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

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    Zhang455787-Published.pdf (1.009Mb)
    Author(s)
    Zhang, Ping
    Roberts, Tegan
    Richards, Brent
    Haseler, Luke J
    Griffith University Author(s)
    Zhang, Ping
    Richards, Brent V.
    Roberts, Tegan J.
    Year published
    2020
    Metadata
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    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 ...
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    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.
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    Journal Title
    BMC Bioinformatics
    Volume
    21
    Issue
    Suppl 17
    DOI
    https://doi.org/10.1186/s12859-020-03814-w
    Copyright Statement
    © The Author(s) 2020. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
    Subject
    Mathematical Sciences
    Biological Sciences
    Information and Computing Sciences
    ECG
    Euclidean distance
    Feature extraction
    HRV
    ICU
    Publication URI
    http://hdl.handle.net/10072/400440
    Collection
    • Journal articles

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