Identification of parameters and formulation of a statistical and machine learning model to identify Babesia canis infections in dogs using available ADVIA hematology analyzer data

Tera Pijnacker*, Richard Bartels, Martin van Leeuwen, Erik Teske

*Corresponding author for this work

    Research output: Contribution to journalArticleAcademicpeer-review

    Abstract

    BACKGROUND: Canine babesiosis is an important tick-borne disease in endemic regions. One of the relevant subspecies in Europe is Babesia canis, and it can cause severe clinical signs such as hemolytic anemia. Apart from acute clinical symptoms dogs can also have a more chronic disease development or be asymptomatic carriers. Our objective was to identify readily available ADVIA hematology analyzer parameters suggestive of B. canis parasitemia in dogs and to formulate a predictive model.

    METHODS: A historical dataset of complete blood count data from an ADVIA hematology system with blood smear or PCR confirmed parasitemia cases was used to obtain a model by conventional statistics (CS) methods and machine learning (ML) using logistical regression and tree methods.

    RESULTS: Both methods identified that important parameters were platelet count, mean platelet volume and percentage large unstained cells. We were able to formulate a CS model and ML model to screen for Babesia parasitemia in dogs with a sensitivity of 84.6% (CS) and 100% (ML), a specificity of 97.7% (CS) and 95.7% (ML) and a positive likelihood ratio (LR+) of 36.78 (CS) and 23.2 (ML).

    CONCLUSIONS: This study introduces two methods of screening for B. canis parasitemia on readily available data from ADVIA hematology systems. The algorithms can easily be introduced in laboratories that use these analyzers. When the algorithm marks a sample as 'suggestive' for Babesia parasitemia, the sample is approximately 37 times more likely to show Babesia merozoites on blood smear analysis.

    Original languageEnglish
    Article number41
    Pages (from-to)1-10
    JournalParasites and Vectors
    Volume15
    Issue number1
    DOIs
    Publication statusPublished - 29 Jan 2022

    Keywords

    • ADVIA
    • Babesia canis
    • Blood smear
    • Machine learning
    • Reproducibility of Results
    • Babesia/classification
    • Hematology/instrumentation
    • Machine Learning
    • Animals
    • Babesiosis/diagnosis
    • Sensitivity and Specificity
    • Dogs
    • Parasitemia
    • Dog Diseases/parasitology

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