A Bayesian hierarchical model for predicting rates of oxygen consumption in mechanically ventilated intensive care patients

Abstract

Patients who are mechanically ventilated in the Intensive Care Unit participate in exercise as a component of their rehabilitation to ameliorate the long-term impact of critical illness on their physical function. The effective implementation of these programmes is limited, however, as clinicians do not have access to a patient's [Formula: see text] values, a physiological measure that quantifies an individual patient's exercise intensity level in real-time. In this work we have developed a Bayesian hierarchical model with temporally correlated latent Gaussian processes to predict [Formula: see text] using readily available physiological data, providing clinicians with information to personalise rehabilitation sessions in real-time. The model was fitted using the Integrated Nested Laplace Approximation and validated using posterior predictive checks, and the impact of alternate specifications of the latent process was examined. Assessed using leave-one-patientout cross-validation, we show that the ability to provide probabilistic statements describing classification uncertainty gives the model favourable predictive power compared to a state-of-the-art comparator based on the oxygen uptake efficiency slope, with a more than seven-fold increase in accuracy in identifying when a patient is at risk of over-exertion.