Biosignal-Based Digital Biomarkers for Prediction of Ventilator Weaning Success

Ji Eun Park, Tae Young Kim, Yun Jung Jung, Changho Han, Chan Min Park, Joo Hun Park, Kwang Joo Park, Dukyong Yoon and Wou Young Chung
Additional contact information
Ji Eun Park: Department of Pulmonology and Critical Care Medicine, Ajou University School of Medicine, Suwon 16499, Korea
Tae Young Kim: BUD.on Inc., Jeonju 54871, Korea
Yun Jung Jung: Department of Pulmonology and Critical Care Medicine, Ajou University School of Medicine, Suwon 16499, Korea
Changho Han: Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Yongin 16995, Korea
Chan Min Park: Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Yongin 16995, Korea
Joo Hun Park: Department of Pulmonology and Critical Care Medicine, Ajou University School of Medicine, Suwon 16499, Korea
Kwang Joo Park: Department of Pulmonology and Critical Care Medicine, Ajou University School of Medicine, Suwon 16499, Korea
Dukyong Yoon: BUD.on Inc., Jeonju 54871, Korea
Wou Young Chung: Department of Pulmonology and Critical Care Medicine, Ajou University School of Medicine, Suwon 16499, Korea

IJERPH, 2021, vol. 18, issue 17, 1-17

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, ? 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.

Keywords: weaning; prediction; mechanical ventilator; biosignal; machine learning; digital biomarker (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1660-4601/18/17/9229/pdf (application/pdf)
https://www.mdpi.com/1660-4601/18/17/9229/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:18:y:2021:i:17:p:9229-:d:627332

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().