Extubation Decisions with Predictive Information for Mechanically Ventilated Patients in the ICU

成果类型:
Article
署名作者:
Cheng, Guang; Xie, Jingui; Zheng, Zhichao; Luo, Haidong; Ooi, Oon Cheong
署名单位:
National University of Singapore; Technical University of Munich; Technical University of Munich; Singapore Management University; National University of Singapore
刊物名称:
MANAGEMENT SCIENCE
ISSN/ISSBN:
0025-1909
DOI:
10.1287/mnsc.2021.01427
发表日期:
2025
页码:
6069-6091
关键词:
intensive care unit mechanical ventilation extubation predictive information treatment effect
摘要:
Weaning patients from mechanical ventilators is a crucial decision in intensive care units (ICUs), significantly affecting patient outcomes and the throughput of ICUs. This study aims to improve the current extubation protocols by incorporating predictive information on patient health conditions. We develop a discrete-time, finite-horizon Markov decision process with predictions of future state to support extubation decisions. We characterize the structure of the optimal policy and provide important insights into how predictive information can lead to different decision protocols. We demonstrate that adding predictive information is always beneficial, even if physicians place excessive trust in the predictions, as long as the predictive model is moderately accurate. Using a comprehensive data set from an ICU in a tertiary hospital in Singapore, we evaluate the effectiveness of various policies and demonstrate that incorporating predictive information can reduce ICU length of stay by up to 3.4% and, simultaneously, decrease the extubation failure rate by up to 20.3%, compared with the optimal policy that does not utilize prediction. These benefits are more significant for patients with poor initial conditions upon ICU admission. Both our analytical and numerical findings suggest that predictive information is particularly valuable in identifying patients who could benefit from continued intubation, thereby allowing for personalized and delayed extubation for these patients.
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