Continuous Monitoring of Pain using Facial Expressions in ICU Environment (COMPANION)

Our team proposes to develop a facial-expressions-based machine learning algorithm named autoBPS to predict the electronic Behavioral Pain Score (eBPS), a digital endpoint for continuous monitoring of pain using facial expressions in the ICU environment (COMPANION). In the UG3 Phase, we will conduct a pilot-phase single-center observational prospective cohort study (N = 48) to develop the autoBPS machine learning algorithm prototype at MGH. We will perform exploratory analyses to establish clinical associations between facial expressions, EEG biosignals and pain, and to conduct analytical and clinical validations in accordance with the FDA regulatory requirements. In the UH3 Phase, we will further scale up to a multi-center prospective cohort study (N = 216) to expand, externally validate, and extend the autoBPS algorithm at three academic hospitals of MGH, BWH, and BIDMC. We will continue to expand the autoBPS model with newly collected BWH/MGH data and externally validate its performance on BIDMC set-aside data. We will extend and examine the utility of autoBPS for evaluating treatment effects as ICU clinical trial endpoints. Lastly, we will examine its generalizability on non-ventilated patient cohorts and perform bias minimization techniques for model enhancement. We expect that our autoBPS algorithm will provide an automated, resource-sparing, and reliable assistant tool for continuous pain monitoring in the ICU. Such a tool will help decrease patient suffering, reduce clinician workload, and facilitate scalable automated pain assessment in clinical research.