Alinity hq platelet count is not impacted by severe microcytosis

Impedance technology has previously been demonstrated to overestimate platelet (PLT) count in samples with microcytosis. [1][2][3][4][5] The most frequent causes of microcytosis are iron deficiency, thalassemia syndromes, and anemia of chronic disease. Tantanate et al 3 . demonstrated that the impedance PLT count by Sysmex XN (PLT-I) showed poor correlation with the International Reference method (IRM), due to high-positive bias and low specificity for the diagnosis of thrombocytopenia, while the optical and fluoresce PLT counts were comparable to the IRM in patients with thalassemia. Several other studies have also concluded that optical or fluorescence-optical

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Alinity hq platelet count is not impacted by severe microcytosis

Lakos

Mukhtar

Masi

et al. 2022

Clinical Laboratory Analysis

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Performance evaluation of PLT-H (hybrid-channel platelet) under various interferences and application studies for platelet transfusion decisions

Cai,

Lin,

Guo

2023

Platelets

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Establishing reflex test rules for platelet fluorescent counting method using machine learning models on Sysmex XN‐series hematology analyzer

Zhou,

Guo,

et al. 2024

Int J Lab Hematology

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IntroductionThe platelet fluorescent counting (PLT‐F) method is utilized as a reflex test method following the initial test of the platelet impedance counting (PLT‐I) method in clinical practice on the Sysmex XN‐series automated hematology analyzer. Our aim is to establish reflex test rules for the PLT‐F method by combining multiple parameters provided by the “CBC + DIFF” mode of the Sysmex XN‐series automated hematology analyzer.MethodsWe tested 120 samples to evaluate the baseline bias between the PLT‐F and PLT‐I methods. Then, we selected 1256 samples to establish and test reflex test rules using seven machine learning models (decision Tree, random forest, neural network, logistic regression, k‐nearest neighbor, support vector machine, and Naive Bayes). The training set and test set were divided at a ratio of 7:3. We evaluated the performance of machine learning models on the test set using various metrics to select the most valuable model.ResultsThe PLT‐F method exhibited a high degree of correlation with the PLT‐I method (r = 0.998). The random forest model emerged as the most valuable, boasting an accuracy of 0.893, an area under the curve of 0.954, an F1 score of 0.771, a recall of 0.719, a precision of 0.831, and a specificity of 0.950. The most important variable in the random forest model was mean cell volume, weighted at 15.09%.ConclusionThe random forest model, which demonstrated high efficiency in our study, can be used to establish PLT reflex test rules based on the PLT‐F method for the Sysmex XN‐series automated hematology analyzer.

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Alinity hq platelet count is not impacted by severe microcytosis

Cited by 3 publications

References 15 publications