Differences between capillary and venous blood counts in children-A data mining approach - Becker, Mathias; Gscheidmeier, Thomas; Gross, Hans-Juergen; Cario, Holger; Wölfle, Joachim; Rauh, Manfred; Metzler, Markus; Zierk, Jakob (International Journal of Laboratory Hematology, 2022)
Background Capillary sampling of blood counts is a well-established alternative to venipuncture in paediatrics. However, the sampling method has to be considered when interpreting test results, as measurements differ. Ethical and practical considerations prevent simultaneous venous and capillary sample acquisition in comprehensive paediatric cohorts that span all ages for the purpose of a direct method comparison, resulting in uncertainty regarding the interpretation of capillary test results. Methods We applied a data mining method to calculate the differences between capillary and venous blood count analytes using laboratory data collected during patient care. We examined 486 401 blood counts performed between 2010 and 2017 in two German paediatric tertiary care centers in children from birth to 18 years analysed on SYSMEX XE-2100 and SYSMEX XE-5000 devices, and analysed the differences between capillary and venous test results in 15 218 paired samples performed within 24 h. Results We identified the mean systematic differences between capillary and venous (capillary-venous) test results for haemoglobin (+6.5 g/L), haematocrit (+2.38%), platelet count (-7.01 x 10(9)/l), red cell count (+0.18 x 10(12)/L), white cell count (-0.64 x 10(9)/L), mean corpuscular cell volume (+2.07 fl), mean corpuscular haemoglobin (+0.33 pg), mean corpuscular haemoglobin concentration (-4.4 g/L) and red cell distribution width (+0.40%). The effect of age on these mean deltas is negligible, while the levels of test results influence the difference between capillary and venous test results in most analytes. Conclusions Our results improve guidance regarding the interpretation of capillary test results for children of all ages and in both physiological and pathological ranges.
CRIS-Team WoS Importer:2022-05-13

Prediction of phenotypes by secretory biomarkers and machine learning in patients with chronic rhinosinusitis - Becker, Mathias; Kist, A. M.; Wendler, O.; Pesold, V. V.; Bleier, B. S.; Mueller, S. K. (European Review for Medical and Pharmacological Sciences, 2025)
OBJECTIVE: Chronic rhinosinusitis (CRS) has traditionally been classified phenotypically according to the presence (CRSwNP) or absence (CRSsNP) of nasal polyps. However, the phenotypic dichotomy does not represent the complexity of the disease. Current research thus focuses on identifying underlying inflammatory mechanisms and distinguishing different endotypes. The objectives of this study were 1) to identify maximally predictive non-invasive biomarkers from nasal mucus, 2) to apply machine learning algorithms to use mucus-derived biomarkers to classify phenotype, and 3) to determine the feature importance of each mucus biomarker to phenotypes. PATIENTS AND METHODS: This is an IRB-approved study of 103 CRS patients (37 CRSsNP, 66 CRSwNP). Nasal mucus was collected using merocele sponges after a 3-week steroid washout period. The nasal mucus was then examined for twelve cytokines/inflammatory protein biomarkers, including interferon (IFN)-γ, interleukin (IL)-4, -5, -17A, -22, immunoglobulin (Ig) E, cystatin-SA (CST-2), eosinophilic cationic protein (ECP), matrix metalloproteinase-9 (MMP-9), pappalysin-A (PAPP-A), periostin, and serpin E1. Protein concentrations were determined by ELISAs and Luminex assays. For phenotype classification, different artificial intelligence algorithms in increasing complexity, including t-distributed stochastic neighbor embedding (t-SNE), Adaboost, and XGBoost, were applied to the data from the biomarker analysis. RESULTS: TThe analysis showed that IL-5 is a non-invasive marker to distinguish between the two phenotypic clusters. This was true for immune cell-derived proteins, and all proteins were analyzed conjointly. Periostin and CST-2 showed the highest feature importance for the epithelial- and tissue-derived proteins. The combination of IL-5, IgE, IL-17, and periostin showed the highest accuracy for prediction. CONCLUSIONS: Nasal mucus can predict phenotypes similar to tissue, with IL-5 as the main trigger for clustering. Periostin and CST-2 may be part of important targetable pathways. Future efforts will be directed at determining how these markers may be used to guide therapeutic choices and individualize treatment.
CRIS-Team Scopus Importer:2025-02-28