Extubation Decision Making with Predictive Information for Mechanically Ventilated Patients in ICU

78 Pages Posted: 13 Jun 2019 Last revised: 14 Sep 2022

See all articles by Guang Cheng

Guang Cheng

National University of Singapore (NUS) - Institute of Operations Research and Analytics

Jingui Xie

Technische Universität München (TUM) - TUM School of Management

Zhichao Zheng

Singapore Management University - Lee Kong Chian School of Business

Haidong Luo

National University Hospital, Singapore

Oon Cheong Ooi

National University Hospital, Singapore

Date Written: June 1, 2019

Abstract

Weaning patients from mechanical ventilators is a critical decision in intensive care units (ICUs), significantly affecting patient outcomes and the throughput of ICUs. In this study, we aim to improve the current extubation protocols by incorporating predictive information on patient health conditions. We develop a discrete-time, finite-horizon Markov decision process (MDP) with predictions on future information to support the extubation decision. We characterize the structure of the optimal policy and provide important insights into how predictive information can lead to different decision protocols. We prove that adding predictive information is always beneficial, even if the physicians overtrust the predictions as long as the prediction accuracy satisfies certain conditions. Using a comprehensive dataset from an ICU in a tertiary hospital in Singapore, we compare the performance of different policies and demonstrate that incorporating predictive information can reduce ICU length of stay (LOS) by up to 9.4% and, simultaneously, decrease the extubation failure rate by up to 18.9%. The benefits are more significant for patients with poor initial conditions at ICU admission. Furthermore, simply optimizing LOS using a classical MDP model without incorporating predictive information leads to an increased extubation failure rate by up to 6%. Both our analytical and numerical findings suggest that predictive information is most useful in identifying patients who can benefit from continued intubation to execute personalized and delayed extubation.

Keywords: Medical Decision Making; Predictive Information; Markov Decision Process; Intensive Care Unit; Mechanical Ventilation

Suggested Citation

Cheng, Guang and Xie, Jingui and Zheng, Zhichao and Luo, Haidong and Ooi, Oon Cheong, Extubation Decision Making with Predictive Information for Mechanically Ventilated Patients in ICU (June 1, 2019). Available at SSRN: https://ssrn.com/abstract=3397530 or http://dx.doi.org/10.2139/ssrn.3397530

Guang Cheng

National University of Singapore (NUS) - Institute of Operations Research and Analytics ( email )

Innovation 4.0, #04-01, 3 Research Link
117602
Singapore

Jingui Xie

Technische Universität München (TUM) - TUM School of Management ( email )

Freising
Germany

Zhichao Zheng (Contact Author)

Singapore Management University - Lee Kong Chian School of Business ( email )

50 Stamford Road
Singapore, 178899
Singapore
(65) 6808 5474 (Phone)
(65) 6828 0777 (Fax)

HOME PAGE: http://www.zhengzhichao.com

Haidong Luo

National University Hospital, Singapore ( email )

5 Lower Kent Ridge Rd
Singapore, 119074
Singapore

Oon Cheong Ooi

National University Hospital, Singapore ( email )

5 Lower Kent Ridge Rd
Singapore, 119074
Singapore

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