Integrating chemokines and machine learning algorithms for diagnosis and bleeding assessment in primary immune thrombocytopenia: A prospective cohort study.
Journal:
British journal of haematology
PMID:
39253817
Abstract
Primary immune thrombocytopenia (ITP) is an autoimmune bleeding disorder, and chemokines have been shown to be dysregulated in autoimmune disorders. We conducted a prospective analysis to identify potential chemokines that could enhance the diagnostic accuracy and bleeding evaluation in ITP patients. In the discovery cohort, a Luminex-based assay was employed to quantify concentrations of plasma multiple chemokines. These levels were subjected to comparative analysis using a cohort of 60 ITP patients and 17 patients with thrombocytopenia other than ITP (non-ITP). Additionally, comparative evaluation was conducted between a subgroup of 12 ITP patients characterised by bleeding episodes (ITP-B, as defined by an ITP-2016 bleeding grade ≥2) and 33 ITP patients without bleeding episodes (ITP-NB, as defined by an ITP-2016 bleeding grade ≤1). Machine learning algorithms further identified CCL20, interleukin-2, CCL26, CCL25, and CXCL1 as promising indicators for accurate diagnosis of ITP and CCL21, CXCL8, CXCL10, CCL8, CCL3, and CCL15 as biomarkers for assessing bleeding risk in ITP patients. The results were confirmed using enzyme-linked immunosorbent assays in a validation cohort (43 ITP patients and 19 non-ITP patients). Overall, the findings suggest that specific chemokines show promise as potential biomarkers for diagnosis and bleeding evaluation in ITP patients.