Clinical chemistry and laboratory medicine最新文献

Objectives: The aim of this study was to evaluate the impact of introducing an autonomous courier in a clinical laboratory, focusing on specimen turnaround time (TAT). We assessed whether high-frequency robotic transport from the central accessioning area to analytical sections could reduce delays and variability compared with manual delivery.

Methods: We retrospectively analyzed routine lithium-heparin glucose specimens processed during the initial days of robot deployment (13-23 May 2025, weekdays, 08:00-16:00) and compared them with the same period in 2024 under manual transport. TAT was defined as the interval from check-in at accessioning to technical validation in the Laboratory Information System (LIS). Data were assessed globally and stratified by time of day, examining changes in central tendency, dispersion, and extreme delays.

Results: In total, 6,299 samples in 2024 and 5,759 in 2025 were analyzed, showing a leftward shift of the distribution with fewer delay. In 2025, the mean TAT decreased from 122.64 to 106.72 min, the median from 112 to 101 min, and dispersion tightened. Stratification by time of day also demonstrated consistent improvements.

Conclusions: Even in its earliest days of operation, the delivery robot reduced TAT and variability, converting specimen transport from batch runs into near-continuous flow. These findings highlight ease of adoption and the potential of robotic transport to improve speed, predictability, and safety in intralaboratory logistics. Further validation across longer periods and additional laboratory sections is warranted.

Objectives: Accurate determination of blood potassium levels in neonates presents significant clinical challenges, primarily attributable to technical difficulties in phlebotomy procedures and the elevated susceptibility of neonatal blood samples to hemolysis. This study aimed to develop and validate a hemoglobin-based mathematical model to correct for hemolysis-induced pseudohyperkalemia in neonatal blood samples.

Methods: This prospective study analyzed 134 neonatal blood specimens (postnatal age ≤7 days). Controlled hemolysis induction established the potassium-hemoglobin relationship, with specimens allocated to model development (n=101) and validation (n=33) cohorts. The model utilized direct hemoglobin quantification and multivariate regression, with statistical analyses performed using SPSS 26.0.

Results: Analysis of the training cohort established a neonatal-specific potassium release coefficient (ΔK⁺/ΔHb=0.28 ± 0.03 mmol/L per g Hb) and revealed a strong linear correlation between hemoglobin concentration and potassium elevation (Y=0.2834X, r²=0.8606, p<0.0001). Method validation showed strong agreement between constant correction and model-predicted values (p=0.887). Independent validation confirmed clinical utility, demonstrating comparable corrected and baseline potassium concentrations (p=0.0693) with excellent correlation (r=0.8845, p<0.0001). The model improved hypokalemia detection from 8.9% to 26.7 %, effectively resolving pseudo-normalization artifacts.

Conclusions: The developed hemoglobin-driven correction model provides a validated and accurate method for hemolysis interference in neonatal potassium measurement, offering significant clinical value for managing irreplaceable specimens in neonatal intensive care settings.

Objectives: This study aimed to perform an extended analytical verification of the Immunoglobulin Isotypes (GAM) for the EXENT^® Analyzer (EXENT^®-GAM) assay, a MALDI-TOF mass spectrometry-based method for detecting and quantifying serum M-proteins in patients with plasma cell dyscrasias, and to compare it with conventional serum protein electrophoresis (SPEP), serum immunofixation electrophoresis (sIFE) and serum free light chains (sFLC) assays.

Methods: Imprecision, linearity, limit of quantification (LOQ), quantification comparison with SPEP, isotyping concordance with sIFE and sFLC, interference from therapeutic monoclonal antibodies (t-mAbs), sample stability, and reagent lot consistency were evaluated.

Results: EXENT^®-GAM demonstrated acceptable imprecision (CV ≤20 % for low and ≤15 % for high M-protein levels) and wide linear range (∼0.03-30 g/L). The polyclonal immunoglobulin background negatively influenced the assays LOQ. M-proteins with mass-shifted light chains (i.e., glycosylated light chains) are prone to non-linearity and inferior LOQ. M-protein quantification by EXENT^® differed systematically and proportionally from quantification by SPEP, highlighting non-interchangeability. EXENT^® demonstrated 97 % concordance with sIFE for M-protein isotyping and identified numerous additional (low-level) M-proteins. Some proved to be clinically relevant (residual disease in sIFE-negative samples); others lacked correlation with sFLC result or clinical diagnosis. EXENT^® reliably distinguished endogenous M-proteins from t-mAbs, except talquetamab, which interfered with quantification and was partially misclassified.

Conclusions: EXENT^®-GAM enables sensitive and reproducible quantification and isotyping of M-proteins below the detection limit of SPEP and sIFE. Its ability to resolve analytical challenges posed by SPEP and sIFE represents a significant advancement. Further clinical studies are needed to confirm its potential in residual disease detection.

Objectives: Progesterone regulates reproductive processes and is used clinically to monitor ovarian function in people experiencing fertility problems. Measuring serum progesterone is challenging as it is highly protein-bound and exists at very low physiological levels. An isotope dilution-liquid chromatography-tandem mass spectrometry-based candidate RMP to quantify progesterone in human serum/plasma has been developed.

Methods: To ensure traceability to the SI Units, this RMP utilized primary reference material from the NMIJ. For the determination of progesterone, two-dimensional heart-cut chromatography, in combination with a straightforward protein precipitation protocol, was employed to minimize matrix effects and the coelution of isobaric interferences. Accuracy and precision of the candidate RMP was assessed in a multi-day validation experiment using certified secondary reference materials, spiked serum and plasma samples; measurement uncertainty was evaluated according to the GUM. Equivalence to JCTLM-listed RMPs was determined using leftover samples from the RELA scheme.

Results: The candidate RMP was highly selective for progesterone within a measurement range of 0.0400-72.5 ng/mL (0.127-231 nmol/L) and matrix independent. Intermediate precision was ≤3.3 %, and repeatability ranged from 1.4 to 2.7 % across all concentration levels. The mean bias ranged from 0.1 to 0.7 % for secondary certified reference materials, from -1.6 % to -0.2 % for serum samples, and from -2.3 to 4.0 % for plasma samples. Expanded measurement uncertainty (k=2) for target value assignment (n=6) was found to be ≤3.7 %. Equivalence to JCTLM-listed RMPs demonstrated a relative bias of -2.4 to 2.2 %, all within the measurement uncertainty of the respective RMP.

Conclusions: A candidate RMP based on ID-LC-MS/MS for progesterone quantification is presented, providing metrological traceability, target value assignment, routine test standardization, and the analysis of clinical samples comprising human serum and plasma to ensure the accuracy and traceability of individual patient results.

Laboratory errors are an important component of medical errors and are predominantly associated with the extra-analytical phases of the total testing process, particularly the pre-pre-analytical and post-post-analytical phases, which are largely dependent on clinical activities but also require laboratory support. The post-post-analytical phase is the stage in which clinicians interpret patients' laboratory data using population-based reference data, such as population-based reference intervals (popRIs) and population-based decision limits (popDLs), typically provided by the laboratory. To minimize errors in this phase, it is essential that more accurate tools - such as personalized reference intervals (prRIs) and personalized decision limits (prDLs) - are made available to clinicians. However, population-based references are still widely used, whereas their personalized counterparts have not yet been implemented in routine practice. The discrepancy between inadequate population-based references and more appropriate personalized references can introduce systematic yet latent errors in the interpretation of patients' laboratory data, potentially compromising patient safety even when clinicians are highly competent in data interpretation. In this opinion paper, we (1) summarize the limitations of popRIs and popDLs, (2) develop the concept of latent errors, and (3) discuss how personalized RIs and personalized DLs can be used to reduce latent errors and enable more accurate interpretation of patient laboratory data.