Development and external validation of a machine learning model for the prediction of persistent acute kidney injury stage 3 in multi-centric, multi-national intensive care cohorts.

Journal: Critical care (London, England)
PMID: 38834995

Abstract

BACKGROUND: The aim of this retrospective cohort study was to develop and validate on multiple international datasets a real-time machine learning model able to accurately predict persistent acute kidney injury (AKI) in the intensive care unit (ICU).

Authors

  • Simone Zappalà
    U-Care Medical srl, Corso Castelfidardo 30A, 10129, Turin, Italy.
  • Francesca Alfieri
    Department of Applied Science and Technology, Politecnico Di Torino, C.so Duca degli Abruzzi 24, 10129, Turin, Italy.
  • Andrea Ancona
    Department of Applied Science and Technology, Politecnico Di Torino, C.so Duca degli Abruzzi 24, 10129, Turin, Italy.
  • Fabio Silvio Taccone
    Department of Intensive Care, Erasme Hospital, Université Libre de Bruxelles, 1070, Brussels, Belgium.
  • Riccardo Maviglia
    Department of Anesthesia, Intensive Care and Emergency Medicine, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168, Rome, Italy.
  • Valentina Cauda
    Department of Applied Science and Technology, Politecnico Di Torino, C.so Duca degli Abruzzi 24, 10129, Turin, Italy. Valentina.cauda@polito.it.
  • Stefano Finazzi
    Clinical Data Science Laboratory, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Stezzano 87, 24126, Bergamo, BG, Italy.
  • Antonio Maria Dell'Anna
    Department of Anesthesia, Intensive Care and Emergency Medicine, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168, Rome, Italy. antoniomaria.dellanna@policlinicogemelli.it.

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