Predictors of mechanical ventilation and mortality in critically ill patients with COVID-19 pneumonia

dc.contributor.authorMuñoz Lezcano, Sergio
dc.contributor.authorArmengol de la Hoz, Miguel Ángel
dc.contributor.authorCorbi, Alberto
dc.contributor.authorLópez Hernández, Fernando Carlos
dc.contributor.authorSánchez García, Miguel
dc.contributor.authorNuñez Reiz, Antonio
dc.contributor.authorFariña González, Tomás
dc.contributor.authorYordanov Zlatkov, Viktor
dc.date.accessioned2025-10-14T08:04:40Z
dc.date.available2025-10-14T08:04:40Z
dc.date.issued2025
dc.description.abstractObjective: To determine if potential predictors for invasive mechanical ventilation (IMV) are also determinants for mortality in COVID-19-associated acute respiratory distress syndrome (C-ARDS). Design: Single center highly detailed longitudinal observational study. Setting: Tertiary hospital ICU: two first COVID-19 pandemic waves, Madrid, Spain. Patients or participants: 280 patients with C-ARDS, not requiring IMV on admission. Interventions: None. Main variables of interest: Target: endotracheal intubation and IMV, mortality. Predictors: demographics, hourly evolution of oxygenation, clinical data, and laboratory results. Results: The time between symptom onset and ICU admission, the APACHE II score, the ROX index, and procalcitonin levels in blood were potential predictors related to both IMV and mortality. The ROX index was the most significant predictor associated with IMV, while APACHE II, LDH, and DaysSympICU were the most with mortality. Conclusions: According to the results of the analysis, there are significant predictors linked with IMV and mortality in C-ARDS patients, including the time between symptom onset and ICU admission, the severity of the COVID-19 waves, and several clinical and laboratory measures. These findings may help clinicians to better identify patients at risk for IMV and mortality and improve their management.
dc.description.departmentDepto. de Análisis Matemático y Matemática Aplicada
dc.description.facultyFac. de Ciencias Matemáticas
dc.description.refereedFALSE
dc.description.statusunpub
dc.identifier.doi10.1016/j.medine.2023.07.009
dc.identifier.officialurlhttps://pubmed.ncbi.nlm.nih.gov/37500305/
dc.identifier.urihttps://hdl.handle.net/20.500.14352/124862
dc.issue.number1
dc.journal.titleMedicina Intensiva (English Edition)
dc.language.isoeng
dc.page.final13
dc.page.initial3
dc.rights.accessRightsopen access
dc.subject.keywordAcute respiratory distress syndrome
dc.subject.keywordArtificial intelligence
dc.subject.keywordInvasive mechanical ventilation
dc.subject.keywordMachine learning
dc.subject.keywordAprendizaje automático
dc.subject.keywordCOVID-19
dc.subject.keywordInteligencia artificial
dc.subject.keywordPredictores
dc.subject.ucmMedicina
dc.subject.ucmInteligencia artificial (Informática)
dc.subject.ucmEnfermedades infecciosas
dc.subject.unesco32 Ciencias Médicas
dc.subject.unesco1203.04 Inteligencia Artificial
dc.subject.unesco3205.05 Enfermedades Infecciosas
dc.titlePredictors of mechanical ventilation and mortality in critically ill patients with COVID-19 pneumonia
dc.typejournal article
dc.volume.number48
dspace.entity.typePublication
relation.isAuthorOfPublication5abd8a73-c9b7-45c0-9758-a37c56926604
relation.isAuthorOfPublication.latestForDiscovery5abd8a73-c9b7-45c0-9758-a37c56926604

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