Real-time prediction of intensive care unit patient acuity and therapy requirements using state-space modelling.

Journal: Nature communications
Published Date:

Abstract

Intensive care unit (ICU) patients often experience rapid changes in clinical status, requiring timely identification of deterioration to guide life-sustaining interventions. Current artificial intelligence (AI) models for acuity assessment rely on mortality as a proxy and lack direct prediction of clinical instability or treatment needs. Here we present APRICOT-M, a state-space model to predict real-time ICU acuity outcomes and transitions, and the need for life-sustaining therapies within the next four hours. The model integrates vital signs, laboratory results, medications, assessment scores, and patient characteristics, to make predictions, handling sparse, irregular data efficiently. Our model is trained on over 140,000 ICU admissions across 55 hospitals and validated on external and real-time data, outperforming clinical scores in predicting mortality and instability. The model demonstrates clinical relevance, with physicians reporting alerts as actionable and timely in a substantial portion of cases. These results highlight APRICOT-M's potential to support earlier, more informed ICU interventions.

Authors

  • Miguel Contreras
    Department of Biomedical Engineering, University of Florida, Gainesville, FL USA.
  • Brandon Silva
    Department of Biomedical Engineering, University of Florida, Gainesville, FL USA.
  • Benjamin Shickel
    Department of Medicine, University of Florida, Gainesville, FL USA.
  • Andrea Davidson
    University of Florida/Intelligent Critical Care Center, Gainesville, Florida, USA.
  • Tezcan Ozrazgat-Baslanti
    Department of Medicine, University of Florida, Gainesville, FL USA.
  • Yuanfang Ren
    Department of Medicine, University of Florida, Gainesville, FL USA.
  • Ziyuan Guan
    Department of Medicine, University of Florida, Gainesville, FL USA.
  • Jeremy Balch
    Department of Surgery, University of Florida Health, Gainesville, FL, USA.
  • Jiaqing Zhang
    From the State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science (J.Z., A.J., X.H., Y.Z., X.Q., X.T., L.L., Y.L.), Guangzhou, China; Guangdong Provincial Clinical Research Center for Ocular Diseases (J.Z., A.J., X.H., Y.Z., X.Q., X.T., L.L., Y.L.), Guangzhou, China.
  • Sabyasachi Bandyopadhyay
    Department of Biomedical Engineering, University of Florida, Gainesville, FL USA.
  • Tyler Loftus
    Intelligent Clinical Care Center (IC3), University of Florida, Gainesville, FL, USA.
  • Kia Khezeli
    Department of Biomedical Engineering, University of Florida, Gainesville, FL USA.
  • Gloria Lipori
    University of Florida Health, UF.
  • Jessica Sena
    Federal University of Minas Gerais/Department of Computer Science, Belo Horizonte, Brazil.
  • Subhash Nerella
    University of Florida, Gainesville, Florida, USA.
  • Azra Bihorac
    Department of Medicine, University of Florida, Gainesville, FL USA.
  • Parisa Rashidi
    Department of Biomedical Engineering, University of Florida, Gainesville, FL USA.