Clinical chemistry and laboratory medicine最新文献

Objectives: The Kappa Index has proven its diagnostic value for multiple sclerosis (MS), while its prognostic potential remains to be fully explored. The objective of this study is thus to investigate the value of the Kappa Index at disease onset in predicting disease activity and high-efficacy therapy (HET) initiation.

Methods: We enrolled MS patients with available Kappa Index values at disease onset and a follow-up of at least two years. Primary outcome was the time to loss of NEDA3 (no evidence of disease activity-3) defined as the absence of relapses, MRI activity, and disability progression. Secondary outcome was the time to HET initiation.

Results: Of 120 enrolled patients (36 M, 84 F, mean age: 35 ± 11 years), NEDA3 loss occurred in 89 (74 %) by the end of the follow-up period. A total of 98 (82 %) initiated a moderate efficacy therapy (MET); of these, 34 (28 %) transitioned to a HET during follow-up. Kappa Index values above the maximally selected log-rank statistic-derived cut-off of 38 were independent risk factors for NEDA3 loss (HR 1.75, 95 % CI: 1.09-2.80, p=0.021) and HET initiation (3.25, 95 % CI: 1.54-6.87, p=0.002) and also independently predicted HET following MET failure (2.54, 95 % CI: 1.17-5.51, p=0.018).

Conclusions: Elevated Kappa Index values at diagnosis predict disease activity, MET failure and HET initiation and may be a valuable adjunctive tool in identifying patients in need of prompt HET initiation.

Objectives: Compare Elecsys (Roche) and Simoa (Quanterix) immunoassays for serum glial fibrillary acidic protein (GFAP) using our reference database and Z scores, and evaluate their prognostic value for progression independent of relapse activity (PIRA) in multiple sclerosis (MS).

Methods: Platform correlation was assessed in 612 samples from healthy controls (n=188; median [interquartile range, IQR] age 45.1 [36.4-61.7] years) and people with MS (n=424; 45.3 [35.2-53.9] years). Elecsys values were converted to Z scores via Passing-Bablok-derived regression and validated in fingolimod (n=414), and B-cell depleting therapy (BCDT; n=353) cohorts. Z scores and hazard ratios (HRs) for time-to-PIRA were compared using Cox regression.

Results: GFAP_Simoa and GFAP_Elecsys measurements were correlated (r=0.94), with Elecsys values ∼54 % lower (GFAP_Elecsys, ng/L=2.847 [95 % confidence interval, CI: 1.335 - 4.98] + 0.457 [0.434 - 0.478] * GFAP_Simoa, ng/L). In univariable Cox models, GFAP_Simoa and GFAP_Elecsys Z scores were associated with time-to-PIRA in both validation cohorts. In multivariable Cox models, higher GFAP_Simoa Z scores were associated with shorter time-to-PIRA in fingolimod cohort (HR: 1.27 [95 % CI 1.08 - 1.50], p=0.0031) and trended toward significance in BCDT (1.18 [0.99 - 1.41, p=0.0693). In contrast, GFAP_Elecsys Z scores were associated with time-to-PIRA in both cohorts (fingolimod: 1.27 [1.09 - 1.48], p=0.0023; BCDT: (1.19 [1.00 - 1.40], p=0.0487).

Conclusions: Serum GFAP measured by Elecsys shows a comparable association with time-to-PIRA as Simoa, and GFAP_Simoa Z scores can be successfully bridged to GFAP_Elecsys Z scores, supporting Elecsys`s potential for clinical implementation.

Objectives: Despite standardization efforts, significant inter-assay variability persists among prostate-specific antigen (PSA) tests, impacting prostate cancer (PCa) diagnosis and monitoring. We aimed to evaluate the analytical and clinical performance of the newly developed Atellica IM high-sensitivity PSA (hsPSA Atellica) assay compared with established PSA assays.

Methods: A total of 236 serum samples from healthy individuals and patients with or without PCa were analyzed using the hsPSA Atellica assay and four FDA-approved PSA assays: Hybritech Access, Architect, Atellica, and Cobas. Analytical performance included limit of detection (LOD), limit of quantification (LOQ), linearity, inter-assay precision, and hook effect. Method comparison was performed using Passing-Bablok regression, Bland-Altman analysis, and kappa index concordance.

Results: The hsPSA Atellica assay demonstrated a LOD of 0.01 μg/L and LOQ of 0.028 μg/L (CV: 3.2 %, accuracy: 115 %). Precision was maintained across concentrations, with CVs of 4.2 %, 3.8 %, and 2.4 % at low, medium, and high levels of PSA. Strong agreement was observed with the compared tests, particularly with Cobas and Hybritech PSA assays. Diagnostic sensitivity and specificity at the 4 μg/L clinical decision threshold were 98 % and 35 %, respectively. In 87 samples between 3 and 10 μg/L, concordance between hsPSA Atellica and Hybritech reached 96.6 % (κ=0.82). The assay remained accurate up to PSA concentrations of 13,311 μg/L, showing minimal hook effect.

Conclusions: The hsPSA Atellica assay shows excellent analytical sensitivity and strong agreement with established assays. To our knowledge, this is the first published evaluation of this assay, supporting its clinical utility in both PCa diagnosis and follow-up.

Objectives: Neurofilament light chain (NfL) is a biomarker of neuroaxonal damage in various neurological conditions, including multiple sclerosis (MS). With new analytical platforms entering the market, standardization of serum NfL (sNfL) measurement is essential. This study compared sNfL levels across three assays - NF-light Advantage V2/Plus (Quanterix), Lumipulse G NfL blood (FujiRebio), and Elecsys NfL (Roche Diagnostics) - and derived conversion formulas for harmonization.

Methods: Serum samples from three cohorts including healthy donors (n=303) and MS patients (n=181) were analyzed across three platforms. Passing-Bablok regression and correlation analyses assessed inter-platform agreement and potential systematic differences. Assays were further evaluated for their ability to detect MS disease activity.

Results: All platforms showed strong correlations (Pearson>0.98; Spearman>0.90, p<0.001). Absolute values from Elecsys were ∼6-fold lower than the other assays. Fourteen of 484 samples (2.90 %) showed substantial, retest-stable deviations (>4 SD) on at least one platform. After excluding outliers, conversion formulas were derived to transform the values to the NF-light Advantage V2/Plus scale: NfL=6.31 × [Elecsys NfL] + 2.33 ng/L; NfL=0.94 × [Lumipulse G NfL] + 1.30 ng/L. In MS patients, all platforms detected significant differences between relapse and remission (p<0.001). Age-adjusted Z scores from converted values yielded the highest effect sizes (Cohen's d: 0.84 for NF-light Advantage, 1.11 for Elecsys, 0.93 for Lumipulse).

Conclusions: The three platforms demonstrated high correlation and comparable performance in sNfL quantification. About 3 % of samples showed strong deviations, highlighting the need for vigilance and further harmonization. Age-adjusted Z scores may enhance clinical and research utility.

Objectives: Free triiodothyronine (FT3) and free thyroxine (FT4) are important diagnostic markers for assessing thyroid function. However, their accurate quantification remains challenging due to low serum concentrations. Significant variability exists among current assay methods for measuring FT3 and FT4. This study aims to establish a candidate Reference Measurement Procedure (cRMP) for simultaneous quantification of serum FT3 and FT4 based on isotope-dilution liquid chromatography-tandem mass spectrometry (ID-LC-MS/MS) technology.

Methods: A convenient and reusable equilibrium dialysis (ED) device was utilized to separate free thyroid hormones from their protein-bound counterparts in serum. Key dialysis parameters, including temperature, pH, membrane type, and duration, were optimized to ensure consistent and reliable performance. The dialysate containing FT3 and FT4 was directly quantified by ID-LC-MS/MS. The method underwent systematic validation, comparative analysis with existing assays, and a comprehensive uncertainty assessment.

Results: The developed cRMP demonstrated limits of quantification (LoQ) of 1.54 pmol/L for FT3 and 3.22 pmol/L for FT4, and with an imprecision of less than 3 %. No interference from endogenous analogs was observed, and the method showed good consistency in interlaboratory comparison. In contrast, chemiluminescent immunoassay results exhibited poor agreement with and the cRMP.

Conclusions: This study developed a highly precise, accurate, specific, and sensitive ID-LC-MS/MS-based cRMP for the simultaneous measurement of FT3 and FT4 in human serum. This method provides a reliable tool for standardizing routine thyroid function tests.

Objectives: Prognostication in patients with severe acute brain injury (SABI) in the Intensive Care Unit (ICU) is complex, often requiring early decisions on life-sustaining therapy. Serum biomarkers such as neurofilament light (NFL), glial fibrillary acidic protein (GFAP) and neuron specific enolase (NSE) may aid in predicting outcomes. This study investigated the association between biomarker concentrations and hospital mortality in ICU patients with SABI.

Methods: In this prospective multi-center study, 103 adult ICU patients with SABI were enrolled within 24 h of injury. Serum concentrations (NFL, GFAP, NSE) were measured at 24 h, 72 h, 7 days, and 14 days post-injury. Biomarker concentrations between hospital survivors and non-survivors were compared using a linear mixed-effects model and ROC curves.

Results: GFAP concentrations peaked on day 1, while NSE and NFL concentrations peaked on day 14. Higher concentrations were observed in non-survivors for all biomarkers on day 3, a 1,4 fold increase for NSE (p=0.034) and approximately 2-fold increase for GFAP (p=0.041) and NFL (p=0.018). Distinct temporal patterns were observed for all biomarkers, although biomarker trajectories did not significantly differ between survivors and non-survivors. Prognostic accuracy was moderate when biomarkers were combined with established mortality predictors (AUC 0.749) on day 3.

Conclusions: NSE, GFAP and NFL show distinct temporal trajectories in SABI patients. Concentrations were higher in non-survivors during the early phase of admission. While individual biomarkers showed limited prognostic accuracy, in combination with other predictors mortality prediction improved. Serum biomarkers may still be valuable in a multimodal prognostication framework for SABI patients.

This scoping review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines to systematically map the current landscape of artificial intelligence (AI) and machine learning (ML) applications in the field of thrombosis and hemostasis (T&H), specifically targeting diagnostic enhancements in clinical and laboratory settings. Utilizing comprehensive searches across MEDLINE, EMBASE, Web of Science, and Scopus (2020-2025), 107 original studies met inclusion criteria and were analyzed. Clinical applications predominantly focused on predictive modelling for venous thromboembolism (VTE), pulmonary embolism (PE), deep vein thrombosis (DVT), anticoagulant management, and disease risk stratification, employing algorithms including neural networks, random forests, and gradient boosting. Laboratory-based AI implementations, though fewer, provided automated quality control, clot detection, and assay interpretation enhancements for potential better decision-making. Significant limitations addressed by the include studies include reliance on retrospective, single-center, small-sample datasets, limited external validation, model interpretability concerns, and integration challenges into clinical workflows. Persistent interdisciplinary disconnect between hemostasis domain experts and AI-ML specialists, compounded by regulatory hurdles, fragmented data, and labor-intensive data labelling processes, was highlighted as a major barrier to broader adoption. Recommendations for future research include developing large, externally validated multicenter datasets, transparent and interpretable ML models, prospective clinical validations, and user-centered integration strategies. Enhancing collaboration between laboratory scientists and AI-ML experts, establishing structured education programs, and creating regulatory frameworks are essential next steps to fully realize the potential of AI for significantly improving diagnostic accuracy, clinical decision-making, and patient management in T&H.