Predictors of Non-invasive Ventilation Failure and Associated Factors Among the COVID-19 Patients Admitted to Intensive Care Unit

dc.authorscopusid57223798315
dc.authorscopusid6603477493
dc.authorscopusid23094932200
dc.contributor.authorBayraktar, Nurhan
dc.contributor.authorBayraktar,N.
dc.contributor.authorMohammadi,M.
dc.contributor.otherNursing
dc.date.accessioned2024-07-05T15:50:20Z
dc.date.available2024-07-05T15:50:20Z
dc.date.issued2023
dc.departmentAtılım Universityen_US
dc.department-tempVarpaei H.A., College of Nursing, Michigan State University, East Lansing, United States; Bayraktar N., Atilim University School of Health Sciences, Nursing Department, Ankara, Turkey; Mohammadi M., Department of Critical Care, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iranen_US
dc.description.abstractBackground: Non-invasive ventilation (NIV) is a method of oxygenation supply that utilizes a face mask and eliminates the need for an endotracheal airway. Non-invasive ventilation failure is defined as the necessity for endotracheal intubation or death during the NIV trial. Objectives: This study aimed to identify the predictors and associated factors of NIV failure in coronavirus disease 2019 (COVID-19) patients admitted to an intensive care unit (ICU). Methods: This retrospective, longitudinal cohort study utilized electronic medical records of COVID-19 patients admitted to the ICU. A total of 150 patients were included in the study. Patient demographics, medical history, laboratory tests, partial pressure of carbon dioxide (PCO2), oxygen saturation (SpO2), heart rate, acidosis, consciousness, oxygenation, and respiratory rate (HACOR score), and the ratio of oxygen saturation (ROX) index (the SpO2/fraction of inspired oxygen [FIO2] to respiratory rate [SF] ratio) were recorded. Non-invasive ventilation failure was determined based on the need for endotracheal intubation or cardiac-respiratory arrest while on NIV. Statistical analysis was conducted using SPSS software (version 26). Results: Of the patients, 55.3% were male (mean age: 55.9 years), with an NIV failure rate of 67.3%, a mortality rate of 66.7%, and 3.3% of patients requiring tracheostomy after NIV failure. The ROX index consistently decreased over time, and an increase in the HACOR score and PCO2 after 6 hours of commencing NIV were the predictors of NIV failure. Additionally, higher levels of lactate dehydrogenase, lower SF ratios, and higher APACHE scores upon ICU admission were significantly associated with NIV failure. Notably, the erythrocyte sedimentation rate (ESR) as an inflammatory index, SF ratio upon ICU admission, HACOR score, ROX index, and PCO2 after 12 hours were significant predictors of in-hospital mortality in patients receiving NIV. Conclusions: The ROX index, HACOR scale, and PCO2 are significant predictors of both NIV failure and in-hospital mortality. © 2023, Varpaei et al.en_US
dc.identifier.citation0
dc.identifier.doi10.5812/aapm-140847
dc.identifier.issn2228-7523
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-85179627497
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.5812/aapm-140847
dc.identifier.urihttps://hdl.handle.net/20.500.14411/4133
dc.identifier.volume13en_US
dc.language.isoenen_US
dc.publisherBrieflandsen_US
dc.relation.ispartofAnesthesiology and Pain Medicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAcute Respiratory Distress Syndromeen_US
dc.subjectAirway Managementen_US
dc.subjectCritical Careen_US
dc.subjectNoninvasive Ventilationen_US
dc.subjectNursing Careen_US
dc.titlePredictors of Non-invasive Ventilation Failure and Associated Factors Among the COVID-19 Patients Admitted to Intensive Care Uniten_US
dc.typeArticleen_US
dspace.entity.typePublication
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relation.isAuthorOfPublication.latestForDiscovery2de7089d-00df-41f2-8c32-e4461b1350a5
relation.isOrgUnitOfPublicatione886e794-386d-4406-82ee-8eecb54d2873
relation.isOrgUnitOfPublication.latestForDiscoverye886e794-386d-4406-82ee-8eecb54d2873

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