A machine learning approach for assessing acute infection by erythrocyte sedimentation rate (ESR) kinetics
Current automated technologies for ESR measurement well agree with the reference method and provide robust results for evaluating systemic infections. The novelty of this study lies in connecting ESR sedimentation kinetics to disease states, particularly for identifying sepsis/acute inflammatory status. Future studies with larger datasets are needed to validate these approaches and guide clinical application.
Authors
- Luisa Galla (ORCID: https://orcid.org/0000-0001-8160-6052)
- Mario Plebani (ORCID: https://orcid.org/0000-0002-0270-1711)
- Chiara Cosma (ORCID: https://orcid.org/0000-0002-0678-0277)
- Francesca Tosato (ORCID: https://orcid.org/0000-0003-4387-7358)
- Martina Zaninotto
- Ilaria Talli (ORCID: https://orcid.org/0000-0002-9425-6140)
- Daniela Diamanti (ORCID: https://orcid.org/0000-0002-1078-1889)
- Michela Pelloso (ORCID: https://orcid.org/0000-0002-2853-4499)
- Andrea Padoan (ORCID: https://orcid.org/0000-0003-1284-7885)
- Elisa Pangrazzi (ORCID: https://orcid.org/0009-0002-6043-5471)
- Alessandra Brogi
Institutions
- University of Padua (IT)
- Azienda Ospedaliera Universitaria Senese (IT)
Publication Details
- Journal
- Clinica Chimica Acta
- Published
- 2025-04-22
- DOI
- https://doi.org/10.1016/j.cca.2025.120308
- Citations
- 3
- Primary Topic
- Blood properties and coagulation
- Type
- article
- Field-Weighted Citation Impact
- 4.56