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

Miguel Contreras, Brandon Silva, Benjamin Shickel, Andrea Davidson, Tezcan Ozrazgat-Baslanti, Yuanfang Ren, Ziyuan Guan, Jeremy Balch, Jiaqing Zhang, Sabyasachi Bandyopadhyay, Tyler Loftus, Kia Khezeli, Gloria Lipori, Jessica Sena, Subhash Nerella, Azra Bihorac and Parisa Rashidi ()
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Miguel Contreras: University of Florida
Brandon Silva: University of Florida
Benjamin Shickel: University of Florida
Andrea Davidson: University of Florida
Tezcan Ozrazgat-Baslanti: University of Florida
Yuanfang Ren: University of Florida
Ziyuan Guan: University of Florida
Jeremy Balch: University of Florida
Jiaqing Zhang: University of Florida
Sabyasachi Bandyopadhyay: Stanford University
Tyler Loftus: University of Florida
Kia Khezeli: University of Florida
Gloria Lipori: University of Florida
Jessica Sena: University of Florida
Subhash Nerella: University of Florida
Azra Bihorac: University of Florida
Parisa Rashidi: University of Florida

Nature Communications, 2025, vol. 16, issue 1, 1-15

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-62121-1

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