Clinical Relevance of Extended Platelet Indices in the Diagnosis of Immune Thrombocytopenia

平均血小板体积 医学 血小板 内科学 胃肠病学 免疫性血小板减少症 耐火材料(行星科学) 临床意义 免疫学 天体生物学 物理
作者
Arshad Ali
出处
期刊:Acta Clinica Croatica [Croatian Dairy Union]
被引量:3
标识
DOI:10.20471/acc.2021.60.04.14
摘要

Immune thrombocytopenia (ITP) is an autoimmune disorder. Besides platelet count, immature platelet fraction (IPF) can be used as a tool to predict megakaryocytic activity in ITP patients. The aim of the study was to evaluate the utility of extended platelet indices in ITP diagnosis and their association with disease persistence and severity. This case-control study (1:1), conducted from January 2015 to December 2017, included 111 ITP patients and 111 healthy controls. ITP patients were grouped as newly diagnosed ITP, persistent ITP, chronic ITP, and refractory ITP patients. Peripheral blood was collected and complete blood profile parameters were recorded using Sysmex XN 1000. Significant (p≤0.05) difference between the groups of ITP patients and healthy control subjects was determined by Fisher exact test, while Pearson correlation was used to evaluate platelet count correlation with IPF using SPSS ver. 23. Low hemoglobin and platelet counts with high total leukocyte count and IPF were detected in ITP patients as compared to healthy subjects (p≤0.001). Among all groups of ITP patients, very low platelet count (6.9±6.02.x109/L) with highest mean IPF (27.1±19.2%) was observed in newly diagnosed ITP group. Other platelet parameters including mean platelet volume (MPV), plateletcrit, platelet large cell ratio (P-LCR) and platelet distribution width values were also altered in patient groups. Pearson correlation revealed negative relationship between platelet count and IPF in all patient groups. With the advent of new, sophisticated hematologic analyzers, the IPF and other platelet parameters provide simple, reliable and easier tools for predicting platelet disorders such as ITP, and to some extent the disease severity. Besides IPF, the MPV and P-LCR seemed to predict disease severity, treatment responsiveness, and duration of the disease to some extent.

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