

Our study aimed to compare the quality of patient self-collected capillary samples with venous blood samples. Additionally, we assessed whether patients with rheumatic disease are both capable of and willing to perform capillary self-sampling through subjective and objective assessments. This research explores the future potential of at-home self-sampling. Patients with rheumatic diseases were asked to perform up to four supervised self-collected capillary blood samples, followed by a standard venous sample performed by study personnel. Anti-rheumatic drug treatment monitoring parameters, including biochemistry and hematology, were analyzed using Cobas 8000 and Sysmex XN-9000, respectively. The agreement was evaluated by Bland-Altman plots and compared to critical difference limits. Study personnel and patients answered a survey questionnaire after every visit to evaluate feasibility. In total, 21 patients completed 53 paired capillary and venous samples from November 2019 to December 2020. We found a strong correlation (r > 0.87) and good agreement for most parameters; platelets showed the poorest agreement. Patients experienced little pain, found self-sampling easy and reported no serious complications. Hemolysis affected 12/53 capillary biochemistry samples, and 5/53 capillary hematology samples coagulated. The good agreement for most parameters and excellent feasibility encourages the potential for capillary self-sampling of DMARD monitoring parameters, relevant limitations were hemolysis and aggregating platelets.

Large language models (LLMs) have demonstrated high performance across various fields due to their ability to understand, generate, and manipulate human language. However, their potential in specialized medical domains, such as clinical chemistry and laboratory management, remains underexplored. This study evaluated the performance of nine LLMs using zero-shot prompting on 109 clinical problem-based quizzes from peer-reviewed journal articles in the Laboratory Medicine Online (LMO) database. These quizzes covered topics in clinical chemistry, toxicology, and laboratory management. The models, including GPT-4o, Claude 3 Opus, and Gemini 1.5 Pro, along with their earlier or smaller versions, were assigned roles as clinical chemists or laboratory managers to simulate real-world decision-making scenarios. Among the evaluated models, GPT-4o achieved the highest overall accuracy, correctly answering 81.7% of the quizzes, followed by GPT-4 Turbo (76.1%), Claude 3 Opus (74.3%), and Gemini 1.5 Pro (69.7%), while the lowest performance was observed with Gemini 1.0 Pro (51.4%). GPT-4o performed exceptionally well across all quiz types, including single-select, open-ended, and multiple-select questions, and demonstrated particular strength in quizzes involving figures, tables, or calculations. These findings highlight the ability of LLMs to effectively apply their pre-existing knowledge base to specialized clinical chemistry inquiries without additional fine-tuning. Among the evaluated models, GPT-4o exhibited superior performance across different quiz types, underscoring its potential utility in assisting healthcare professionals in clinical decision-making.

Vitamin B12 (B12) is essential for DNA synthesis in all cells and for the development and maintenance of a healthy nervous system. B12 is transported in the circulation bound to two carrier proteins, haptocorrin and transcobalamin, measured as the biomarkers total B12 and holotranscobalamin (holoTC). The latter measures the fraction of cobalamin available for tissue uptake and is considered to have a better sensitivity and specificity for diagnosing vitamin deficiency. The concentration of both carrier proteins depends on age, but data on paediatric reference values for holoTC are still sparse. Blood samples were obtained from 1320 healthy school children, mainly Caucasians (age 6-12 years old) in three different municipalities in Norway. The holoTC and total B12 levels were determined by chemiluminescent microparticle immunoassay on the Architect 2000 analyser. Age specific paediatric reference intervals (RIs) were estimated by calculating the 2.5 and 97.5 percentiles by the nonparametric method with corresponding 90% confidence intervals, according to the Clinical and Laboratory Standards Institute C28-A3C guidelines. The 95% RIs for total B12 were 295-1066 pmol/L for children 6-8 years old, and 249-879 pmol/L for children 9-12 years old. Reference intervals for holoTC were ≥56 pmol/L for children 6-8 years old, and ≥37 pmol/L for children 9-12 years old. Age specific RIs will aid clinicians in interpretation of cobalamin results in children aged 6-12 years old.

ProGRP (Progastrin-releasing peptide), SCC (Squamous Cell Carcinoma Antigen), and HE4 (Human epididymis protein 4) are serum tumor markers (STMs) frequently used in clinical practice, particularly for detection and monitoring of ovarian and lung neoplasms. In clinical laboratories, their quantification is commonly performed using automated immunoassays. Nevertheless, variations in results obtained by different immunoassays can impact diagnostic accuracy and effectiveness of patient monitoring. Our aim is to assess differences in STMs concentrations between two automated immunoassays: the Elecsys (Roche) and the Architect (Abbott), which are integrated into the Cobas e402 and Architect i2000SR systems respectively. We included 401 serum samples from patients with different clinical conditions: patients with cancer (n = 170), benign diseases (n = 100) and a control group (n = 131). ProGRP, SCC, and HE4 concentrations were quantified in parallel by both analyzers. To evaluate the clinical impact of changing these methodologies, overall concordance, the kappa index and ROC (Receiver Operating Characteristic) curves were calculated. While some discrepancies were noted in specific cases, overall, we obtained a good correlation for three STMs, with a Pearson coefficient for proGRP (r = 0.99), SCC (r = 0.95) and HE4 (r = 0.973). We also obtained a similar performance in the differential diagnosis of cancer, according to the results of the ROC analyses for Cobas and Archictect assays respectively: proGRP (AUC = 0.92; 0.91), SCC (AUC = 0.90; 0.92) and HE4 (AUC = 0.92; 0.93).

Data on the long-term within-subject biological variation (CV_I) of serum thyroid stimulating hormone (S-TSH) are scarce. In the EFLM Biological Variation Database, the longest observation period was one year. We estimated a coefficient of variation that included analytical variation (CV_I+A) using data from 16,976 individuals in the Trøndelag Health Study (HUNT), where S-TSH was measured on two occasions with an average interval of 10.6 (range 9.3-12.3) years. These individuals reported their health to be 'good' or 'very good' on both occasions and were not registered with any diagnoses or use of medications (according to Norwegian Prescribed Drug Registry) related to the thyroid. We used the software refineR to identify an assumed nonpathological subpopulation in the distribution of distances from each observation to the center of the bivariate distribution of the two S-TSH-values. From this subpopulation, individuals with a distance ≤ the 95 percentile in the distribution of distances were selected for estimation of CV_I+A. The difference in percent of the mean (DPM) was calculated for each individual, and CV_I+A as the standard deviation in the distribution of DPMs divided by 2^0.5. This method was robust against systematic bias between the two measurements. CV_I+A was 21-23% for different groups of age and sex. Accounting for CV_A would imply a CV_I 0.1-0.8% less than CV_I+A. Our estimates are well within the 12-29.3% range of CV_I reported from the seven studies in the meta-analysis of the EFLM Biological Variation Database.

Soluble urokinase plasminogen activator receptor (suPAR) is an inflammatory marker that has been shown to predict poorer outcomes in cardiovascular disease and after cardiac surgery. The relationship between suPAR concentrations and postoperative complications after valvular surgery, however, remains unclear. This study aims to evaluate the predictive value of suPAR concentrations for infection, acute kidney injury (AKI) and prolonged mechanical ventilation after valvular surgery. This prospective, observational, single-centre study included 414 patients who underwent valvular cardiac surgery at Skåne University Hospital between 1 February 2020 and 22 September 2021. Early postoperative suPAR levels were measured, and multivariable logistic regression was used to identify significant risk factors for postoperative infection, AKI and prolonged mechanical ventilation. Left ventricular ejection fraction (LVEF) 30-50% (OR 3.57 [1.29-9.86], p = 0.014) and suPAR concentration (OR 1.41 [1.56-1.71], p <0.001) were found to be predictive risk factors for developing postoperative infection. Additionally, suPAR concentration (OR 1.23 [1.05-1.43], p = 0.008), cardiopulmonary bypass (CPB) time (OR 1.01 [1.00-1.02], p = 0.004) and age (OR 1.04 [1.01-1.08], p = 0.007) were found to be predictive risk factors for postoperative AKI. However, suPAR concentration did not predict prolonged mechanical ventilation. Plasma suPAR levels after cardiac valve surgery were found to be predictive of postoperative AKI and infection. Our results indicate that early postoperative suPAR measurements may be a valuable tool for identifying patients at higher risk for developing postoperative complications.

Introduction: Monitoring CD3+, CD4+ and CD8+ T lymphocytes count is used in patients with known HIV infection, to determine efficacy of antiretroviral medication. Sometimes, due to the long distance, more time is needed for the sample to reach a more equipped laboratory. The aim of our study was to evaluate the impact of prolonged pre-analytical storage of blood at temperature, 96 h at room temperature, on the quality of results for the three parameters.

Methods: The analysis of 60 EDTA-anticoagulated blood samples, stored at room temperature (15-25 °C) after sampling, was conducted after 24 h and 96 h, respectively. The BD FACSLyric^™ system was used to identify and enumerate CD3+, CD4+, and CD8+ T lymphocytes.

Results: Following a 96-hour period, no notable discrepancies were observed in the data for CD3+, CD4+, and CD8+ T lymphocytes. Passing-Bablok regression analysis showed no significant difference in y-intercept and slope. The Pearson correlation coefficient (r) demonstrated a significant and strong correlation with rho values of 0.994, 0.992, and 0.996, respectively. The analytical agreements demonstrated that all results fell within the total allowable margin of total error.

Conclusion: The results of this study demonstrated that diagnostic samples, monitored for CD3+, CD4+ and CD8+ T lymphocytes, could be stored for up to 96 h without compromising the quality of the results.

The concentration of chromogranin A in serum (s-CgA) is a general marker of neuroendocrine neoplasms. Unfortunately, s-CgA is increased in several other clinical conditions, including renal failure. The physician who assesses s-CgA values must consider the patients' renal function. How this should be done is not clear. We developed an adjustment formula from the association between median s-CgA and the estimated glomerular filtration rate (eGFR) in 2708 patients where s-CgA was measured by the CgA II KRYPTOR method. We used multivariable fractional polynomial quantile regression with the model ln(s-CgA) = c₀ + c₁ × sex + c₂ × age + c₃ × eGFR, thus accounting for sex and age. The final adjustment formula could be simplified to s-CgA₁₀₀ = (eGFR / 100) × s-CgA, where s-CgA₁₀₀ is an indication of what s-CgA would be if eGFR in the same patient was 100 mL/minute/1.73 m². In patients with eGFR > 100 mL/minute/1.73 m² no adjustment was done. We tested the formula on another patient population (n = 1563), where s-CgA was measured by a RIA method. S-CgA₁₀₀ proved to be independent of eGFR in that population. The clinical validity of s-CgA₁₀₀ must await further investigations.