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

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.

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.

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.

Introduction: 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.

Method: 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%).

Results: 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.

Discussion: 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.

Introduction: 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.

Materials and methods: 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.

Results: 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.

Conclusion: 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.

Background: 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.

Case: 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.

Conclusion: 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.

Accurate quantification of urinary protein is fundamental for the diagnosis, monitoring, and management of kidney disease. Despite technological advances, both pre-analytical and analytical non-conformities in laboratory testing remain significant sources of misinterpretation that can adversely affect clinical decision-making. This report analyzes two such cases to highlight common but critical pitfalls in proteinuria assessment. We present two illustrative cases: the first involves a pediatric patient, where a pre-analytical non-conformity led to a significant overestimation of proteinuria severity. The second case describes an elderly diabetic patient where an analytical non-conformity resulted in a profound underestimation of albuminuria. In both instances, discrepancies between semi-quantitative and quantitative results were the critical clues that prompted investigation. These cases underscore that urinary protein results, while quantitative, are not infallible. Vigilant attention to pre-analytical procedures, strict adherence to analytical limits, and the integration of semi-quantitative results as a plausibility check are essential to prevent diagnostic non-conformities. Effective communication between clinicians and laboratory professionals is paramount to ensure that laboratory results accurately inform, rather than misdirect, clinical decision-making.

Introduction: Hyperparathyroidism due to chronic kidney disease (CKD) is a common complication characterized by elevated parathyroid hormone (PTH) levels secondary to derangements in the homeostasis of calcium and phosphorus. The aim of our study was to figure out PTH, calcium, phosphorus, and magnesium levels in CKD and the possible correlations between them and the stages of CKD.

Materials and methods: We performed a prospective study including 217 outpatients with levels of serum creatinine out of reference range from February 2023 to July 2024. We calculated the glomerular filtration rate (eGFR) with the 2021 CKD-Epi equation using serum creatinine. We used Jamovi Statistical Software version 2.3.28. A "p" value equal to or less than 0.05 was considered statistically significant.

Results: We had 105 females (48%) and 112 males (52%), median age 72 years (35-94yrs). We found differences in PTH and phosphorus levels between distinct stages of CKD. For PTH the differences were found between stages IIIa and IV (p<0.001), IIIb and IV (p=0.001), while for phosphorus between stages IIIa and IIIb (p=0.003), IIIa and IV (p<0.001), IIIb and IV (p=0.01). We found correlation between eGFR and PTH (r= -0.360, p=0.001), eGFR and calcium (r=0.169, p=0.015), eGFR and magnesium (r= -0.153, p=0.028), eGFR and phosphorus (r= -0.336, p<0.001).

Conclusions: We concluded that there is a statistically significant correlation between PTH and stages of CKD, but the strength of correlation is low, it cannot be generalized, therefore each patient with CKD must be assessed individually.

Background: Synovial fluid analysis plays a crucial role in the diagnosis of periprosthetic joint infection (PJI). However, the stability of leukocyte counts and the percentage of polymorphonuclear neutrophils (PMN%) under different storage conditions remains uncertain, and many institutions lack immediate access to on-site laboratories. We investigated whether storage temperature (room temperature (RT) vs 4 °C) influences synovial fluid white blood cell (WBC) count and PMN%, and if these parameters are stable for up to 72 hours after aspiration.

Methods: We prospectively analysed 106 synovial fluid samples obtained during revision arthroplasty for suspected PJI. Assuming that the population was homogenous according to the inclusion criteria, patient's samples were randomly allocated either to be stored at RT or at 4°C, in order to obtain two different set of samples. In both set of samples WBC count and PMN% were measured at baseline, 6, 12, 24, 48, and 72 hours using an automated haematology analyser after pre-treatment with hyaluronidase. Changes over time in the same patient synovial fluid and between different storage temperature groups were assessed with independent T test.

Results: Both WBC count and PMN% remained stable for up to 72 hours in samples stored at either RT or 4 °C. Mean WBC counts were slightly higher in refrigerated samples, but differences were minimal and not statistically significant. No variation led to reclassification of samples across the ICM 2018 diagnostic thresholds for PJI.

Conclusion: Synovial fluid WBC and PMN% remain stable for up to 72 hours regardless of storage temperature. These findings challenge the assumption that immediate analysis is required and support greater flexibility in clinical workflows, particularly in institutions without immediate on-site laboratory availability.