A machine learning approach for assessing acute infection by erythrocyte sedimentation rate (ESR) kinetics.

Journal: Clinica chimica acta; international journal of clinical chemistry
Published Date: Apr 22, 2025

Abstract

BACKGROUND: The erythrocyte sedimentation rate (ESR) is a traditional marker of inflammation, valued for its simplicity and low cost but limited by unsatisfactory specificity and sensitivity. This study evaluated the equivalence of ESR measurements obtained from three automated analyzers compared to the Westergren method. Furthermore, various machine learning (ML) techniques were employed to assess the usefulness of early sedimentation kinetics in inflammatory disease classification.

Authors

  • Andrea Padoan
    Department of Laboratory Medicine, University-Hospital of Padova, via Giustiniani 2, Padova 35128, Italy.
  • Ilaria Talli
    Department of Medicine (DIMED), University of Padova, Padova, Italy; Laboratory Medicine Unit, University-Hospital, Padova, Italy; QI.LAB.MED, Spin-off of the University of Padova, Italy.
  • Michela Pelloso
    Laboratory Medicine Unit, University-Hospital, Padova, Italy.
  • Luisa Galla
    Laboratory Medicine Unit, University-Hospital, Padova, Italy.
  • Francesca Tosato
    Laboratory Medicine Unit, University-Hospital, Padova, Italy.
  • Daniela Diamanti
    DIESSE Diagnostica Senese, Monteriggioni, Siena, Italy.
  • Chiara Cosma
    Department of Medicine (DIMED), University of Padova, Padova, Italy; QI.LAB.MED, Spin-off of the University of Padova, Italy.
  • Elisa Pangrazzi
    Department of Medicine (DIMED), University of Padova, Padova, Italy; QI.LAB.MED, Spin-off of the University of Padova, Italy.
  • Alessandra Brogi
    DIESSE Diagnostica Senese, Monteriggioni, Siena, Italy.
  • Martina Zaninotto
    QI.LAB.MED, Spin-off of the University of Padova, Italy.
  • Mario Plebani
    Department of Laboratory Medicine, University-Hospital of Padova, Padova, Italy.

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