Optimal Stopping for Medical Treatment with Predictive Information

Abstract

Weaning patients from mechanical ventilators is a critical decision in intensive care units (ICUs), especially during the COVID-19 pandemic. In this study, we aim to improve the extubation decision for ventilated patients by exploiting data analytics and predictive information. 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. Using a comprehensive data set 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 failure rate of ventilated patients by up to 18.9%. The benefits are more significant for patients with poor initial conditions. Furthermore, simply optimizing LOS using a classical MDP model without incorporating predictive information leads to an increased failure rate of ventilated patients by up to 6%. Using publicly available data on critical COVID-19 patients, we show that applying the extubation protocols using predictive information can improve ICU throughput by up to 9.0%.