Cited 14 times in
Advantage of Vital Sign Monitoring Using a Wireless Wearable Device for Predicting Septic Shock in Febrile Patients in the Emergency Department: A Machine Learning-Based Analysis
DC Field | Value | Language |
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dc.contributor.author | 정경수 | - |
dc.contributor.author | 정성필 | - |
dc.contributor.author | 최아롬 | - |
dc.contributor.author | 김지훈 | - |
dc.date.accessioned | 2022-12-22T04:20:02Z | - |
dc.date.available | 2022-12-22T04:20:02Z | - |
dc.date.issued | 2022-09 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/192104 | - |
dc.description.abstract | Intermittent manual measurement of vital signs may not rapidly predict sepsis development in febrile patients admitted to the emergency department (ED). We aimed to evaluate the predictive performance of a wireless monitoring device that continuously measures heart rate (HR) and respiratory rate (RR) and a machine learning analysis in febrile but stable patients in the ED. We analysed 468 patients (age, ≥18 years; training set, n = 277; validation set, n = 93; test set, n = 98) having fever (temperature >38 °C) and admitted to the isolation care unit of the ED. The AUROC of the fragmented model with device data was 0.858 (95% confidence interval [CI], 0.809-0.908), and that with manual data was 0.841 (95% CI, 0.789-0.893). The AUROC of the accumulated model with device data was 0.861 (95% CI, 0.811-0.910), and that with manual data was 0.853 (95% CI, 0.803-0.903). Fragmented and accumulated models with device data detected clinical deterioration in febrile patients at risk of septic shock 9 h and 5 h 30 min earlier, respectively, than those with manual data. Continuous vital sign monitoring using a wearable device could accurately predict clinical deterioration and reduce the time to recognise potential clinical deterioration in stable ED patients with fever. | - |
dc.description.statementOfResponsibility | open | - |
dc.language | English | - |
dc.publisher | MDPI | - |
dc.relation.isPartOf | SENSORS | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.subject.MESH | Adolescent | - |
dc.subject.MESH | Clinical Deterioration* | - |
dc.subject.MESH | Emergency Service, Hospital | - |
dc.subject.MESH | Fever / diagnosis | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Machine Learning | - |
dc.subject.MESH | Shock, Septic* / diagnosis | - |
dc.subject.MESH | Vital Signs / physiology | - |
dc.subject.MESH | Wearable Electronic Devices* | - |
dc.title | Advantage of Vital Sign Monitoring Using a Wireless Wearable Device for Predicting Septic Shock in Febrile Patients in the Emergency Department: A Machine Learning-Based Analysis | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Internal Medicine (내과학교실) | - |
dc.contributor.googleauthor | Arom Choi | - |
dc.contributor.googleauthor | Kyungsoo Chung | - |
dc.contributor.googleauthor | Sung Phil Chung | - |
dc.contributor.googleauthor | Kwanhyung Lee | - |
dc.contributor.googleauthor | Heejung Hyun | - |
dc.contributor.googleauthor | Ji Hoon Kim | - |
dc.identifier.doi | 10.3390/s22187054 | - |
dc.contributor.localId | A03570 | - |
dc.contributor.localId | A03625 | - |
dc.contributor.localId | A05856 | - |
dc.contributor.localId | A05321 | - |
dc.relation.journalcode | J03219 | - |
dc.identifier.eissn | 1424-8220 | - |
dc.identifier.pmid | 36146403 | - |
dc.subject.keyword | deterioration | - |
dc.subject.keyword | emergency department | - |
dc.subject.keyword | machine learning | - |
dc.subject.keyword | septic shock | - |
dc.subject.keyword | vital sign monitoring | - |
dc.subject.keyword | wearable device | - |
dc.contributor.alternativeName | Jung, Kyung Soo | - |
dc.contributor.affiliatedAuthor | 정경수 | - |
dc.contributor.affiliatedAuthor | 정성필 | - |
dc.contributor.affiliatedAuthor | 최아롬 | - |
dc.contributor.affiliatedAuthor | 김지훈 | - |
dc.citation.volume | 22 | - |
dc.citation.number | 18 | - |
dc.citation.startPage | 7054 | - |
dc.identifier.bibliographicCitation | SENSORS, Vol.22(18) : 7054, 2022-09 | - |
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