0000000000942044

AUTHOR

Cesar P Pere

showing 2 related works from this author

Geoeconomic variations in epidemiology, ventilation management, and outcomes in invasively ventilated intensive care unit patients without acute resp…

2022

Contains fulltext : 252214.pdf (Publisher’s version ) (Open Access) BACKGROUND: Geoeconomic variations in epidemiology, the practice of ventilation, and outcome in invasively ventilated intensive care unit (ICU) patients without acute respiratory distress syndrome (ARDS) remain unexplored. In this analysis we aim to address these gaps using individual patient data of four large observational studies. METHODS: In this pooled analysis we harmonised individual patient data from the ERICC, LUNG SAFE, PRoVENT, and PRoVENT-iMiC prospective observational studies, which were conducted from June, 2011, to December, 2018, in 534 ICUs in 54 countries. We used the 2016 World Bank classification to defi…

MaleSociodemographic FactorsRespiration Artificial/methodsARDS ; mechanical ventilationSeverity of Illness IndexNOSettore MED/41 - ANESTESIOLOGIA80 and overTidal VolumeHumansHospital MortalityProspective Studiesddc:610Developing CountriesAgedHospital Mortality/trendsAged 80 and overDeveloped Countries/statistics & numerical dataDeveloping Countries/statistics & numerical dataRespirationDeveloped CountriesArticlesGeneral Medicineacute respiratory distress syndromeLength of StayMiddle AgedRespiration ArtificialIntensive Care UnitsObservational Studies as Topiclnfectious Diseases and Global Health Radboud Institute for Health Sciences [Radboudumc 4]Length of Stay/statistics & numerical dataArtificialIntensive Care Units/statistics & numerical dataIncomeFemaleARDS
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Validation and utility of ARDS subphenotypes identified by machine-learning models using clinical data: an observational, multicohort, retrospective …

2022

Item does not contain fulltext BACKGROUND: Two acute respiratory distress syndrome (ARDS) subphenotypes (hyperinflammatory and hypoinflammatory) with distinct clinical and biological features and differential treatment responses have been identified using latent class analysis (LCA) in seven individual cohorts. To facilitate bedside identification of subphenotypes, clinical classifier models using readily available clinical variables have been described in four randomised controlled trials. We aimed to assess the performance of these models in observational cohorts of ARDS. METHODS: In this observational, multicohort, retrospective study, we validated two machine-learning clinical classifie…

Pulmonary and Respiratory MedicineClinical SciencesAcute Lung InjuryArticleMachine LearningPositive-Pressure RespirationRare DiseasesClinical ResearchRetrospective StudieSettore MED/41 - ANESTESIOLOGIAHumansLungAcute Respiratory Distress SyndromeRetrospective StudiesRespiratory Distress SyndromeOther Medical and Health SciencesLUNG SAFE Investigators and the ESICM Trials Grouplnfectious Diseases and Global Health Radboud Institute for Health Sciences [Radboudumc 4]PhenotypeGood Health and Well BeingArea Under CurveARDS: PhenotypeRespiratoryPublic Health and Health ServicesARDSHuman
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