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

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.