Clinical chemistry and laboratory medicine最新文献

{"title":"Reviewer Acknowledgment.","authors":"","doi":"10.1515/cclm-2024-2001","DOIUrl":"https://doi.org/10.1515/cclm-2024-2001","url":null,"abstract":"","PeriodicalId":10390,"journal":{"name":"Clinical chemistry and laboratory medicine","volume":"63 1","pages":"220-223"},"PeriodicalIF":3.8,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143983796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Use of indirect methods and machine learning algorithms for the estimation of reference intervals, taking cortisol measurements as an example.","authors":"Fatma Demet Arslan, Georg Hoffmann","doi":"10.1515/cclm-2025-0984","DOIUrl":"https://doi.org/10.1515/cclm-2025-0984","url":null,"abstract":"<p><strong>Objectives: </strong>This study aims to determine reliable reference intervals (RIs) for total cortisol (TC) in adults considering the effects of both age and blood collection time, using indirect methods and machine learning approaches.</p><p><strong>Methods: </strong>Serum TC results from blood samples collected between 08:00 and 10:00 am at the first outpatient visit were included in the study. Serum TC were measured using a Roche Elecsys Cortisol II kit. Estimated RIs by indirect methods with the support of R packages (refineR and reflimR) for the implementation of machine learning algorithms (mclust and rpart) were compared with the manufacturer's reference interval (RI) (48-195 μg/L).</p><p><strong>Results: </strong>Estimated RIs by refineR and reflimR (57-256 μg/L and 62-271 μg/L, respectively) were wider than the manufacturer's RI. When reflimR was applied to Box-Cox-transformed data with the lambda value of 0.284 suggested by refineR, an RI of 57-251 μg/L was obtained, which was like that obtained with refineR. An even better match with the manufacturer's RI was achieved using Gaussian mixture modelling with the mclust, which suggested one out of four clusters with an RI of 55.8-187 μg/L. Clustering the data with rpart suggested stratification into two age groups (≤35 and >35 years) and three blood collection periods (08:00-08:45, 08:45-09:35, and 09:35-10:00). The TC levels demonstrated the highest concentrations in the early morning (8:00-08:45) and in young adults (18-35 years).</p><p><strong>Conclusions: </strong>This study highlights the necessity of considering both age and blood collection time in clinical interpretation and demonstrates the effectiveness of indirect methods and machine learning approaches in the verification of RIs for hormones with known heterogeneity.</p>","PeriodicalId":10390,"journal":{"name":"Clinical chemistry and laboratory medicine","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145343790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ReferenceRangeR: a novel tool designed to facilitate reference interval estimation and verification.","authors":"Gunnar Brandhorst, Maike Voß, Werner Wosniok, Farhad Arzideh, Rainer Haeckel, Daniel Rosenkranz, Thomas Streichert, Astrid Petersmann","doi":"10.1515/cclm-2025-0637","DOIUrl":"https://doi.org/10.1515/cclm-2025-0637","url":null,"abstract":"<p><strong>Objectives: </strong>Reference intervals (RIs) are essential for interpreting laboratory test results. It is recommended that each medical laboratory establishes and reviews its own RIs. The use of direct methods is often unfeasible for most laboratories, while indirect methods are a more viable alternative. However, these methods require not only medical, but also statistical and technical expertise, thereby limiting accessibility for many laboratories. To address this challenge, a web-based application was developed to facilitate the estimation and verification of RIs using real-world laboratory data.</p><p><strong>Methods: </strong>The application was developed using R Studio and the Shiny web framework. The tool supports five indirect methods for reference interval estimation: refineR, TMC, TML, kosmic and reflimR. Furthermore, a Docker container was designed to enable a secure local deployment.</p><p><strong>Results: </strong>Up to 200,000 laboratory test results can be included via a straightforward copy-and-paste input. The tool provides recommendations for sex-based stratification by performing statistical analysis. In addition, a drift-detection algorithm was developed to analyze whether age-based stratification is necessary. The results of RI estimation are displayed and visualized alongside the underlying data distribution. Existing RIs can be verified by comparing them against calculated intervals.</p><p><strong>Conclusions: </strong>ReferenceRangeR is a user-friendly tool for estimating and verifying RIs using real-world laboratory data, eliminating the need for statistical or technological expertise, thereby supporting laboratory professionals in meeting the current regulatory standards.</p>","PeriodicalId":10390,"journal":{"name":"Clinical chemistry and laboratory medicine","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145343804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hemolysis index. Can we uncritically trust manufacturer declarations?","authors":"Alen Vrtaric, Marijana Miler, Nora Nikolac Gabaj, Ivana Celap, Valentina Vidranski, Marina Bocan, Petra Filipi, Marija Kocijancic","doi":"10.1515/cclm-2025-1302","DOIUrl":"https://doi.org/10.1515/cclm-2025-1302","url":null,"abstract":"<p><strong>Objectives: </strong>Hemolysis is a common preanalytical interference in clinical biochemistry, affecting up to 3.3 % of routine samples and contributing to approximately 60 % of rejected specimens. It results from the release of intracellular constituents, such as hemoglobin and potassium, into the plasma due to cell membrane disruption. This interference is often evaluated through hemolysis index (HI) thresholds defined by <i>in vitro</i> diagnostic (IVD) analyzers, but these thresholds may not align with clinical needs or biological variation.</p><p><strong>Methods: </strong>A descriptive study was conducted from February to April 2025, comparing the hemolysis interference data of 72 clinical chemistry analytes across five major manufacturers: Abbott, Beckman Coulter, BioSystems, Roche, and Siemens. Manufacturer package inserts were reviewed and compared to the CLSI C56-A guideline for hemolysis interference testing.</p><p><strong>Results: </strong>The compliance of manufacturers with the CLSI guideline varied significantly. Siemens had the highest compliance, reporting hemolysis interference at two analyte levels in 46/71 of cases, while BioSystems did not report analyte concentrations for any tested parameters. Criteria for significant interference was often missing, and in many cases, manufacturers used arbitrary thresholds, such as 10 % bias. Additionally, acceptance criteria and reported bias values were inconsistent across manufacturers, with only Abbott and Siemens providing detailed bias data.</p><p><strong>Conclusions: </strong>Manufacturer instructions often lack completeness and consistency, making it crucial for clinical laboratories to independently validate hemolysis thresholds. A harmonized approach, incorporating manufacturer data, research, and local validation, is essential to improve laboratory quality, minimize misinterpretations, and ensure compliance with regulatory standards.</p>","PeriodicalId":10390,"journal":{"name":"Clinical chemistry and laboratory medicine","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145344008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization and comparison of genomic DNA extraction from whole blood collected in PAXgene blood RNA tube using automated platforms.","authors":"Jianlan You, Tomoe Shiomi, Paul Zappile, Luis Chiriboga, Sandra Mendoza, Andre L Moreira","doi":"10.1515/cclm-2025-1079","DOIUrl":"https://doi.org/10.1515/cclm-2025-1079","url":null,"abstract":"<p><strong>Objectives: </strong>With the development of genomic technologies, the isolation of genomic DNA (gDNA) from clinical samples has become more important for both clinical diagnostics and research studies. Blood samples collected in PAXgene Blood RNA Tubes, which are typically used for RNA extraction, can be used for gDNA extraction, particularly in clinical studies when only such blood samples are available.</p><p><strong>Methods: </strong>We optimized the pre-treatment of blood samples collected in PAXgene Blood RNA Tubes. For the first time, three magnetic bead-based automated platforms (QIAsymphony SP, Maxwell RSC and KingFisher Apex) were compared for gDNA extraction from these blood samples. Additionally, the effects of storage at 4 °C or freeze-thaw cycles of the blood samples on gDNA yield were investigated. High-throughput extraction in 96-well format was evaluated.</p><p><strong>Results: </strong>Systematic optimization of blood sample pre-treatment shows that prolonged incubation at room temperature and/or increased centrifugation speed and time improved gDNA yield from blood samples collected in PAXgene Blood RNA Tubes. QIAsymphony SP (4.27 ± 2.19 µg) and Maxwell RSC (4.82 ± 2.96 µg) produced significantly higher gDNA yields than KingFisher Apex (1.09 ± 0.61 µg). Higher gDNA yields were obtained with shorter storage time at 4 °C or fewer freeze-thaw cycles of the blood samples. In the 96-well format extraction, gDNA yields ranged from 0.24 to 13.46 µg.</p><p><strong>Conclusions: </strong>Not all magnetic bead-based automated platforms are suitable for gDNA extraction from blood samples collected in PAXgene Blood RNA Tubes. Systematic pre-treatments optimization provides guidance for routine and high-throughput workflows, and storage conditions and freeze-thaw cycles offer practical reference for biobanking.</p>","PeriodicalId":10390,"journal":{"name":"Clinical chemistry and laboratory medicine","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145318294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dental amalgams and blood mercury concentrations in American adults.","authors":"David A Geier","doi":"10.1515/cclm-2025-0909","DOIUrl":"https://doi.org/10.1515/cclm-2025-0909","url":null,"abstract":"<p><strong>Objectives: </strong>Dental amalgam (50 % mercury (Hg) by weight) is a commonly used material to restore a tooth damaged by decay. In recent years, amalgam safety has become a matter of ongoing controversy. This hypothesis-testing epidemiological study evaluated the relationship between blood Hg concentrations and amalgams in American adults.</p><p><strong>Methods: </strong>Examination of the 2015-2016 National Health and Nutrition Examination Survey (NHANES) was undertaken using SAS, version 9.4 (Cary, NC, USA), survey regression statistical modeling (with adjustments for covariates). A total of 180,811,187 weighted-Americans (n=1,377) between the ages of 18-70 years-old, with known: dental filing surface status; urinary Hg concentrations; total and blood Hg species (inorganic and methyl-Hg) concentrations; bodyweight; and urine flow rates were examined.</p><p><strong>Results: </strong>Significant increases were found in the blood concentrations of total and inorganic Hg, when comparing adults exposed to amalgams as compared to adults not exposed to amalgams. Amalgam surfaces significantly correlated with blood inorganic Hg concentrations, and estimated daily Hg vapor doses from amalgams significantly correlated with blood total and inorganic Hg concentrations. This study supports the importance of blood as an important transport avenue for Hg, which is dose-dependently released by amalgams, to accumulate in tissues and cells throughout the body.</p><p><strong>Conclusions: </strong>Persons with amalgams, desiring to lower their blood Hg concentrations, should consult with a dentist trained and certified in safe amalgam removal. Also, pharmaceutical treatments to reduce/render non-toxic the blood Hg concentrations from amalgams should be considered. Efforts should be made to reduce/eliminate the continued use of amalgams.</p>","PeriodicalId":10390,"journal":{"name":"Clinical chemistry and laboratory medicine","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145318246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An isotope dilution-liquid chromatography-tandem mass spectrometry-based candidate reference measurement procedure for the quantification of cortisol in human urine.","authors":"Myriam Ott, Friederike Bauland, Daniel Köppl, Alice Ayik, Andrea Geistanger, Manfred Rauh, Judith Taibon","doi":"10.1515/cclm-2025-0522","DOIUrl":"https://doi.org/10.1515/cclm-2025-0522","url":null,"abstract":"<p><strong>Objectives: </strong>Precise and accurate determination of urinary cortisol is important for diagnosis and monitoring of cortisol excess and deficiency. To achieve this, a selective and specific, isotope-dilution two-dimensional heart-cut liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based method was developed.</p><p><strong>Methods: </strong>A novel, two-dimensional heart-cut LC-MS/MS method was developed using a Waters Acquity BEH C18 column and a Raptor Biphenyl column, with water (A1) and with 0.2 mM ammonium fluoride in water (A2) and methanol (B1+B2) as mobile phases. This approach was complemented by a supported liquid extraction (SLE) sample preparation protocol. Matrix effects were assessed through post-column infusion experiments and comparisons of standard line slopes across matrices. A multi-day validation study evaluated accuracy and precision of the candidate reference measurement procedure (RMP) and measurement uncertainty (MU) was estimated in compliance with current guidelines. Reproducibility was assessed by performing a method comparison study between two independent laboratories.</p><p><strong>Results: </strong>In the first LC column dimension, cortisol and prednisolone were separated from cortisone, prednisone, and 20-beta-dihydrocortisone. Baseline separation of cortisol from prednisolone was achieved in the second dimension. No interfering signals were detected at the retention time for quantifier and internal standard transitions in any urine samples analysed. No significant matrix effect, ion suppression, or enhancement was observed. Linearity was successfully demonstrated (r≥0.999); residuals were randomly distributed in a quadratic model via linear regression with an 1/x weighting. The lower limit of the measuring interval was determined at 2.00 ng/mL (5.52 nmol/L), displaying a relative deviation of -0.1 %, a coefficient of variation (CV) of 2.9 %, and recoveries of 96-103 %. Intermediate precision ranged from 1.9-3.2 % and repeatability CV from 1.6-2.7 %. Relative mean bias ranged from -3.5-1.2 %, indicating method accuracy was unaffected by matrix variance. Measurement uncertainties (k=1) for cortisol were ≤3.4 % across different concentrations and sample types. The expanded uncertainty at a 95 % confidence level (k=2) was ≤6.8 %. An increased number of measurements (n=6) for target value assignment reduced the uncertainty to 0.8-1.3 % (k=1), yielding an expanded uncertainty (k=2) of 1.7-2.5 %.</p><p><strong>Conclusions: </strong>The validation results confirm the robustness, accuracy, and reliability of this RMP for determination of cortisol in human urine within a working range of 2.00-220 ng/mL (5.52-552 nmol/L).</p>","PeriodicalId":10390,"journal":{"name":"Clinical chemistry and laboratory medicine","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145318303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Keeping pace with patient safety by developing and qualifying higher-order reference measurement procedures for laboratory measurement standardization.","authors":"Mauro Panteghini, Robert Wielgosz","doi":"10.1515/cclm-2025-1340","DOIUrl":"https://doi.org/10.1515/cclm-2025-1340","url":null,"abstract":"","PeriodicalId":10390,"journal":{"name":"Clinical chemistry and laboratory medicine","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145318219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ROTEM-guided diagnostic pathway in a centralized Laboratory Medicine setting: an organizational report.","authors":"Monica Bovara, Margherita Scapaticci, Mario Sebastiani, Sebastiano Nigro, Marina Bioli, Maria Beatrice Rondinelli, Rita Mancini, Andrea Bartolini","doi":"10.1515/cclm-2025-0919","DOIUrl":"https://doi.org/10.1515/cclm-2025-0919","url":null,"abstract":"","PeriodicalId":10390,"journal":{"name":"Clinical chemistry and laboratory medicine","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145318054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The role of AI in pre-analytical phase - use cases.","authors":"Hikmet Can Çubukçu","doi":"10.1515/cclm-2025-1220","DOIUrl":"https://doi.org/10.1515/cclm-2025-1220","url":null,"abstract":"<p><p>The pre-analytical phase of laboratory testing, encompassing processes from test ordering to sample analysis, represents the most error-prone component of laboratory medicine, accounting for 68-98 % of laboratory mistakes. These errors compromise patient safety, increase healthcare costs, and disrupt operational efficiency. Artificial intelligence (AI) and machine learning (ML) technologies have emerged as promising solutions to address these challenges across multiple pre-analytical applications. This narrative review examines current AI research applications and commercial implementations across seven key pre-analytical domains: clot detection, wrong blood in tube (WBIT) error detection, sample dilution management, chemical manipulation detection in urine samples, serum quality assessment based on hemolysis/icterus/lipemia (HIL), test utilization optimization, and automated tube handling. Research studies demonstrate impressive performance, with neural networks achieving accuracies exceeding 95 % for clot detection, XGBoost models reaching 98 % accuracy for WBIT detection, and deep learning systems attaining AUCs above 0.94 for test recommendation systems. However, a significant translation gap persists between research prototypes and commercial deployment. Academic models excel at pattern recognition using curated datasets but face limitations including single-center validation, retrospective designs, and integration challenges. Commercial solutions prioritize deterministic controls, barcoding, and sensor-based approaches that ensure reliability and scalability, with limited explicit AI implementation. Successful clinical laboratory translation requires multicenter prospective validation, robust laboratory information system integration, regulatory compliance frameworks, and evaluation metrics focused on operational outcomes rather than solely statistical performance. As infrastructure and standards mature, strategic AI adoption in pre-analytical tasks offers measurable improvements in safety, efficiency, and cost-effectiveness.</p>","PeriodicalId":10390,"journal":{"name":"Clinical chemistry and laboratory medicine","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145291152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}