Use of a Continuous Single Lead Electrocardiogram Analytic to Predict Patient Deterioration Requiring Rapid Response Team Activation

Sooin Lee, Bryce Benson, Ashwin Belle, Richard P. Medlin, David Jerkins, Foster Goss, Ashish K. Khanna, Michael A. DeVita, Kevin R. Ward

Abstract

Identifying the onset of patient deterioration is challenging despite the potential to respond to patients earlier with better vital sign monitoring and rapid response team (RRT) activation. In this study an ECG based software as a medical device, the Analytic for Hemodynamic Instability Predictive Index (AHI-PI), was compared to the vital signs of heart rate, blood pressure, and respiratory rate, evaluating how early it indicated risk before an RRT activation. 

A higher proportion of the events had risk indication by AHI-PI (92.71%) than by vital signs (41.67%). AHI-PI indicated risk early, with an average of over a day before RRT events. In events whose risks were indicated by both AHI-PI and vital signs, AHI-PI demonstrated earlier recognition of deterioration compared to vital signs.

Introduction

Recognizing signs of early physiologic instability in hospitalized patients is critical to improving outcomes and preventing mortality. Failing to do so can contribute to unexpected transfers to a higher level of care, increased lengths of stay, and even unexpected deaths. Studies show that deliberate and consistent monitoring of vital signs improves early detection and clinical action [1,2]. However, measuring and documenting vital signs remain inconsistent and error prone in practice [3,4]. 

As a result, there may be delays in both recognizing and treating a patient at risk for deterioration. There are additional compounding factors that increasingly contribute to this problem. A few examples of such factors are nursing shortages, growing clinical workloads, patient comorbidities, data limitations, and resource constraints.

Methods

This was a retrospective single-center observational cohort study conducted at the University of Michigan, a quaternary academic health system in Ann Arbor, Michigan, between August 2019 and April 2020. The study dataset included consecutive hospitalized adult (≥ 18 years) patients who were undergoing continuous ECG monitoring on telemetry, stepdown and intensive care units and for whom an RRT was activated.

The study was approved by the University of Michigan Institutional Review Board (HUM00092309). Due to its retrospective design and use of deidentified data, a waiver of consent was granted.

Results

The mean patient age was 61.25 ± 14.36 years. Patient demographics are provided in Table 1. AHI-PI indicated risk in 92.71% (89) of the 96 RRT events (Fig 3). All 89 events (subgroup 1) had AHI-PI high or moderate risk outputs within 48 hours before the RRT. In contrast, EHR documented vital signs indicated risk in 41.67% (40) of the 96 RRT events, with all 40 events having documented hypotension and tachycardia or tachypnea in the previous 48 hours. In these 40 events, both vital signs and AHI-PI indicated risk (subgroup 1A). Where documented vital signs indicated no risk before the RRT event, AHI-PI indicated risk in 51.04% (49) of events (subgroup 1B).

Discussion

Prevention of morbidity and mortality using RRTs can only work if high risk deteriorating patients are rapidly and reliably identified, and RRT calls are triggered. Few studies have worked on preventing physiologic decline before achieving the triggering criteria. While evidence exists that changes in vital signs can be helpful in the early identification of patients who may benefit from an RRT, identifying the early onset of clinical deterioration by vital sign measurements can be challenging due to various factors such as infrequent, inaccurate, or delayed vital signs documentation and suboptimal nursing to patient ratios [5]. 

This can make it difficult to determine when to activate an RRT. As such there continues to be a need to develop clinical decision support tools that could optimize RRT utilization and efficacy by recognizing the potential for clinical deterioration at earlier time points.

Citation: Lee S, Benson B, Belle A, Medlin RP, Jerkins D, Goss F, et al. (2024) Use of a continuous single lead electrocardiogram analytic to predict patient deterioration requiring rapid response team activation. PLOS Digit Health 3(10): e0000465. https://doi.org/10.1371/journal.pdig.0000465

Academic Editor: Danilo Pani, University of Cagliari: Universita degli Studi Di Cagliari, ITALY

Received: February 8, 2024; Accepted: August 18, 2024; Published: October 24, 2024

Copyright: © 2024 Lee et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: This data is owned exclusively by University of Michigan and was used for this analysis with an established data use agreement between Fifth Eye Inc. and the University of Michigan under a cleared IRB. For inquiries or data access requests, please contact the email address provided below: University of Michigan Max Harry Weil Institute for Critical Care Research and Innovation weilinfo@med.umich.edu.

Funding: The author(s) received no specific funding for this work.

Competing Interests: Bryce Benson and Ashwin Belle are currently employed by Fifth Eye Inc. Sooin Lee was previously employed by Fifth Eye Inc. Bryce Benson, Ashwin Belle, and Kevin Ward have patents and equity interest in Fifth Eye Inc. The remaining authors declare no competing interests exist.