Extubation Decisions with Predictive Information for Mechanically Ventilated Patients in ICU

Management Science (forthcoming)

83 Pages Posted: 13 Jun 2019 Last revised: 5 Mar 2024

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 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 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 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 dataset 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 to 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.

Keywords: Intensive Care Init; Mechanical Ventilation; Extubation; Predictive Information; Treatment Effect

Suggested Citation

Cheng, Guang and Xie, Jingui and Zheng, Zhichao and Luo, Haidong and Ooi, Oon Cheong, Extubation Decisions with Predictive Information for Mechanically Ventilated Patients in ICU (June 1, 2019). Management Science (forthcoming), 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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