Clinical chemistry and laboratory medicine最新文献

The Sigma metric is widely used in laboratory medicine.

Objectives: This study aimed to deliver biological variation (BV) estimates for 25 types of lymphocyte subpopulations subjected to deep immunophenotyping (memory T/B cells, regulatory T cells, etc.) and classical, intermediate, and nonclassical monocyte subsets based on the full spectrum flow cytometry (FS-FCM) and a Biological Variation Data Critical Appraisal Checklist (BIVAC) design.

Methods: Samples were collected biweekly from 60 healthy Chinese adults over 10 consecutive two-week periods. Each sample was measured in duplicate within a single run for lymphocyte deep immunophenotyping and monocyte subset determination using FS-FCM, including the percentage (%) and absolute count (cells/μL). After trend adjustment, a Bayesian model was applied to deliver the within-subject BV (CV_I) and between-subject BV (CV_G) estimates with 95 % credibility intervals.

Results: Enumeration (% and cells/μL) for 25 types of lymphocyte deep immunophenotyping and three types of monocyte subset percentages showed considerable variability in terms of CV_I and CV_G. CV_I ranged from 4.23 to 47.47 %. Additionally, CV_G ranged between 10.32 and 101.30 %, except for CD4⁺ effector memory T cells re-expressing CD45RA. No significant differences were found between males and females for CV_I and CV_G estimates. Nevertheless, the CV_Gs of PD-1⁺ T cells (%) may be higher in females than males. Based on the desired analytical performance specification, the maximum allowable imprecision immune parameter was the CD8⁺PD-1⁺ T cell (cells/μL), with 23.7 %.

Conclusions: This is the first study delivering BV estimates for 25 types of lymphocyte subpopulations subjected to deep immunophenotyping, along with classical, intermediate, and nonclassical monocyte subsets, using FS-FCM and adhering to the BIVAC design.

Objectives: Clinical laboratories face limitations in implementing advanced quality control (QC) methods with existing systems. This study aimed to develop a web-based application to addresses this gap, and improve QC practices.

Methods: QC Constellation, a web application built using Python 3.11, integrates various statistical QC modules. These include Levey-Jennings charts with Westgard rules, sigma-metric calculations, exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) charts, and method decision charts. Additionally, it offers a risk-based QC section and a patient-based QC module aligning with modern QC practices. The codes and the web application links for QC Constellation were shared at https://github.com/hikmetc/QC_Constellation, and http://qcconstellation.com, respectively.

Results: Using synthetic data, QC Constellation demonstrated effective implementation of Levey-Jennings charts with user-friendly features like checkboxes for Westgard rules and customizable moving averages graphs. Sigma-metric calculations for hypothetical performance values of serum total cholesterol were successfully performed using allowable total error and maximum allowable measurement uncertainty goals, and displayed on method decision charts. The utility of the risk-based QC module was exemplified by assessing QC plans for serum total cholesterol, showcasing the application's capability in calculating risk-based QC parameters including maximum unreliable final patient results, risk management index, and maximum run size and offering risk-based QC recommendations. Similarly, the patient-based QC and optimization modules were demonstrated using simulated sodium results.

Conclusions: In conclusion, QC Constellation emerges as a pivotal tool for laboratory professionals, streamlining the management of quality control and analytical performance monitoring, while enhancing patient safety through optimized QC processes.

Identification of the molecular culprits of allergic reactions leveraged molecular allergology applications in clinical laboratory medicine. Molecular allergology shifted the focus from complex, heterogeneous allergenic extracts, e.g. pollen, food, or insect venom, towards genetically and immunologically defined proteins available for in vitro diagnosis. Molecular allergology is a precision medicine approach for the diagnosis, stratification, therapeutic management, follow-up and prognostic evaluation of patients within a large range of allergic diseases. Exclusively available for in vitro diagnosis, molecular allergology is nonredundant with any of the current clinical tools for allergy investigation. As an example of a major application, discrimination of genuine sensitization from allergen cross-reactivity at the molecular level allows the proper targeting of the culprit allergen and thus dramatically improves patient management. This review aims at introducing clinical laboratory specialists to molecular allergology, from the biochemical and genetic bases, through immunological concepts, to daily use in the diagnosis and management of allergic diseases.

Objectives: Laboratory medical reports are often not intuitively comprehensible to non-medical professionals. Given their recent advancements, easier accessibility and remarkable performance on medical licensing exams, patients are therefore likely to turn to artificial intelligence-based chatbots to understand their laboratory results. However, empirical studies assessing the efficacy of these chatbots in responding to real-life patient queries regarding laboratory medicine are scarce.

Methods: Thus, this investigation included 100 patient inquiries from an online health forum, specifically addressing Complete Blood Count interpretation. The aim was to evaluate the proficiency of three artificial intelligence-based chatbots (ChatGPT, Gemini and Le Chat) against the online responses from certified physicians.

Results: The findings revealed that the chatbots' interpretations of laboratory results were inferior to those from online medical professionals. While the chatbots exhibited a higher degree of empathetic communication, they frequently produced erroneous or overly generalized responses to complex patient questions. The appropriateness of chatbot responses ranged from 51 to 64 %, with 22 to 33 % of responses overestimating patient conditions. A notable positive aspect was the chatbots' consistent inclusion of disclaimers regarding its non-medical nature and recommendations to seek professional medical advice.

Conclusions: The chatbots' interpretations of laboratory results from real patient queries highlight a dangerous dichotomy - a perceived trustworthiness potentially obscuring factual inaccuracies. Given the growing inclination towards self-diagnosis using AI platforms, further research and improvement of these chatbots is imperative to increase patients' awareness and avoid future burdens on the healthcare system.

Objectives: This study performed an analytical validation study of the Mindray high-sensitivity cardiac troponin I (hs-cTnI) assay addressing limit of blank (LoB), limit of detection (LoD), precision, linearity, analytical specificity and sex-specific 99th percentile upper reference limits.

Methods: LoB, LoD, precision, linearity and analytical specificity were studied according to Clinical and Laboratory Standards Institute. We used one reagent lot and one CL1200i analyzer. Skeletal troponin I and T, cardiac troponin T, troponin C, actin, tropomyosin, myosin light chain, myoglobin and creatine kinase (CK-MB) were studied for cross-reactivity. Interference with biotin was examined. Lithium heparin samples (one freeze thaw cycle) from healthy males and females were measured to determine the 99th percentiles by using the non-parametric method. Analyses were performed before and after excluding subjects with clinical conditions and/or increased surrogate biomarkers.

Results: The Mindray hs-cTnI assay met criteria to be considered as a hs-cTn assay. LoB and LoD was <0.1 ng/L and 0.1 ng/L, respectively. Repeatability had a coefficient of variation 1.2-3.8 %, and within-laboratory imprecision 1.7-5.0 %. The measuring interval ranged from 1.1 to 28,180 ng/L. The analytical specificity was clinically acceptable for the interferents studied. After exclusions, the 99th percentile URLs obtained were 10 ng/L overall, 5 ng/L for females and 12 ng/L for males.

Conclusions: Analytical observations of the Mindray hs-cTnI assay demonstrated excellent LoB, LoD, precision, linearity and analytical specificity, that were in alignment with the manufacturer's claims and regulatory guidelines for hs-cTnI. The assay is suitable for clinical investigation for patient-oriented studies.

Objectives: Even in the current era of hematology analyzer automation and peripheral equipment, quality control sample measurement remains a manual task, leading to variability in quality control data and increased workload. In this study, we evaluated the performance of quality control measurement using the BT-50 Transportation Unit (BT-50, Sysmex, Kobe, Japan), equipped with a scheduled automatic quality control function, to ensure measurement accuracy and streamline the workflow of hematology testing.

Methods: We evaluated the automatic measurement performance of quality control samples using the BT-50 for six representative blood test parameters: WBC (white blood cell), RBC (red blood cell), HGB (hemoglobin), HCT (hematocrit), PLT (platelet), and RET% (reticulocyte percent). We evaluated the equivalence and compared measurement accuracy between the BT-50 and the manual method. We then compared the variability to other laboratories and confirmed the stability of quality control samples. We also evaluated changes in workflow and staff resources before and after the introduction of the BT-50.

Results: The quality control measurement results for the BT-50 and the manual method were found to be equivalent for all six parameters. The variability measured by the BT-50 was lower for some parameters compared to the manual method. Furthermore, the workflow was streamlined by reducing manual processes, resulting in increased efficiency.

Conclusions: We confirmed the performance of quality control measurements using the schedule function of the BT-50. Introducing the BT-50 reduced the operator's workload, improved operational efficiency, and promoted the standardization of quality control measurements.