Forecasting disease trajectories in critical illness: comparison of probabilistic dynamic systems to static models to predict patient status in the intensive care unit

作者：Abhijit Duggal, R.G. Scheraga, Gretchen L. Sacha, Xiaofeng Wang, Shuaiqi Huang, Sudhir Krishnan, Matthew Siuba, Heather Torbic, Siddharth Dugar, Simon Mucha, Joshua Veith, Eduardo Mireles‐Cabodevila, Seth R. Bauer, Shravan Kethireddy, Vidula Vachharajani, Jarrod E. Dalton · 发表于：BMJ Open · 年份：2024 · DOI：10.1136/bmjopen-2023-079243 · 被引用次数：7 · 研究领域：Sepsis Diagnosis and Treatment、COVID-19 epidemiological studies、COVID-19 and healthcare impacts

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) ...