Electronic Journal of the International Federation of Clinical Chemistry and Laboratory Medicine最新文献

{"title":"Assessment of best practices for quality assurance in laboratories in Portuguese-speaking countries.","authors":"Flávia Martinello, Alice Berlanda Seidler, Maria Elisabeth Menezes, Helena Correia, Silvânia Da Veiga Leal, Armandina Miranda, Ana Faria","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Introduction: </strong>Laboratories (labs) play a fundamental role in screening, diagnosis, prognosis, and treatment of diseases. For a laboratory result to be useful, it must have guaranteed quality. In this context, there is no informative data on the best practices adopted by labs in Portuguese-speaking countries (PSC). This information is essential for formulating policies and educational strategies intended for this target audience.</p><p><strong>Objective: </strong>To identify and assess adherence to laboratory best practices by clinical analysis labs in Portuguese-speaking countries.</p><p><strong>Methods: </strong>A digital questionnaire consisting of 47 questions on laboratory best practices and quality management was sent to participants in the National Program for External Quality Assessment of Portugal and other labs involved in the Laboratory Quality Improvement Project for PSC-ProMeQuaLab, except from Brazil. Data were collected anonymously between July 7 and September 30, 2024 and analysed with descriptive statistics.</p><p><strong>Results: </strong>59 labs (ambulatory and hospital) participated in the study, but 5 institutions did not consent to the disclosure of their data, even if anonymously. Of the 54 included labs, most were from Portugal (39; 72%), followed by Cabo Verde (9; 16%), Guinea-Bissau (4; 7%), São Tomé and Príncipe (1; 2%), and 1 lab did not specify its country of origin. 57% of the labs have an implemented management system, and half of them are certified. Most labs belong to public services (63%), have a professional responsible for the management system (85%), conduct an annual training plan (85%), use quality indicators for the pre-analytical (87%) and post-analytical (83%) phases, and perform internal (70%) and external (89%) quality control. Opportunities for improvement were identified, as only 59% of labs record the causes of rejection of control sample results, 65% develop a competency matrix, 66% construct control charts, and 72% use quality specifications to assess analytical performance.</p><p><strong>Conclusion: </strong>Portuguese labs contributed the most to these results. Laboratory best practices are implemented, but there are opportunities for improvement. Conducting training and involving more labs from PSC will contribute to the implementation and harmonization of laboratory best practices, which can contribute to ensuring the quality of results and patient safety.</p>","PeriodicalId":37192,"journal":{"name":"Electronic Journal of the International Federation of Clinical Chemistry and Laboratory Medicine","volume":"37 2","pages":"227-237"},"PeriodicalIF":0.0,"publicationDate":"2026-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13088421/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147724109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Plasma Gasdermin D as a Biomarker for Pyroptosis in Early Detection of Newly Diagnosed Type 2 Diabetes Mellitus.","authors":"Mimoh Sharma, Anil Kumar, Akash Garwal","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Pyroptosis, a caspase-mediated inflammatory cell death pathway driven by Gasdermin D (GSDMD), has emerged as a potential mechanistic link between metabolic stress, β-cell injury, and chronic inflammation in Type 2 diabetes mellitus (T2DM). This study evaluated plasma GSDMD as a biomarker for newly diagnosed T2DM and explored its metabolic and inflammatory correlates. In a hospital-based case-control study, 130 newly diagnosed T2DM patients and 130 age- and sex-matched normoglycemic controls were recruited. Anthropometric indices, glycemic parameters, βFins, HOMA-IR, HOMA-β, lipid profile, and hs-CRP were measured using standard methods. Plasma GSDMD, IL-18, and IL-1β were quantified by ELISA. Group differences, correlations, multivariable logistic regression, and receiver operating characteristic (ROC) analyses were performed. T2DM patients exhibited higher BMI, adverse lipid profile, increased hs-CRP, and marked elevation of GSDMD, IL-18, and IL-1β compared with controls (all p<0.0001). Plasma GSDMD correlated positively with FBS, HbA1c, HOMA-IR, IL-18, IL-1β, and hs-CRP, and negatively with HOMA-β, indicating close links to hyperglycemia, insulin resistance, β-cell dysfunction, and systemic inflammation. In adjusted models, GSDMD remained an independent predictor of T2DM (OR 1.18 per 10 pg/mL, 95% CI 1.09-1.29), alongside IL-18, hs-CRP, higher BMI, and lower HDL-C. ROC analysis showed excellent diagnostic performance for GSDMD (AUC 0.98, 95% CI 0.96-0.99; cutoff 17.5 pg/mL; sensitivity 93.0%; specificity 97.0), superior to IL-18, IL-1β, and hs-CRP. Plasma GSDMD is markedly elevated in T2DM and integrates metabolic and inflammatory information, functioning as an independent risk factor and highly accurate diagnostic biomarker. These findings support GSDMD-mediated pyroptosis as a promising target for early diagnosis and risk stratification in T2DM.</p>","PeriodicalId":37192,"journal":{"name":"Electronic Journal of the International Federation of Clinical Chemistry and Laboratory Medicine","volume":"37 2","pages":"297-305"},"PeriodicalIF":0.0,"publicationDate":"2026-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13088507/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147724127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Triple Point Pooled Sera (TriPPS) QC for Laboratory Analyte Error Detection: A Machine Learning based Quality Control in Laboratory.","authors":"Prakruti Dash, Sudeshna Rout, Bharath Kumar Koppisetty, Chhabi Rani Panda, Dharashree Priyadarshini, Tanushree Roy, Saurav Nayak","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Background: </strong>Reliable internal quality control (IQC) is vital for ensuring analytical accuracy in clinical laboratories. Conventional rule-based QC systems, such as Westgard and Levey-Jennings, often exhibit retrospective detection and limited sensitivity to small but clinically meaningful shifts. This study introduces the Triple-Point Pooled Sera (TriPPS) Quality Control system, a novel, machine-learning-based framework integrating in-house pooled sera with adaptive algorithms for enhanced error detection.</p><p><strong>Methods: </strong>Residual patient sera were pooled to create stable, matrix-relevant IQC material for 60 consecutive analytical days. Sodium and potassium were used as representative analytes. Three complementary machine learning models were applied: k-Nearest Neighbour (k-NN) for trend detection, Isolation Forest (IF) for random error identification, and Gaussian Process Regression (GPR) for systematic bias modeling. Controlled ±1% daily biases and stochastic random errors were introduced to simulate analytical drift. Detection lag, sensitivity, and anomaly classification were evaluated.</p><p><strong>Results: </strong>The k-NN algorithm effectively identified trend errors within 0-2 days of bias onset, while IF accurately detected random fluctuations with minimal false positives. GPR modeled nonlinear systematic drift with high fidelity, capturing bias progression that is overlooked by linear methods. The integration of pooled sera enhanced the system's stability, reproducibility, and cost efficiency across all error types.</p><p><strong>Conclusion: </strong>The TriPPS system demonstrates a scalable, data-driven approach to laboratory quality control by combining pooled sera with machine learning algorithms. This framework enhances analytical vigilance, facilitates proactive error identification, and provides a practical, resource-efficient solution for real-time QC monitoring in clinical chemistry laboratories.</p>","PeriodicalId":37192,"journal":{"name":"Electronic Journal of the International Federation of Clinical Chemistry and Laboratory Medicine","volume":"37 2","pages":"336-344"},"PeriodicalIF":0.0,"publicationDate":"2026-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13088458/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147724074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ulcerative colitis initially misdiagnosed as irritable bowel syndrome: A case report.","authors":"Swathi Nalla, Burra Sai Ruthvik, G Tulja Rani, Illa Asha Latha","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Background: </strong>Irritable bowel syndrome (IBS) and early ulcerative colitis (UC) share overlapping gastrointestinal symptoms, which may lead to misdiagnosis and delay in appropriate treatment.</p><p><strong>Case presentation: </strong>The patient presenting with abdominal pain and altered bowel habits was initially diagnosed with IBS based on symptoms, physical examination, and past medical history, in the absence of alarm features. However, despite one week of symptomatic treatment, the patient's condition worsened.</p><p><strong>Diagnostic assessment: </strong>Further evaluation including complete blood profile, abdominal ultrasonography, colonoscopy, and colonic biopsy was subsequently performed, which confirmed a diagnosis of inflammatory bowel disease consistent with ulcerative colitis.</p><p><strong>Conclusion: </strong>This case highlights the diagnostic pitfalls in differentiating IBS from early UC based solely on clinical presentation. Clinicians should maintain a high index of suspicion and pursue appropriate laboratory and endoscopic investigations when symptoms persist or worsen, to avoid misdiagnosis, treatment delay, and disease progression.</p>","PeriodicalId":37192,"journal":{"name":"Electronic Journal of the International Federation of Clinical Chemistry and Laboratory Medicine","volume":"37 2","pages":"372-378"},"PeriodicalIF":0.0,"publicationDate":"2026-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13088462/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147724142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Variant in the TSH β Gene Resulted in Discordant TSH Levels in an Indian Patient.","authors":"Poonam Gera, Preeti Chavan, Sujeet Kamtalwar, Ashwini More, Varsha Jadhav, Prafulla Parikh, Sharda Haralkar, Rajni Mohite, Madhura Patil, Rushikesh Samal, Rajiv Sarin, Avinash Pagdhune","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>A clinically euthyroid patient of Indian origin was identified with persistently undetectable TSH concentrations using our laboratory's third-generation Ultra TSH assay, raising concerns of assay interference. The discordant results, flagged by the treating physician, prompted an in-depth investigation to determine the cause of undetectable TSH values despite the patient's euthyroid clinical status. Over an eight-month period, three consecutive serum samples consistently showed TSH levels below 0.008 μIU/mL on our routine platform. To rule out analytical artifacts such as the high-dose hook (prozone) effect, heterophilic antibody interference, and other pre-analytical or analytical errors, the samples were re-evaluated under various dilution protocols and assay conditions. Reanalysis using two alternate FDA-approved TSH immunoassays (CLIA and ECLIA platforms) revealed a TSH concentration of 6.81 μIU/mL, consistent with the clinical picture and in stark contrast to our initial results. Given the persistence of this discrepancy and the suspected interference with antibody recognition, genetic analysis of the TSHB gene was performed. Sanger sequencing of the entire coding region revealed a homozygous A-to-G substitution (c.223A>G; AGA>GGA) in exon 3, resulting in an arginine-to-glycine amino acid change at codon 75 (R75G) in the mature TSH β-subunit (RefSeq: NP_000540.2). This variant was absent in a control subject of similar ethnic background with normal TSH levels on the same assay, supporting its role in the observed interference. The mutation likely alters the epitope conformation of the TSH molecule, reducing its recognition by monoclonal antibodies used in specific immunoassays without impairing its biological activity. This case underscores the importance of correlating laboratory results with clinical findings and highlights the need for cross-platform verification when discordant TSH values are encountered. Genetic variants affecting TSH structure can lead to misinterpretation of thyroid function, and efforts toward assay standardization and harmonization are essential to mitigate such diagnostic pitfalls.</p>","PeriodicalId":37192,"journal":{"name":"Electronic Journal of the International Federation of Clinical Chemistry and Laboratory Medicine","volume":"37 1","pages":"171-176"},"PeriodicalIF":0.0,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12882083/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146143831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Professional Misconduct Discipline in New York State Clinical Laboratories, 2006-2024.","authors":"Paul A Elgert","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Since its implementation in 2006, the New York State Clinical Laboratory Practice Act has licensed 30,287 professionals. As of 2024, 56 disciplinary actions (0.18%) have been recorded, mostly due to criminal convictions (64.3%), particularly DWI, followed by fraudulent applications (23.2%) and workplace violations (12.5%). Males comprised 57% of the disciplined population, 66.7% of those fined, and 78.6% of the total fines paid. Most significant disciplinary outcome included 22 individuals (39.2%) that lost their licenses through annulment (n=13), revocation (n=2), surrender (n=5), and actual suspension (n=2). Laboratory disciplinary outcomes include 3 license surrenders and 1 actual suspension of at least two years (57.1% of workplace actions). Among cases triggered by criminal conviction, 2 licenses were surrendered and 2 revoked (11.1%). The data indicate rare but patterned misconduct, with notable gender disparities in penalties and license terminations.</p>","PeriodicalId":37192,"journal":{"name":"Electronic Journal of the International Federation of Clinical Chemistry and Laboratory Medicine","volume":"37 1","pages":"139-148"},"PeriodicalIF":0.0,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12882082/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146143911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intra-and Inter-Analyzer Imprecision of Cell Population Data on Sysmex XN-10.","authors":"Marco Tosi, Laura Pighi, Mariateresa Rizza, Gian Luca Salvagno, Giuseppe Lippi","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Introduction: </strong>Cell population data (CPD) derived from modern hematology analyzers provide morphological and functional insights into leukocytes beyond traditional cell counts. Nevertheless, their introduction into clinical practice requires proven analytical precision and consistency across instrumentation.</p><p><strong>Method: </strong>Two K2EDTA blood samples (one from a healthy blood donor and one from an intensive care unit patient) were analyzed in ten replicates on two Sysmex XN-10 analyzers. Intra- and inter-analyzer imprecision were calculated as coefficients of variation (CV%).</p><p><strong>Results: </strong>Intra-analyzer CV% ranged from 0.2-7.9% and inter-analyzer CV% from 0.6-9.8%. For neutrophil, lymphocyte, and monocyte CPD parameters, intra-/inter-analyzer CV% were 0.2-2.5%/0.6-7.0%, 0.5-6.6%/0.7-7.2%, and 0.2-7.9%/0.8-9.8%, respectively. The mostly used CPD parameters NE-SFL (neutrophil fluorescence intensity) and MO-X (monocyte complexity) displayed very low imprecision, with intra-analyzer CV% of 0.7-0.9% and 0.2-0.5%, and inter-analyzer CV% of 0.9-1.1% and 0.8-1.7%, respectively.</p><p><strong>Discussion: </strong>Our results confirm excellent reproducibility of Sysmex XN-10 CPD, consistent with or even improving upon earlier data obtained with the previous Sysmex XN-9000. The very low intra- and inter-analyzer variability of NE-SFL and MO-X supports their use as reliable clinical parameters, especially for infection and sepsis diagnostics.</p>","PeriodicalId":37192,"journal":{"name":"Electronic Journal of the International Federation of Clinical Chemistry and Laboratory Medicine","volume":"37 1","pages":"159-165"},"PeriodicalIF":0.0,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12882073/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146143953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Opinion Paper: Smartwatches in Healthcare: Revolutionizing Health or Creating Data Confusion?","authors":"Alistair J Gammie, James H Nichols, Ola Egaddar, Juergen Becker, Pradeep K Dabla, Bernard Gouget, Evgenija Homšak, Irena Korita, Kazuhiko Kotani, Ebru Saatçi, Sanja Stankovic, Zihni Onur Uygun, Laila AbdelWareth, Ram Vinod Mahato, Anirban Ganguly, Royronald O Ogong'a, Jani Rytkönen","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Smartwatches have gained significant attention for their role in advancing digital health interventions and enhancing wellbeing. These modern technologies have the potential to revolutionize patient care by making healthcare more accessible and personalized. By promoting preventive care, shifting services from hospitals to communities, and transitioning from analog to digital healthcare systems, smartwatches can help individuals stay healthier and reduce hospital visits. A key aspect of this transformation is the integration of smartwatch data into a unified patient record, ensuring comprehensive access to health information. However, with over 20 smartwatch manufacturers, offering multiple models with diverse health-monitoring capabilities, critical questions arise: What data is collected? How is it stored? Should any of it be integrated into medical records? Beyond the risks of data misuse for financial purposes, persistent challenges include ensuring accuracy, reliability, and standardization. A recent IFCC best practice publication on incorporating patient-generated health data into Electronic Health Records (EHRs) [1] highlights essential questions that must be addressed before smartwatch data can contribute meaningfully to healthcare. This paper explores these issues, weighing the potential benefits of smartwatches against the complexities of data integration and management.</p>","PeriodicalId":37192,"journal":{"name":"Electronic Journal of the International Federation of Clinical Chemistry and Laboratory Medicine","volume":"37 1","pages":"16-25"},"PeriodicalIF":0.0,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12882078/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146143900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Risk Management in a Clinical Biochemistry Laboratory.","authors":"Afif Ba, Mariem Machghoul, Manel Ayoub, Sana Aboulkacem, Mariem Belhedi, Zied Aouni, Chakib Mazigh","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Introduction: </strong>To ensure compliance with new laboratory standards, it is imperative to adopt risk-based thinking, which involves a systematic examination of the functions, procedures, and activities associated with risks and opportunities. This article aims to explore the implementation of risk-based thinking in medical biology laboratories and to highlight the challenges inherent in this approach.</p><p><strong>Materials and methods: </strong>This descriptive study was conducted in the biochemistry laboratory of the Main Military Teaching Hospital of Tunis during the first half of 2024. A risk analysis was performed by a working group to identify failures by analyzing non-conformities recorded during the study period. The group adopted the Failure Mode and Effects Analysis (FMEA) methodology, an inductive approach well-suited to process analysis and mastered by all participants. Subsequently, a corrective action plan was developed for each process phase.</p><p><strong>Results: </strong>Across the entire laboratory workflow, 33 distinct failure modes were identified and cataloged for each step, followed by a criticality analysis. The distribution of these failures was 36.36% in the pre-analytical phase, 33.34% in the analytical phase, and 30.3% in the post-analytical phase. A review of the severity of their effects revealed that a significant portion constituted major risks.</p><p><strong>Conclusion: </strong>In response to the major risks identified at each stage of the laboratory workflow, a corrective action plan has been proposed. This plan outlines specific actions designed to reduce the criticality of these risks and enhance patient safety and quality of service.</p>","PeriodicalId":37192,"journal":{"name":"Electronic Journal of the International Federation of Clinical Chemistry and Laboratory Medicine","volume":"37 1","pages":"120-126"},"PeriodicalIF":0.0,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12882091/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146143906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lipemia-Induced Hemoglobin Overestimation and Correction by Plasma Replacement in a Pediatric Acute Lymphoblastic Leukemia Patient.","authors":"Zeynep Şule Evren, Kamil Taha Uçar","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Background: </strong>Pre-analytical and analytical errors in laboratory testing can lead to clinical misinterpretation. This case highlights a falsely elevated hemoglobin level due to lipemia and the corrective laboratory intervention.</p><p><strong>Case: </strong>A 3-year-7-month-old girl with acute lymphoblastic leukemia underwent a follow-up complete blood count which reported a hemoglobin level of 16.9 g/dL. The hemoglobin result was inconsistent with previous clinical findings and hematocrit. A simultaneously drawn venous blood gas sample showed a hemoglobin value of 9.2 g/dL. The biochemistry sample showed visible lipemia, with a lipemia index of 3041. The same sample revealed a triglyceride level of 8042 mg/dL (1:50 dilution) and total cholesterol of 492.2 mg/dL. These findings indicated a falsely elevated hemoglobin due to lipemia. The patient was not on parenteral nutrition. Pediatric endocrinology consultation attributed lipemia to L-asparaginase and corticosteroids in the treatment regimen. To eliminate lipemic interference, the EDTA blood sample was centrifuged at 1000 x g for 10 minutes, and the lipemic plasma was replaced with an equal volume of 0.9% NaCl solution. The sample was gently mixed to restore whole blood integrity. After this plasma replacement procedure, hemoglobin was measured as 10.2 g/dL, consistent with the blood gas result and clinical picture.</p><p><strong>Conclusion: </strong>This case emphasizes the need to correlate laboratory results with clinical and biochemical data. In lipemic samples, plasma replacement may provide a practical correction method for falsely elevated hemoglobin values when resampling is not feasible. Recognition and prompt correction of lipemia-induced errors are crucial to avoid inappropriate clinical decisions.</p>","PeriodicalId":37192,"journal":{"name":"Electronic Journal of the International Federation of Clinical Chemistry and Laboratory Medicine","volume":"37 1","pages":"191-194"},"PeriodicalIF":0.0,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12882086/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146143923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}