Predictive Monitoring: IMPact in Acute Care Cardiology Trial (PM-IMPACCT) - A Randomized Clinical Trial Protocol (Preprint)

BACKGROUND Patients on acute care wards who deteriorate and are emergently transferred to intensive care units have poor outcomes. Early identification of decompensating patients might allow for earlier clinical intervention and reduced morbidity and mortality. Advances in bedside continuous predictive analytics monitoring (i.e., artificial intelligence (AI)-based risk prediction) make complex data easily available to healthcare providers, and can provide early warning of potentially catastrophic clinical events. We present a dynamic, visual predictive analytics monitoring tool that integrates real-time bedside telemetric physiologic data into robust clinical models to estimate and communicate risk of imminent events. This tool, CoMET (Continuous Monitoring of Event Trajectories), has been shown in retrospective observational studies to predict clinical decompensation on the acute care ward. There is a need to more definitively study this advanced predictive analytics or AI monitoring system in a prospective, randomized controlled clinical trial. OBJECTIVE The goal of this trial is to determine the impact of an AI-based visual risk analytic, CoMET, on: (1) improving patient outcomes related to clinical deterioration, (2) response time to proactive clinical action, and (3) costs to the healthcare system. METHODS We propose a cluster randomized controlled trial (NCT04359641) to test the impact of displaying CoMET on an acute care cardiology and cardiothoracic surgery hospital floor. The number of admissions to a room undergoing cluster-randomization is estimated to be 10,424 over the 20-month study period. Cluster randomization based on bed number occurs every 2 months. The intervention cluster will have the CoMET score displayed (along with standard of care), while the usual care group receives standard of care only. RESULTS The primary outcome will be hours free from events of clinical deterioration. Hours of acute clinical events are defined as time when one or more of the following occur: emergent ICU transfer, emergent surgery prior to ICU transfer, cardiac arrest prior to ICU transfer, emergent intubation, or death. The clinical trial began randomization in January 2021. CONCLUSIONS Very few AI-based health analytics are translated from algorithm to real-world use. This study will use robust prospective, randomized controlled clinical trial methodology to assess the effectiveness of an advanced AI predictive analytics monitoring system incorporating real-time telemetric data for identifying clinical deterioration on acute care wards. This analysis will strengthen the ability of healthcare organizations to evolve as learning health systems, which apply bioinformatics data to improve patient outcomes by incorporating AI into knowledge tools that are successfully integrated into clinical practice by healthcare providers. CLINICALTRIAL Clinical trials identifier: NCT04359641