Application value of multi-parameter dynamic monitoring of blood cells in evaluation of primary immune thrombocytopenia
Objective:To analyze the application value of multi-parameter dynamic monitoring of blood cells in the evaluation of pri-mary immune thrombocytopenia(ITP).Methods:95 children with ITP who were hospitalized from December 2021 to December 2022 were selected as the observation group,and 95 healthy children with physical examination in the same period were selected as the con-trol group.Mean platelet volume(MPV),mean platelet width(PDW),platelet count(PLT),and proportion of large platelets(P-LCR)were compared between the two groups at the time of enrolment.Children with different prognosis of ITP(before and after treat-ment)were compared in terms of disease condition,therapy,and haematocrits parameters.Correlation between haematological parame-ter indices and disease regression was analyzed and the predictive value of disease regression for the children with ITP was assessed.Results:At the time of admission,MPV,PDW,and P-LCR were higher and PLT was lower in the observation group than in the control group(P＜0.05).One month and two months after treatment,MPV,PDW and P-LCR of the children with good prognosis were lower than those with poor prognosis,and PLT was higher than those with poor prognosis(P＜0.05).The result showed that MPV,PDW and P-LCR were negatively correlated with the disease outcome after 1 month and 2 months of treatment,while PLT was positively correlated with the disease outcome(P＜0.05).One month and two months after the treatment,the ROC curve analysis showed that the area un-der the curve(AUC)of MPV,PLT,PDW and P-LCR in the joint prediction of the disease outcome of the children were 0.830 and 0.871,respectively,with sensitivity being 77.78％and 83.33％,and specificity being 88.31％and 90.91％,respectively.All of them were higher than that predicted by single index(P＜0.05).Conclusion:Dynamic monitoring of MPV,PLT,PDW and P-LCR can assist in the assessment of ITP,and has high efficacy in predicting the disease outcome of children.
万方数据
维普
