Scandinavian Journal of Clinical & Laboratory Investigation最新文献

Background: The direct antiglobulin test (DAT) is a key diagnostic tool in evaluating autoimmune haemolytic anaemia. However, indiscriminately ordering this test, together with certain methodological limitations, can compromise the efficiency of the clinical laboratory. This study aimed to develop and validate a predictive equation to identify negative results, optimising the use of DAT while maintaining the quality of care.

Methods: Through the laboratory information system (LIS), 1155 data were obtained from patients requesting DAT. A multiple logistic regression analysis was performed based on magnitudes related to haemolytic anaemia to obtain the best predictive model. The predictive equation obtained was: p = 1/(1 + e^-z) where 'P' represents the probability that the DAT is positive and 'z' the equation with the variables included in the model. Subsequently, its diagnostic efficiency was evaluated using a receiver operating characteristic curve. Finally, the equation was validated using a new cohort of data (N = 164).

Results: The 'z' value obtained from the best predictive equation was: $z=-2.884\hspace{0.17em}-\left(0.373\text{\hspace{0.17em}x\hspace{0.17em}Haptoglobin}\right)+\left(0.312\text{\hspace{0.17em}x\hspace{0.17em}\%Ret}\right).$ For the selected threshold, the equation demonstrated a sensitivity of 81.6%, a negative predictive value of 95.8%, and an area under the curve [95% confidence interval] of 0.812 [0.760-0.864]. According to the proposed equation, the performance of 61.6% of DAT would be reduced.

Conclusions: The proposed equation has an excellent predictive ability for negative DATs. Its simple integration into the LIS confirms its applicability in routine clinical laboratory practice, providing an effective screening tool for optimising DAT demand and managing resources efficiently.

Background and aims: The Triglyceride/HDL-Cholesterol (TG/HDL-C) ratio is a recognized biomarker for cardiovascular risk, linked to atherogenic dyslipidemia and insulin resistance. Recent evidence also implicates molecules like trimethylamine N-oxide (TMAO), CD36, and CD38 in metabolic inflammation and atherosclerosis. This study investigated the relationship between TMAO, CD36, and CD38 in individuals with a high TG/HDL-C ratio.

Methods: A total of 82 volunteer individuals with no known cardiac disease were enrolled in the study (41 dyslipidemic individuals and 41 healthy controls). Serum TMAO levels from participants were quantitatively measured using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The surface expression levels of CD36 and CD38 on the monocyte population were assessed by flow cytometry. The plasma atherogenic index (PAI) was calculated using the log(TG/HDL-C) formula to assess the atherogenic risk status.

Results: The TG/HDL-C ratio, TMAO level, and CD38 expressions were significantly higher in the dyslipidemia group compared to the control group (p < 0.05). Additionally, low-density lipoprotein cholesterol levels and HOMA-IR scores were higher in dyslipidemic individuals. According to the PAI assessment, the entire dyslipidemia group was in the high cardiovascular risk category. The TG/HDL-C ratio showed a positive correlation with TMAO and CD38 levels.

Conclusion: The positive relationship between the TG/HDL-C ratio and TMAO and CD38 levels suggests that these parameters could be evaluated together to reflect cardiovascular risk. However, given the cross-sectional design of the study, these findings indicate associations rather than causal relationships, and metabolic and inflammatory markers may reflect early stages of subclinical atherosclerosis in individuals without known cardiovascular disease.

Kidney dysfunction is a significant complication of allogeneic hematopoietic cell transplantation (alloHCT). Standard serum creatinine (sCr) testing is imperfect due to latency from the event causing damage to sCr increase. The purpose of this study was to evaluate the role of kidney damage biomarkers in the field of alloHCT. Seventy adult alloHCT candidates from 2 centers were recruited. 34 patients constituted pilot cohort and 36 - validation cohort. In pilot cohort serum and urine samples obtained at baseline and 7 timepoints were tested using ELISA assays for LFABP-1, KIM-1, NAG, uromodulin, TIMP-2 and IGFBP-7. Urine concentrations were corrected to urine creatinine concentration (uCr). Results were analyzed as predictors of acute kidney injury (AKI) within 100 days from alloHCT using receiver operating curve (ROC). LFABP-1, KIM-1, uromodulin, NAG and TIMP-2 performed significant predictive value for AKI. The best results were obtained for LFABP-1 and NAG and these biomarkers were tested in validation cohort where the results were borderline significant. Given the heterogeneity of the studied population, calculations for the whole group were performed: ROC for day +7/baseline ratio of urinary LFABP-1 and NAG in predicting AKI within 100 days from alloHCT was 0.673 and 0.678 respectively (p < 0.05). ROC for baseline urinary LFABP-1 in predicting early AKI was 0.721 (p < 0.05). Findings from our study confirm that patients undergoing alloHCT frequently experience subclinical kidney damage. However, due to the multifactorial nature of renal insults and preexisting kidney impairment, the predictive value of studied biomarkers in this population is limited.

Phototherapy is the most commonly used treatment for indirect (unconjugated) hyperbilirubinemia (IHB) in newborns. This study aims to determine the impact of IHB and phototherapy on oxidative stress balance by thiol-disulfide homeostasis and ischemia-modified albümin (IMA). This prospective study included 107 hospitalised newborns with IHB who received phototherapy and 55 controls. Neonates aged 0-28 days with a gestational age above 35 weeks were included in the patient group. Oxidative stress parameters were assessed in the control group as well as before and after phototherapy in the patient group. Sex, gestational age, and birth weight were similar between IHB group and control groups (p > 0.05). Native thiol and total thiol levels were significantly higher in the IHB group compared to controls (p < 0.05). After phototherapy, native thiol, total thiol, and Index 3 values were significantly reduced (p < 0.05), while disulfide levels, disulfide/native thiol ratio, disulfide/total thiol ratio, and IMA values significantly increased (p < 0.05). Oxidative stress via thiol-disulphide homeostasis is increasing in patients hospitalised due to IHB and receiving phototherapy. Our findings suggest that both IHB and phototherapy contribute to oxidative stress imbalance. Phototherapy should be used based on clinical indications and for the minimum needed duration.

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.

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.

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).