Clinical chemistry and laboratory medicine最新文献

Objectives: Clinical laboratories play a vital role in healthcare but are also among the most resource-intensive hospital units. High energy and water consumption, extensive use of single-use plastics, and the generation of non-recyclable biomedical waste present major environmental challenges. This study aimed to quantify the environmental and economic burden associated with common and avoidable practices in clinical laboratory medicine, including: excessive blood collection, inappropriate test ordering, and unnecessary tube duplication in pre-analytical workflows.

Methods: Real-world data from the Clinical Biochemistry Laboratory of the AOU Maggiore della Carità (Novara, Italy) for the year 2024 were analysed to quantify the environmental and economic impact of inappropriate test ordering based on minimum retesting intervals, as well as pre-analytical practices, including redundant sample tube collection and excessive blood volumes relative to analytical requirements.

Results: Inappropriate retesting of selected biomarkers resulted in 2,931 unnecessary tubes, generating over 26.7 kg of avoidable waste. Redundant EDTA tubes for complete blood count, glycated haemoglobin, erythrocyte sedimentation rate, and haemoglobin electrophoresis led to 356.14 kg of excess waste, which could be eliminated through sample consolidation. In haematology, 264,834 CBC tests generated 2.62 tonnes of biological waste. For biochemistry, 3 million tests produced 36.98 tonnes of waste.

Conclusions: Excessive blood collection and unnecessary test repetition contribute to significant but avoidable environmental and financial burdens. Pre-analytical interventions, such as reducing tube volume and consolidating tests, could prevent over 40 tonnes of waste and save more than € 60,800 annually in a single laboratory.

Objectives: Antiphosphatidylserine/prothrombin (aPS/PT) antibodies are promising non-criteria aPLs associated with antiphospholipid syndrome (APS) manifestations. Their influence on long-term outcomes and damage remains uncertain. To assess aPS/PT antibodies association with accrual damage and severe APS disease phenotypes, focusing on their potential for risk stratification.

Methods: This single-centre cohort study involved 163 patients who fulfilled the 2023 ACR/EULAR APS criteria. IgG/IgM aPS/PT, aCL, and anti-β2GPI antibodies were measured by commercial ELISA. Lupus anticoagulant (LA) was detected in accordance with the ISTH-SSC recommendation. Clinical data covered macrovascular, microvascular, obstetric, and hematologic manifestations. Damage was assessed using DIAPS.

Results: aPS/PT IgG and IgM were positive in 46.0 % and 65.6 %, respectively. aPS/PT IgG positivity was linked to microvascular involvement (58.7 % vs. 34.1 %, p=0.002), obstetric APS (57.4 % vs. 24.6 %, p<0.0001), valvular disease (17.3 % vs. 4.6 %, p=0.008), and thrombocytopenia (37.3 % vs. 12.5 %, p<0.0001). aPS/PT IgM positivity was also associated with microvascular APS (p=0.006), obstetric morbidity (p=0.02), and thrombocytopenia (p=0.01). In multivariable analysis, quadruple aPL positivity (LA + aCL + anti-β2GPI + aPS/PT) was independently linked to damage (OR 4.3, 95 % CI 1.3-14.5; p=0.02), with no other factors remaining significant.

Conclusions: Both IgG and IgM aPS/PT antibodies are associated with severe APS features, including microvascular and obstetric issues, as well as overall organ damage. Adding aPS/PT to standard aPL tests finds a subgroup of quadruple-positive APS patients at higher risk of damage. These results could help guide personalized APS management.

Objectives: Despite growing interest in artificial intelligence (AI) and machine learning (ML), many laboratory professionals lack experience with developing in-house AI systems or implementing those supplied by external providers. The IFCC Committee on AI in Laboratory Medicine (C-AILM) conducted a survey to collect the status of AI/ML applications, challenges, and expert perspectives on key technical considerations.

Methods: An 20-item survey was distributed to laboratory professionals experienced in AI. It covered application status (in-house or provider-supplied, with or without regulatory approval); essential information to request from AI system providers; validation or verification practices; monitoring strategies; and perceived implementation challenges.

Results: Fifty complete responses from global experts were received. AI implementation in clinical laboratories was limited and heterogeneous. Most respondents agreed that AI systems provided externally, regardless of regulatory approval status, require local verification. Key information needed from providers included performance metrics from original and external datasets, and demographics of the training/test populations. For both approved and non-approved models, high-priority verification studies were local performance analysis, confirmation of intended-use alignment, and verification of privacy and security safeguards. Top monitoring strategies were regular accuracy checks and comparison against human decision-making. Leading challenges were insufficient IT infrastructure and lack of practical implementation guidelines.

Conclusions: Although many challenges remain, clinical laboratories demonstrate strong enthusiasm for AI, particularly with the growing prevalence of commercial AI products. The timely expert insights from our survey and C-AILM recommendations for both AI system providers and clinical laboratories on essential information, verification requirements, and monitoring strategies will inform standardized guideline development.

Recent advances in blood-based biomarkers for neurodegenerative diseases present promising opportunities for earlier diagnosis and more effective disease monitoring. However, challenges in standardizing sample collection, processing protocols, and data reporting continue to hinder reproducibility, limit cross-study comparability, and delay clinical adoption. As efforts toward harmonization progress (supported by technological innovation and integration into multimodal diagnostic strategies), blood biomarkers are expected to transition from research settings to routine clinical use, bringing precision medicine closer to individuals affected by these disorders.

Objectives: Diagnosing atypical IgG4-mediated anti-glomerular basement membrane (anti-GBM) disease is challenging because conventional serological assays poorly detect IgG4 antibodies. Here we study which commercial assays are affected and we describe proof-of-concept of improved IgG4 anti-GBM detection.

Methods: To investigate the scope of the diagnostic dilemma detecting IgG4 anti-GBM antibodies, serum from a patient with atypical IgG4-mediated anti-GBM was distributed to 36 laboratories participating in the Dutch External Quality Assessment (EQA) program. To improve IgG4 anti-GBM detection in the fluorescent enzyme immunoassay (FEIA), the standard anti-IgG conjugate was replaced with anti-IgG4 conjugate.

Results: We report the diagnostic delay of a patient with atypical anti-GBM disease who presented with an indolent disease course. Histopathology comprised hallmarks of classic anti-GBM disease including bright linear IgG deposits along the GBM but without the typical findings of diffuse crescentic and necrotizing glomerulonephritis. IgG anti-GBM test results were repeatedly negative. Histopathological subclass analysis demonstrated that the linear IgG deposits were predominantly IgG4. All 36 Dutch EQA-participants reported negative anti-GBM test results, demonstrating that the diagnostic challenge of detecting IgG4 anti-GBM is broadly applicable across multiple diagnostic assays. A minor modification to the manufacturer's standard protocol of the FEIA anti-GBM dramatically improved the assay's performance for measuring antibodies of the IgG4 isotype. Using the modified IgG4-GBM test, we were able to diagnose and monitor one more patient with atypical IgG4-mediated anti-GBM disease.

Conclusions: Here we demonstrate proof-of-concept of a modified IgG4 anti-GBM blood test allowing serological confirmation of atypical anti-GBM disease and sensitive monitoring of therapy response.

Objectives: Newborn screening for congenital hypothyroidism (CH) relies on thyroid-stimulating hormone (TSH) levels from dried blood spots (DBS). We investigated whether incorporating TSH variation across serial DBS could improve prediction of CH in term and preterm infants with complex risk profiles.

Methods: Among 207,895 newborns screened, 272 (0.13 %) were diagnosed with CH. TSH variations across 3 serial DBS were analyzed in 6,146 healthy infants (2.96 %). Predictive algorithms were developed using linear mixed-effects models in 1,968 term and 1,387 preterm infants with ≥2 and ≥3 DBS, respectively. Average DBS collection times were 58, 260, and 478 h for term and 63, 350, and 664 h for preterm infants. TSH was measured by GSP neonatal hTSH assay (Revvity).

Results: Daily TSH variation was influenced by the initial DBS1 value. For DBS1 >5.2 and <1.7 mUI/L, increase and decrease, respectively, in TSH level on DBS2 is detectable, and this particularly occurs in preterm infants. In preterms, CH could be excluded when TSH remained <5 mUI/L on DBS1, <6 mUI/L on DBS2, <4 mUI/L on DBS3, and with daily variation <12 % from DBS1 to DBS3 (sensitivity 100 %; specificity 77.85 %). Term infants with TSH <11 mUI/L on DBS1, <4.5 mUI/L on DBS2, and daily variation <13 % from DBS1 to DBS2 may be ruled out for CH (sensitivity 96.5 %; specificity 66.6 %).

Conclusions: Distinct predictive algorithms for term and preterm newborns, incorporating TSH variations as daily percentage changes, may improve CH rule out in children with complex risk profiles.

Objectives: Accurate B-lineage assignment in acute leukemia is critical for therapeutic decisions. While nuclear expression of PAX5 protein (nPAX5) is a highly reliable marker for B-cell differentiation, its conventional assessment by time-consuming immunohistochemistry (IHC) is not ideal for rapid diagnosis. This study aimed to validate the utility of flow cytometric (FCM) detection of nPAX5 for the diagnosis of B-lymphoblastic leukemia (B-ALL), potentially enhancing diagnostic efficiency.

Methods: A retrospective study was conducted, comparing nPAX5 expression by FCM and IHC in 125 bone marrow biopsies (57 B-ALL, 12 T-ALL, 56 AML). Further diagnostic performance analysis, using ROC analysis of the geometric mean fluorescence intensity ratio (Blast/Normal T-cell), was performed for nPAX5, cCD22, and cCD79a across a larger cohort of 538 acute leukemia cases (123 B-ALL, 29 T-ALL, 386 AML).

Results: FCM and IHC for PAX5 expression showed 100 % concordance across the 125 cases (57/57 B-ALL positive; 0/12 T-ALL negative). The single PAX5-positive AML case harbored the t(8;21) translocation. ROC analysis demonstrated excellent diagnostic performance for both cCD79a (AUC 0.980) and nPAX5 (AUC 0.955). At optimal criteria, cCD79a achieved 100.00 % specificity and 91.06 % sensitivity. nPAX5 showed strong utility, matching the 91.06 % sensitivity with 89.45 % specificity (criterion >6.30). cCD22 exhibited moderate discriminatory power (AUC 0.807).

Conclusions: Flow cytometric nuclear PAX5 (nPAX5) demonstrated 100 % agreement with IHC and exhibited excellent diagnostic accuracy (AUC 0.955, 91.06 % sensitivity). These compelling results validate nPAX5 as a reliable and powerful FCM marker, supporting its immediate adoption for rapid and efficient B-lineage assignment in acute leukemia diagnostics.

Objectives: Testing for high-sensitivity cardiac troponin (hs-cTn) often occurs following pediatric cardiac surgery, although evidence regarding its utility remains heterogeneous. This study aimed to assess the prognostic value of postoperative hs-cTnT patterns detected within 48 h after cardiac surgery.

Methods: Serum hs-cTnT (Roche Diagnostics, 5th generation assay) concentrations were measured post-operatively at three time points: upon pediatric intensive care unit (PICU) admission (T1), 13 h after admission (T2), and 24 h after T2 (T3). Surgical and postoperative variables were recorded. The outcome was a composite of 30-day mortality and/or PICU stay >10 days. Multivariable logistic regression and model discrimination were evaluated.

Results: Over 15 months, 154 patients (56.5 % male) with a median age of 3.8 (25th-75th percentile: 1.2-7.8) months and a median Risk Assessment for Congenital Heart Surgery score of 2, were included in this study. The outcome occurred in 24 % of the population (with the 30-day mortality rate being 7.8 %). The time point of the highest recorded hs-cTnT concentration, and not the concentration, was significantly associated with the outcome (p=0.001). Patients, whose peak hs-cTnT concentration occurred later after surgery had a sixfold higher risk of the adverse outcome. Urgent/emergent procedures were associated with a 3.6-fold increase in relative risk, and each additional minute of cardiopulmonary bypass (CPB) time conferred a 1.6 % incremental increase in risk. The multivariate model including time to troponin peak, CPB duration, need for urgent/emergent procedures showed a good discriminatory ability (c-statistic=0.84; 95 %CI: 0.78-0.90).

Conclusions: Timing of hs-cTnT elevation post cardiac surgery is a valuable prognostic marker in children.

Objectives: The reliability of cerebrospinal fluid (CSF) and serum biomarkers depends on sample integrity, which is strongly influenced by storage conditions. While -80 °C is considered the gold standard for long-term preservation, it requires specialized infrastructure, whereas -20 °C is more accessible but may compromise protein stability. This study aimed to evaluate the stability of three clinically relevant biomarkers - neuron-specific enolase (NSE), S100, and β-2 microglobulin (B2M), associated with neuronal injury and neuroinflammation - in CSF and serum samples stored at -20 °C and -80 °C over different time periods.

Methods: A total of 63 CSF and 56 serum samples from pediatric patients were analyzed. Samples were stored at -20 °C (24 h and 6 months) and -80 °C (6 and 12 months). NSE and S100 were measured by electrochemiluminescence immunoassays and B2M by turbidimetric immunoassay. Biomarker variability was expressed as relative percentage change from baseline. A maximum permissible error of ±15 % for CSF and ±10 % for serum was selected.

Results: NSE and S100 were highly unstable at -20 °C. In CSF, after 6 months, NSE decreased by ∼74 % and S100 by ∼71 %; in serum, NSE declined by ∼54 % and S100 by ∼13 %. Remarkably, after only 24 h at -20 °C, CSF NSE dropped by 61 %. Storage at -80 °C largely preserved these two biomarkers, with declines below ∼15 % over 12 months. B2M levels remained stable under all conditions. The reduction in CSF NSE at -20 °C was strongly correlated with baseline concentrations.

Conclusions: CSF biomarkers NSE and S100 are highly susceptible to degradation at -20 °C, whereas B2M remains relatively stable. Strict adherence to -80 °C storage protocols is essential to ensure the reliability of biomarker-based diagnostics and research.

Objectives: This study aims to explore the biological variability of calcium homeostasis biomarkers, including calcium, phosphate, magnesium, 25-OH vitamin D, 1,25-OH vitamin D, and parathyroid hormone (PTH), and to develop a model that predicts PTH concentrations based on these parameters.

Methods: Thirty healthy volunteers were sampled on a weekly basis over a period of 10 weeks. Each sample was analyzed in duplicate. Statistical methods included estimating within- and between-subject biological variation (CV_I, CV_G) using ANOVA with outlier removal and a Bayesian model. A linear mixed-effects model (LMM) was used to estimate 95 % prediction intervals for PTH as a function of calcium, phosphate, and 25-OH vitamin D in healthy participants. The effect of applying this prediction model in routine practice was estimated using data from the Laboratory Information System.

Results: Estimated CV_I from ANOVA and the Bayesian model was: PTH, 18.0 % and 17.4 %; calcium, 1.7 % and 1.6 %; phosphate, 9.3 % and 9.3 %; 25-OH vitamin D, 4.7 % and 7.2 %; and 1,25-OH vitamin D, 16.2 % and 17.1 %. The LMM indicated the best 95 % prediction interval for PTH included calcium (PTH (pmol/L)=exp(3.46-2.67·ln(Ca (mmol/L)) ± Z·0.29)) and did not improve with phosphate and/or 25-OH vitamin D. Compared with conventional reference intervals, this model flagged 56 % vs. 41.6 % of routine PTH/calcium results, respectively.

Conclusions: Similar CV_I data were obtained using two different methods. A predictive model can be used to predict concentrations for co-regulated biomarkers, potentially enhancing sensitivity in identifying non-physiological results and facilitating clinicians' awareness of conditions affecting calcium homeostasis.