Utilizing heart rate variability to predict ICU patient outcome in traumatic brain injury
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RESEARCH
Utilizing heart rate variability to predict ICU patient outcome in traumatic brain injury Ping Zhang1* , Tegan Roberts2, Brent Richards3,4 and Luke J. Haseler5
From 3rd International Workshop on Computational Methods for the Immune System Function (CMISF 2019) San Diego, CA, USA. 18-21 November 2019 *Correspondence: [email protected] 1 Menzies Health Institute QLD, Griffith University, Gold Coast, Australia Full list of author information is available at the end of the article
Abstract Background: Prediction 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. Results: A 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. Conclusions: The 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. Keywords: ECG, Time series, HRV, Feature extraction, Euclidean distance, Patient outcome, ICU
Background Traumatic brain injury (TBI) is increasingly considered to be an important global health priority as it results in a large number of deaths and impairments leading to permanent disabilities [1, 2]. TBI patients are almost always admitted to an intensive care unit (ICU)
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