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Numerous studies have proven the potential of cytokeratin 19 fragment 21-1 (CYFRA 21-1) detection in the (early) diagnosis and treatment monitoring of non-small cell lung cancer (NSCLC). Conventional immunoassays for CYFRA 21-1 quantification are however prone to interferences and lack diagnostic sensitivity and standardization. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is an emerging approach based on a different, often superior, detection principle, which may improve the clinical applicability of CYFRA 21-1 in cancer diagnostics. Therefore, we developed and validated a protein precipitation, immunoaffinity (IA) LC-MS/MS assay for quantitative analysis of serum CYFRA 21-1. Selective sample preparation was performed using ammonium sulfate (AS) precipitation, IA purification, tryptic digestion and LC-MS/MS quantification using a signature peptide and isotopically labeled internal standard. The workflow was optimized and validated according to EMA guidelines and results were compared to a conventional immunoassay. Significant interference effects were seen during IA purification, which were sufficiently solved by performing AS precipitation prior to IA purification. A linear calibration curve was obtained in the range of 1.0–100 ng/mL (R2=0.98). Accuracy and precision were well within acceptance criteria. In sera of patients suspected of lung cancer, the method showed good correlation with the immunoassay. A robust AS precipitation-IA LC-MS/MS assay for the quantification of serum CYFRA 21-1 was developed. With this assay, the clinically added value of LC-MS/MS-based detection over immunoassays can be further explored. [ABSTRACT FROM AUTHOR]

Background: Heart failure (HF) biomarkers have prognostic value. The aim of this study was to combine HF biomarkers into an objective classification system for risk stratification of patients with HF. Methods: HF biomarkers were analyzed in a population of HF outpatients and expressed relative to their cut-off values (N-terminal pro-B-type natriuretic peptide [NT-proBNP] >1,000 pg/mL, soluble suppression of tumorigenesis-2 [ST2] >35 ng/mL, growth differentiation factor-15 [GDF-15] >2,000 pg/mL, and fibroblast growth factor-23 [FGF-23] >95.4 pg/mL). Biomarkers that remained significant in multivariable analysis were combined to devise the Heartmarker score. The performance of the Heartmarker score was compared to the widely used New York Heart Association (NYHA) classification based on symptoms during ordinary activity. Results: HF biomarkers of 245 patients were analyzed, 45 (18%) of whom experienced the composite endpoint of HF hospitalization, appropriate implantable cardioverter-defibrillator shock, or death. HF biomarkers were elevated more often in patients that reached the composite endpoint than in patients that did not reach the endpoint. NT-proBNP, ST2, and GDF-15 were independent predictors of the composite endpoint and were thus combined as the Heartmarker score. The event-free survival and distance covered in 6 minutes of walking decreased with an increasing Heartmarker score. Compared with the NYHA classification, the Heartmarker score was better at discriminating between different risk classes and had a comparable relationship to functional capacity. Conclusions: The Heartmarker score is a reproducible and intuitive model for risk stratification of outpatients with HF, using routine biomarker measurements. [ABSTRACT FROM AUTHOR]

Background: The metabolic health index (MHI) is a biomarker-based model that objectively assesses the cumulative impact of comorbidities type 2 diabetes mellitus, hypertension and dyslipidemia on the health state of bariatric patients. The MHI was developed on a single-center cohort using a fully laboratory data-driven approach, resulting in a MHI score on a range from 1 to 6. To show universal applicability in clinical care, the MHI was validated externally and potential laboratory-related shortcomings were evaluated. Methods: Retrospective laboratory and national bariatric quality registry data were collected from five Dutch renowned bariatric centers (n = 11 501). MHI imprecision was derived from the cumulative effect of biological and analytical variance of the individual input variables of the MHI model. The performance of the MHI (model) was assessed in terms of discrimination and calibration. Results: The cumulative imprecision in MHI was 0.25 MHI points. Calibration of the MHI model diverged over the different centers but was accounted for by misregistration of comorbidity after cross-checking the data. Discriminative performance of the MHI model was consistent across the different centers. Conclusions: The MHI model can be applied in clinical practice of bariatric centers, regardless of patient mix and analytical platform. Because the MHI is based on objective parameters, it is insensitive to diverging clinical definitions of comorbidities. Therefore, the MHI can be used to objectify severity of metabolic comorbidities in bariatric patients. The MHI can support the patient-selection process for surgery and objectively assessing the effect of surgery on the metabolic health state. [ABSTRACT FROM AUTHOR]