Real-time prediction of intensive care unit patient acuity and therapy requirements using state-space modelling
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 predi...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-62121-1 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Miguel Contreras |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ea94d1a648244fc0bd8f5cde5e02ceab |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-ea94d1a648244fc0bd8f5cde5e02ceab2025-08-20T04:03:03ZengNature PortfolioNature Communications2041-17232025-08-0116111510.1038/s41467-025-62121-1Real-time prediction of intensive care unit patient acuity and therapy requirements using state-space modellingMiguel Contreras0Brandon Silva1Benjamin Shickel2Andrea Davidson3Tezcan Ozrazgat-Baslanti4Yuanfang Ren5Ziyuan Guan6Jeremy Balch7Jiaqing Zhang8Sabyasachi Bandyopadhyay9Tyler Loftus10Kia Khezeli11Gloria Lipori12Jessica Sena13Subhash Nerella14Azra Bihorac15Parisa Rashidi16Department of Biomedical Engineering, University of FloridaIntelligent Clinical Care Center (IC3), University of FloridaIntelligent Clinical Care Center (IC3), University of FloridaIntelligent Clinical Care Center (IC3), University of FloridaIntelligent Clinical Care Center (IC3), University of FloridaIntelligent Clinical Care Center (IC3), University of FloridaIntelligent Clinical Care Center (IC3), University of FloridaIntelligent Clinical Care Center (IC3), University of FloridaIntelligent Clinical Care Center (IC3), University of FloridaDepartment of Medicine, Stanford UniversityIntelligent Clinical Care Center (IC3), University of FloridaDepartment of Biomedical Engineering, University of FloridaDeparment of Pharmaceutical Outcomes & Policy, University of FloridaDepartment of Biomedical Engineering, University of FloridaDepartment of Biomedical Engineering, University of FloridaIntelligent Clinical Care Center (IC3), University of FloridaDepartment of Biomedical Engineering, University of FloridaAbstract 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.https://doi.org/10.1038/s41467-025-62121-1 |
| spellingShingle | 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 Real-time prediction of intensive care unit patient acuity and therapy requirements using state-space modelling Nature Communications |
| title | Real-time prediction of intensive care unit patient acuity and therapy requirements using state-space modelling |
| title_full | Real-time prediction of intensive care unit patient acuity and therapy requirements using state-space modelling |
| title_fullStr | Real-time prediction of intensive care unit patient acuity and therapy requirements using state-space modelling |
| title_full_unstemmed | Real-time prediction of intensive care unit patient acuity and therapy requirements using state-space modelling |
| title_short | Real-time prediction of intensive care unit patient acuity and therapy requirements using state-space modelling |
| title_sort | real time prediction of intensive care unit patient acuity and therapy requirements using state space modelling |
| url | https://doi.org/10.1038/s41467-025-62121-1 |
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