Abstract
The aim of this study was to develop a model that could be used to forecast the bleeding risk of ITP based on proinflammatory and anti-inflammatory factors. One hundred ITP patients were recruited to build a new predictive nomogram, another eighty-eight ITP patients were enrolled as validation cohort, and data were collected from January 2016 to January 2019. Four demographic characteristics and fifteen clinical characteristics were taken into account. Eleven cytokines (IFN-γ, IL-1, IL-4, IL-6, IL-8, IL-10, IL-17A, IL-22, IL-23, TNF-α and TGF-β) were used to study and the levels of them were detected by using a cytometric bead array (CBA) human inflammation kit. The least absolute shrinkage and selection operator regression model was used to optimize feature selection. Multivariate logistic regression analysis was applied to build a new predictive nomogram based on the results of the least absolute shrinkage and selection operator regress ion model. The application of C-index, ROC curve, calibration plot, and decision curve analyses were used to assess the discrimination, calibration, and clinical practicability of the predictive model. Bootstrapping validation was used for testing and verifying the predictive model. After feature selection, cytokines IL-1, IL-6, IL-8, IL-23 and TGF-β were excluded, cytokines IFN-γ, IL-4, IL-10, IL-17A, IL-22, TGF-β, the count of PLT and the length of time of ITP were used as predictive factors in the predictive nomogram. The model showed good discrimination with a C-index of 0.82 (95% confidence interval 0.73376–0.90 624) in training cohortn and 0.89 (95% CI 0.868, 0.902) in validation cohort, an AUC of 0.795 in training cohort, 0.94 in validation cohort and good calibration. A high C-index value of 0.66 was reached in the interval validation assessment. Decision curve analysis showed that the bleeding risk nomogram was clinically useful when intervention was decided at the possibility threshold of 16–84%. The bleeding risk model based on IFN-γ, IL-4, IL-10, IL-17A, IL-22, TGF-β, the count of PLT and the length of time of ITP could be conveniently used to predict the bleeding risk of ITP.
Highlights
Immune thrombocytopenia (ITP) is a hematological disorder characterized by a decrease in platelet numbers with or without potential bleeding at multiple s ites[1]
The differentiation of T helper 1 (Th1) cells is induced by the cytokine IFN-γ, the differentiation of T helper 2 (Th2) cells is induced by the cytokine IL-4, the differentiation of T helper 17 (Th17) cells is induced by the cytokine IL-17, and the differentiation of T regulatory (Treg) cells is induced by the cytokine TGF-β
It is necessary to construct a risk nomogram that can be used by clinicians to clarify and control the risk of bleeding
Summary
Immune thrombocytopenia (ITP) is a hematological disorder characterized by a decrease in platelet numbers with or without potential bleeding at multiple s ites[1]. The imbalances between these T cells result in an inflammatory state, which contributes to vascular endothelial injury and platelet destruction in ITP patients. The differentiation of Th1 cells is induced by the cytokine IFN-γ, the differentiation of Th2 cells is induced by the cytokine IL-4, the differentiation of Th17 cells is induced by the cytokine IL-17, and the differentiation of Treg cells is induced by the cytokine TGF-β. The activities of these T cells are regulated by the cytokines IL-1, IL-6, IL-8, IL-10, IL22, IL-23, TNF-α and so o n5–8. It is meaningful for clinicians to estimate the bleeding risk of ITP patients
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