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

Overt hypothyroidism is associated with high levels of serum uric acid (UA) however, the association between UA and thyroid function in patients with subclinical thyroid dysfunction remains unclear. Subclinical hypothyroidism (SCH) is a common endocrine disorder characterized by normal thyroxine (T4) and triiodothyronine (T3), and elevated thyroid stimulating hormone (TSH) levels, usually without clinical manifestations. Therefore, we carried out a study of patients with subclinical thyroid dysfunction to assess the relationship between thyroid function and UA. This lead us to review the literature to find to what extent subclinical hypothyroidism is associated with uric acid. This study adopts the method of retrospective analysis to collect general information and laboratory results aimed at assessing the correlation between uric acid and thyroid hormone levels. We searched 3 databases using different keywords. Literature search was done for articles published in the last ten years, between 2013-2023. All relevant studies were screened. A total of eighteen articles were finalized for the review. Some studies supported T3 supplementation resulting in SCH correction. Our study indicates that it is important to screen for serum uric acid levels routinely in patients with subclinical hypothyroidism.

Introduction: A workplace-based assessment (WBA) model was implemented in the postgraduate (PG) residency program of Chemical Pathology at the Department of Pathology & Laboratory Medicine, Aga Khan University (AKU). PGs were assessed using direct observation of practical skills (DOPS), evaluation of clinical events (ECE) and case-based discussion (CBD) on a virtual learning environment (VLE) platform.

Objectives: To evaluate WBA frequency, case mix, feedback, and satisfaction levels of faculty and PGs of Chemical Pathology at AKU.

Methods: Data from January 2019 to June 2023 was assessed. Tool utilization and case mix frequencies were calculated. PG and faculty satisfaction levels, as well as feedback and discussion time, were averaged. A thematic analysis was conducted on descriptive comments.

Results: Out of 911 WBAs attempted, 79.1% (n=730) were CBDs, 10.8% (n=98) were DOPS, and 9.1% (n=83) were ECEs, showing a well-distributed case mix. Average satisfaction levels for CBD, ECE, and DOPS among both PGs and faculty were 8.38, 8.48, and 8.59, and 8.20, 8.36, and 8.46, respectively. Faculty feedback averaged 8.40, 8.65, and 7.85 minutes for CBD, ECE, and DOPS, respectively. Discussion times averaged 9.37, 9.52, and 13.36 minutes for CBD, ECE, and DOPS, respectively. Suggestions for development were noted in 20.82% (n=225) of CBDs, 21.69% (n=18) of ECEs, and 16.32% (n=16) of DOPS. Positives were documented in 40% (n=292) of CBDs, 28.92% (n=24) of ECEs, and 7.14% (n=7) of DOPS.

Conclusion: This study evaluated a web-based WBA model in chemical pathology training, suggesting its applicability in diverse pathology specialties and regional training programs.

Background: Reference intervals (RI) are a vital part of information provided with laboratory results. It is recommended that RI should be established by each laboratory following pre-laid guidelines. In this systemic review, we aim to comprehensively analyze and summarize all the published literature about establishment of RI for biochemical parameters in Pakistani population.

Methodology: We conducted a comprehensive search using Medline (PubMed interface) and PakMediNet literature, adhering to PRISMA guidelines. The search spanned from January 1984 to February 2024. All studies done for establishment of RI of biochemical parameters were included, while were nonhuman studies, case studies, preprints, no full text and articles in languages other than English were excluded. Rigorous evaluation ensured the robustness of their study analysis.

Results: Database search reveled 161 studies, 30 were analyzed as per inclusion criteria. The accumulated sample size of the studies comprised 108,563 individuals. Most of the studies were carried out on adults in Punjab and Sindh provinces. A wide variation was noted among the RIs established and units used in each study. Gaps were identified regarding description of healthy population, patient preparation sample handing and quality control.

Conclusion: In this review, critical gaps in data, methodology and reporting were identified. To enhance future studies, researchers should clearly define healthy populations, incorporate rigorous sample handling and quality control, and collaborate across centers.

Background: Though paraproteinaemic interferences is a well-known phenomenon in clinical chemistry, a large-scale evaluation study involving multiple paraproteinaemic specimens on multiple platforms including multiple measurands with an aim to provide a predictive analysis, is singularly lacking. The present study aims to fill this gap in research.

Material and methods: This cross-sectional non-interventional observational study involved thirteen paraproteinaemic subjects, determined their gamma globulin characterization and measured their total bilirubin, direct bilirubin, HDL-cholesterol, calcium, inorganic phosphate, iron and unsaturated iron binding capacity (UIBC) levels on a dry chemistry platform (Vitros 350) as the established method and two wet chemistry platforms (AU5800 and Cobas 6000) as the evaluation methods. Data thus generated was analyzed for any significant variation and tested if such variation increased with decreasing albumin/ globulin ratio.

Results: Significant variation between dry chemistry and wet chemistry measurements were obtained for direct bilirubin, HDL and iron on AU5800 with p-values of 0.0009, <0.0001 and 0.0466 respectively. Similarly, discrepant results were obtained on Cobas 6000 for direct bilirubin and iron, with p-values of <0.0001 and 0.0002 respectively. Additionally, UIBC measurements on AU5800 varied significantly with increasing amounts of paraprotein present in the specimen (p-value = 0.0207).

Conclusion: This study emphasizes on predictive analyses to show that paraprotein interferences are fairly common on wet chemistry platforms. Evolving algorithms for monitoring of reaction curves can minimize release of erroneous results due to such interferences.

Introduction: Quality Control Management (QCM) in clinical laboratories is crucial for ensuring reliable results in analytical measurements, with biological variation being a key factor. The study focuses on assessing the analytical performance of the Reverse Transcription Polymerase Chain Reaction (RT-PCR) system for Human Immunodeficiency Virus (HIV), Hepatitis B (HBV), and Hepatitis C (HCV). Five models proposed between 1999 and 2014 offer different approaches to evaluating analytical quality, with Model 2 based on biological variation and Model 5 considering the current state of the art. The study evaluates the RT-PCR system's analytical performance through Internal Quality Control (IQC) and External Quality Control (EQC).

Materials and methods: The Laboratório Central de Saúde Pública do Estado do Ceará (LACEN-CE) conducted daily IQC using commercial kits, and EQC was performed through proficiency testing rounds. Random error, systematic error, and total error were determined for each analyte.

Results: Analytical performance, assessed through CV and random error, met specifications, with HIV and HBV classified as "desirable" and "optimal." EQC results indicated low systematic error, contributing to total errors considered clinically insignificant.

Conclusion: The study highlights the challenge of defining analytical specifications without sufficient biological variability data. Model 5 is deemed the most suitable. The analytical performance of the RT-PCR system for HIV, HBV, and HCV at LACEN-CE demonstrated satisfactory, emphasizing the importance of continuous quality control in molecular biology methodologies.

Introduction: The majority of the high-grade serous ovarian cancer (HGSOC) cases are diagnosed late, preventing effective treatment and therapy. We examine the feasibility of using EVA (Early oVArian cancer), a new molecular test for early HGSOC detection.

Methods: Comparison of the advantages and disadvantages of EVA with previously reported ovarian cancer tests, including CA125, was made, and the positive and negative predictive values of the tests were calculated as a measure of usefulness in the clinic.

Results: The positive predictive value of EVA and CA125 was 8.6% and 6.8% respectively, which was calculated based on the disease prevalence of 0.5%. The negative predictive value was 99.9% in both cases.

Conclusions: EVA and CA125 are unlikely to provide a meaningful population screening method for HGSOC in women at risk, since the predictive values would drive women not to perform these tests.

Background: Insulin resistance (IR), a hallmark feature of diabetes and metabolic syndrome, is characterized by chronic low-grade inflammation. Pan-immune-inflammation value (PIV), an emerging immune cell count-based inflammatory index, is the global quantifier of systemic inflammation. This study analyses the levels of PIV and its association with various markers of IR.

Materials and methods: This retrospective, cross-sectional study was done using the Center for Disease Control-National Health and Nutritional Examination Survey (CDC-NHANES) pre-pandemic data from 2017-2020. Data from 4620 survey participants was included after screening. Homeostasis model assessments of insulin resistance (HOMA-IR) and beta-cell function (HOMA-B), triglyceride glucose (TyG) index, visceral adiposity index (VAI), and lipid accumulation product (LAP) were used as markers of IR. Multiple logistic regression and trend analysis were done to determine the associations, and receiver operator characteristic curve (ROC) analysis was done to estimate the diagnostic utility of PIV to predict IR.

Results: PIV levels were significantly higher in obesity, diabetes, and metabolic syndrome. HOMA-IR, HOMA-B, LAP, VAI, and TyG levels were found to be higher in those with higher PIV (i.e., quartiles 4 and 3). Regression and trend analysis showed that the odds ratio for IR increased with PIV. However, ROC indicated that the diagnostic utility of PIV to predict IR is low compared to the other surrogate markers.

Conclusions: PIV levels differed significantly based on glycemic status, BMI, and metabolic syndrome status. PIV showed a significant positive association with IR. However, the ability of PIV to predict IR is not optimal compared to other surrogate markers.

Introduction: As Artificial Intelligence (AI) technology continues to assimilate into various industries, there is a huge scope in the healthcare industry specifically in clinical laboratories. The perspective of the laboratory professionals can give valuable insight on the ideal path to take for AI implementation.

Methods: The study utilized a cross-sectional survey design and was conducted at the section of Chemical Pathology, Department of Pathology and Laboratory Medicine, the Aga Khan University (AKU), Karachi, Pakistan in collaboration with Consultant Pathologists of 9 clinical laboratories associated with teaching hospitals across Pakistan from October-November 2023. The survey was for a duration of 2 weeks and was circulated to all working laboratory technical staff after informed consent.

Results: A total of 351 responses were received, of which 342 (male=146, female=196) responses were recorded after exclusion. Respondents ranged from technologists, faculty, residents, and coordinators, and were from different sections (chemical pathology, microbiology, haematology, histopathology, POCT). Out of the total 312 (91.2%) of respondents stated that they were at least somewhat familiar with AI technology. Experts in AI were only 2.0% (n=7) of all respondents, but 90% (n=6) of these were < 30 years old. 76.3% (n=261) of the respondents felt the need to implement more AI technology in the laboratories, with time saving (26.1%) and improving performances of tests (17.7%) cited to be the greatest benefits of AI. Security concerns (n=144) and a fear of decreasing personal touch (n=143) were the main concerns of the respondents while the younger employees had an increased fear of losing their jobs. 76.3% were in favour of an increase in AI usage in the laboratories.

Conclusion: This study highlights a favourable perspective among laboratory professionals, acknowledging the potential of AI to enhance both the efficiency and quality of laboratory practices. However, it underscores the importance of addressing their concerns in the thoughtful implementation of this emerging technology.