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Biosignal-Based Digital Biomarkers for Prediction of Ventilator Weaning Success

Authors
 Ji Eun Park  ;  Tae Young Kim  ;  Yun Jung Jung  ;  Changho Han  ;  Chan Min Park  ;  Joo Hun Park  ;  Kwang Joo Park  ;  Dukyong Yoon  ;  Wou Young Chung 
Citation
 INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, Vol.18(17) : 9229, 2021-09 
Journal Title
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
ISSN
 1661-7827 
Issue Date
2021-09
Keywords
biosignal ; digital biomarker ; machine learning ; mechanical ventilator ; prediction ; weaning
Abstract
We evaluated new features from biosignals comprising diverse physiological response information to predict the outcome of weaning from mechanical ventilation (MV). We enrolled 89 patients who were candidates for weaning from MV in the intensive care unit and collected continuous biosignal data: electrocardiogram (ECG), respiratory impedance, photoplethysmogram (PPG), arterial blood pressure, and ventilator parameters during a spontaneous breathing trial (SBT). We compared the collected biosignal data's variability between patients who successfully discontinued MV (n = 67) and patients who did not (n = 22). To evaluate the usefulness of the identified factors for predicting weaning success, we developed a machine learning model and evaluated its performance by bootstrapping. The following markers were different between the weaning success and failure groups: the ratio of standard deviations between the short-term and long-term heart rate variability in a Poincaré plot, sample entropy of ECG and PPG, α values of ECG, and respiratory impedance in the detrended fluctuation analysis. The area under the receiver operating characteristic curve of the model was 0.81 (95% confidence interval: 0.70-0.92). This combination of the biosignal data-based markers obtained during SBTs provides a promising tool to assist clinicians in determining the optimal extubation time.
Files in This Item:
T202104039.pdf Download
DOI
10.3390/ijerph18179229
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Biomedical Systems Informatics (의생명시스템정보학교실) > 1. Journal Papers
Yonsei Authors
Yoon, Dukyong(윤덕용)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/185392
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