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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月8日

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  • 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,
  • Parisa Rashidi

摘要

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.