A Bayesian hierarchical model for improving exercise rehabilitation in mechanically ventilated ICU patients

TL;DR

This paper introduces a Bayesian hierarchical model that predicts exercise intensity in ventilated ICU patients using physiological data, enabling real-time, personalized rehabilitation strategies to improve outcomes.

Contribution

The study develops a novel Bayesian hierarchical model with Gaussian processes for real-time exercise intensity prediction in ICU patients, addressing a key gap in personalized rehabilitation.

Findings

Model accurately predicts exercise intensity categories

Probabilistic uncertainty improves clinical decision-making

Validated with leave-one-patient-out cross-validation

Abstract

Patients who are mechanically ventilated in the intensive care unit (ICU) 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 hindered, however, by the lack of a scientific method for quantifying an individual patient's exercise intensity level in real time, which results in a broad one-size-fits-all approach to rehabilitation and sub-optimal patient outcomes. In this work we have developed a Bayesian hierarchical model with temporally correlated latent Gaussian processes to predict $\dot VO_2$, a physiological measure of exercise intensity, using readily available physiological data. Inference was performed using Integrated Nested Laplace Approximation. For practical use by clinicians $\dot VO_2$ was classified into exercise intensity categories. Internal validation using leave-one-patient-out cross-validation was conducted based on these classifications, and the role of probabilistic statements describing the classification uncertainty was investigated.