Predictive Monitoring-Impact in Acute Care Cardiology Trial (PM-IMPACCT): Protocol for a Randomized Controlled Trial

Jessica Keim-Malpass, Sarah J Ratcliffe, Liza P Moorman, Matthew T Clark, Katy N Krahn, Oliver J Monfredi, Susan Hamil, Gholamreza Yousefvand, J Randall Moorman, Jamieson M Bourque, Jessica Keim-Malpass, Sarah J Ratcliffe, Liza P Moorman, Matthew T Clark, Katy N Krahn, Oliver J Monfredi, Susan Hamil, Gholamreza Yousefvand, J Randall Moorman, Jamieson M Bourque

Abstract

Background: Patients in acute care wards who deteriorate and are emergently transferred to intensive care units (ICUs) have poor outcomes. Early identification of patients who are decompensating might allow for earlier clinical intervention and reduced morbidity and mortality. Advances in bedside continuous predictive analytics monitoring (ie, artificial intelligence [AI]-based risk prediction) have made complex data easily available to health care providers and have provided 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, Continuous Monitoring of Event Trajectories (CoMET), 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 improving patient outcomes related to clinical deterioration, response time to proactive clinical action, and costs to the health care system.

Methods: We propose a cluster randomized controlled trial to test the impact of using the CoMET display in an acute care cardiology and cardiothoracic surgery hospital floor. The number of admissions to a room undergoing cluster randomization was estimated to be 10,424 over the 20-month study period. Cluster randomization based on bed number will occur every 2 months. The intervention cluster will have the CoMET score displayed (along with standard of care), while the usual care group will receive 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 have been 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 in incorporating real-time telemetric data for identifying clinical deterioration on acute care wards. This analysis will strengthen the ability of health care organizations to evolve as learning health systems, in which bioinformatics data are applied to improve patient outcomes by incorporating AI into knowledge tools that are successfully integrated into clinical practice by health care providers.

Trial registration: ClinicalTrials.gov NCT04359641; https://ichgcp.net/clinical-trials-registry/NCT04359641.

International registered report identifier (irrid): DERR1-10.2196/29631.

Keywords: AI; acute care; artificial intelligence; cardiology; clinical deterioration; impact; monitoring; prediction; predictive analytics monitoring; randomized controlled trial; risk; risk estimation; visual analytics.

Conflict of interest statement

Conflicts of Interest: LPM is chief implementation officer, MC is chief scientific officer, and JRM is chief medical officer of AMP3D in Charlottesville, VA. LPM, MC, and JRM are also shareholders in AMP3D. The other authors have no conflicts of interest to declare.

©Jessica Keim-Malpass, Sarah J Ratcliffe, Liza P Moorman, Matthew T Clark, Katy N Krahn, Oliver J Monfredi, Susan Hamil, Gholamreza Yousefvand, J Randall Moorman, Jamieson M Bourque. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 02.07.2021.

Figures

Figure 1
Figure 1
CoMET artificial intelligence–based visual risk analytic. CoMET: Continuous Monitoring of Event Trajectories.

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