Utilizing heart rate variability to predict ICU patient outcome in traumatic brain injury

Luke J. Haseler,Brent Richards,Tegan Roberts,Ping Zhang

doi:10.1186/s12859-020-03814-w

Luke J. Haseler, Brent Richards + Show 2 more

Open Access

https://doi.org/10.1186/s12859-020-03814-w

Copy DOI

Abstract

BackgroundPrediction of patient outcome in medical intensive care units (ICU) may help for development and investigation of early interventional strategies. Several ICU scoring systems have been developed and are used to predict clinical outcome of ICU patients. These scores are calculated from clinical physiological and biochemical characteristics of patients. Heart rate variability (HRV) is a correlate of cardiac autonomic regulation and has been evident as a marker of poor clinical prognosis. HRV can be measured from the electrocardiogram non-invasively and monitored in real time. HRV has been identified as a promising ‘electronic biomarker’ of disease severity. Traumatic brain injury (TBI) is a subset of critically ill patients admitted to ICU, with significant morbidity and mortality, and often difficult to predict outcomes. Changes of HRV for brain injured patients have been reported in several studies. This study aimed to utilize the continuous HRV collection from admission across the first 24 h in the ICU in severe TBI patients to develop a patient outcome prediction system.ResultsA feature extraction strategy was applied to measure the HRV fluctuation during time. A prediction model was developed based on HRV measures with a genetic algorithm for feature selection. The result (AUC: 0.77) was compared with earlier reported scoring systems (highest AUC: 0.76), encouraging further development and practical application.ConclusionsThe prediction models built with different feature sets indicated that HRV based parameters may help predict brain injury patient outcome better than the previously adopted illness severity scores.

Highlights

Prediction of patient outcome in medical intensive care units (ICU) may help for development and investigation of early interventional strategies
There are several ICU scoring systems in place to measure the severity of Traumatic brain injury (TBI) including the Acute Physiology and Chronic Health Evaluation (APACHE II and the updated versions APACHE III/IV) [3,4,5,6], Simplified Acute Physiology Score (SAPS II) [7], Multiple Organ Dysfunction Score (MODS) [8], the Sequential Organ Failure Assessment (SOFA) [9], and Injury Severity Score (ISS) [10, 11], with a comprehensive review of ICU scoring systems by Rapsang and Shyam [12]
Association of brain death with Heart rate variability (HRV) responses was reported in the study of Baillard et al [34], and Piantino et al [35] reported that children who progressed to brain death exhibited lower HRV in both time and frequency domains. These findings suggest autonomic nervous system (ANS) dysfunction may be implicated with poor outcomes and indicate that HRV may be a promising predictor of adverse outcomes in TBI patients

Summary

Introduction

Prediction of patient outcome in medical intensive care units (ICU) may help for development and investigation of early interventional strategies. There are several ICU scoring systems in place to measure the severity of TBI including the Acute Physiology and Chronic Health Evaluation (APACHE II and the updated versions APACHE III/IV) [3,4,5,6], Simplified Acute Physiology Score (SAPS II) [7], Multiple Organ Dysfunction Score (MODS) [8], the Sequential Organ Failure Assessment (SOFA) [9], and Injury Severity Score (ISS) [10, 11], with a comprehensive review of ICU scoring systems by Rapsang and Shyam [12] These scores correspond to risk of death, and are commonly used to predict TBI patient outcomes. None-the-less, these scores do not take into account the heterogeneity that exists between patients due to the discrepancies in initial TBI presentations and the evolution of secondary brain injurie

Objectives

Methods

Results