Forecasting disease trajectories in critical illness : comparison of probabilistic dynamic systems to static models to predict patient status in the intensive care unit
© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ..
OBJECTIVE: Conventional prediction models fail to integrate the constantly evolving nature of critical illness. Alternative modelling approaches to study dynamic changes in critical illness progression are needed. We compare static risk prediction models to dynamic probabilistic models in early critical illness.
DESIGN: We developed models to simulate disease trajectories of critically ill COVID-19 patients across different disease states. Eighty per cent of cases were randomly assigned to a training and 20% of the cases were used as a validation cohort. Conventional risk prediction models were developed to analyse different disease states for critically ill patients for the first 7 days of intensive care unit (ICU) stay. Daily disease state transitions were modelled using a series of multivariable, multinomial logistic regression models. A probabilistic dynamic systems modelling approach was used to predict disease trajectory over the first 7 days of an ICU admission. Forecast accuracy was assessed and simulated patient clinical trajectories were developed through our algorithm.
SETTING AND PARTICIPANTS: We retrospectively studied patients admitted to a Cleveland Clinic Healthcare System in Ohio, for the treatment of COVID-19 from March 2020 to December 2022.
RESULTS: 5241 patients were included in the analysis. For ICU days 2-7, the static (conventional) modelling approach, the accuracy of the models steadily decreased as a function of time, with area under the curve (AUC) for each health state below 0.8. But the dynamic forecasting approach improved its ability to predict as a function of time. AUC for the dynamic forecasting approach were all above 0.90 for ICU days 4-7 for all states.
CONCLUSION: We demonstrated that modelling critical care outcomes as a dynamic system improved the forecasting accuracy of the disease state. Our model accurately identified different disease conditions and trajectories, with a <10% misclassification rate over the first week of critical illness.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:14 |
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| Enthalten in: |
BMJ open - 14(2024), 2 vom: 06. Feb., Seite e079243 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Duggal, Abhijit [VerfasserIn] |
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| Links: |
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| Themen: |
Adult intensive & critical care |
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| Anmerkungen: |
Date Completed 14.02.2024 Date Revised 14.02.2024 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1136/bmjopen-2023-079243 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM368331210 |
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| 245 | 1 | 0 | |a Forecasting disease trajectories in critical illness |b comparison of probabilistic dynamic systems to static models to predict patient status in the intensive care unit |
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| 520 | |a © Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. | ||
| 520 | |a OBJECTIVE: Conventional prediction models fail to integrate the constantly evolving nature of critical illness. Alternative modelling approaches to study dynamic changes in critical illness progression are needed. We compare static risk prediction models to dynamic probabilistic models in early critical illness | ||
| 520 | |a DESIGN: We developed models to simulate disease trajectories of critically ill COVID-19 patients across different disease states. Eighty per cent of cases were randomly assigned to a training and 20% of the cases were used as a validation cohort. Conventional risk prediction models were developed to analyse different disease states for critically ill patients for the first 7 days of intensive care unit (ICU) stay. Daily disease state transitions were modelled using a series of multivariable, multinomial logistic regression models. A probabilistic dynamic systems modelling approach was used to predict disease trajectory over the first 7 days of an ICU admission. Forecast accuracy was assessed and simulated patient clinical trajectories were developed through our algorithm | ||
| 520 | |a SETTING AND PARTICIPANTS: We retrospectively studied patients admitted to a Cleveland Clinic Healthcare System in Ohio, for the treatment of COVID-19 from March 2020 to December 2022 | ||
| 520 | |a RESULTS: 5241 patients were included in the analysis. For ICU days 2-7, the static (conventional) modelling approach, the accuracy of the models steadily decreased as a function of time, with area under the curve (AUC) for each health state below 0.8. But the dynamic forecasting approach improved its ability to predict as a function of time. AUC for the dynamic forecasting approach were all above 0.90 for ICU days 4-7 for all states | ||
| 520 | |a CONCLUSION: We demonstrated that modelling critical care outcomes as a dynamic system improved the forecasting accuracy of the disease state. Our model accurately identified different disease conditions and trajectories, with a <10% misclassification rate over the first week of critical illness | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Adult intensive & critical care | |
| 650 | 4 | |a INTENSIVE & CRITICAL CARE | |
| 650 | 4 | |a STATISTICS & RESEARCH METHODS | |
| 700 | 1 | |a Scheraga, Rachel |e verfasserin |4 aut | |
| 700 | 1 | |a Sacha, Gretchen L |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Xiaofeng |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Shuaqui |e verfasserin |4 aut | |
| 700 | 1 | |a Krishnan, Sudhir |e verfasserin |4 aut | |
| 700 | 1 | |a Siuba, Matthew T |e verfasserin |4 aut | |
| 700 | 1 | |a Torbic, Heather |e verfasserin |4 aut | |
| 700 | 1 | |a Dugar, Siddharth |e verfasserin |4 aut | |
| 700 | 1 | |a Mucha, Simon |e verfasserin |4 aut | |
| 700 | 1 | |a Veith, Joshua |e verfasserin |4 aut | |
| 700 | 1 | |a Mireles-Cabodevila, Eduardo |e verfasserin |4 aut | |
| 700 | 1 | |a Bauer, Seth R |e verfasserin |4 aut | |
| 700 | 1 | |a Kethireddy, Shravan |e verfasserin |4 aut | |
| 700 | 1 | |a Vachharajani, Vidula |e verfasserin |4 aut | |
| 700 | 1 | |a Dalton, Jarrod E |e verfasserin |4 aut | |
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