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Biosignal-Based Digital Biomarkers for Prediction of Ventilator Weaning Success

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dc.contributor.author윤덕용-
dc.date.accessioned2021-10-21T00:08:16Z-
dc.date.available2021-10-21T00:08:16Z-
dc.date.issued2021-09-
dc.identifier.issn1661-7827-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/185392-
dc.description.abstractWe 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.-
dc.description.statementOfResponsibilityopen-
dc.languageEnglish-
dc.publisherMDPI-
dc.relation.isPartOfINTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.titleBiosignal-Based Digital Biomarkers for Prediction of Ventilator Weaning Success-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Biomedical Systems Informatics (의생명시스템정보학교실)-
dc.contributor.googleauthorJi Eun Park-
dc.contributor.googleauthorTae Young Kim-
dc.contributor.googleauthorYun Jung Jung-
dc.contributor.googleauthorChangho Han-
dc.contributor.googleauthorChan Min Park-
dc.contributor.googleauthorJoo Hun Park-
dc.contributor.googleauthorKwang Joo Park-
dc.contributor.googleauthorDukyong Yoon-
dc.contributor.googleauthorWou Young Chung-
dc.identifier.doi10.3390/ijerph18179229-
dc.contributor.localIdA06062-
dc.relation.journalcodeJ01111-
dc.identifier.eissn1660-4601-
dc.identifier.pmid34501829-
dc.subject.keywordbiosignal-
dc.subject.keyworddigital biomarker-
dc.subject.keywordmachine learning-
dc.subject.keywordmechanical ventilator-
dc.subject.keywordprediction-
dc.subject.keywordweaning-
dc.contributor.alternativeNameYoon, Dukyong-
dc.contributor.affiliatedAuthor윤덕용-
dc.citation.volume18-
dc.citation.number17-
dc.citation.startPage9229-
dc.identifier.bibliographicCitationINTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, Vol.18(17) : 9229, 2021-09-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Biomedical Systems Informatics (의생명시스템정보학교실) > 1. Journal Papers

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