Please use this identifier to cite or link to this item: http://hdl.handle.net/11690/1900
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dc.contributor.authorZampieri, Fernando G.-
dc.contributor.authorSalluh, Jorge I. F.-
dc.contributor.authorAzevedo, Luciano C. P.-
dc.contributor.authorKahn, Jeremy M.-
dc.contributor.authorDamiani, Lucas P.-
dc.contributor.authorBorges, Lunna P.-
dc.contributor.authorViana, William N.-
dc.contributor.authorCosta, Roberto-
dc.contributor.authorCorrêa, Thiago D.-
dc.contributor.authorAraya, Dieter E. S.-
dc.contributor.authorMaia, Marcelo O.-
dc.contributor.authorFerez, Marcus A.-
dc.contributor.authorCarvalho, Alexandre G. R.-
dc.contributor.authorKnibel, Marcos F.-
dc.contributor.authorMelo, Ulisses O.-
dc.contributor.authorSantino, Marcelo S.-
dc.contributor.authorLisboa, Thiago-
dc.contributor.authorCaser, Eliana B.-
dc.contributor.authorBesen, Bruno A. M. P.-
dc.contributor.authorBozza, Fernando A.-
dc.contributor.authorAngus, Derek C.-
dc.contributor.authorSoares, Marcio-
dc.date.accessioned2021-07-30T17:40:05Z-
dc.date.available2021-07-30T17:40:05Z-
dc.date.issued2019-
dc.identifier.citationZAMPIERI, Fernando G. et al. ICU staffing feature phenotypes and their relationship with patients' outcomes: an unsupervised machine learning analysis. Intensive Care Medicine, v. 45, p. 1599-1607, 2019. Disponível em: https://link.springer.com/article/10.1007/s00134-019-05790-z. Acesso em: 30 jul. 2021.pt_BR
dc.identifier.urihttp://hdl.handle.net/11690/1900-
dc.description.abstractPurpose: To study whether ICU stafng features are associated with improved hospital mortality, ICU length of stay (LOS) and duration of mechanical ventilation (MV) using cluster analysis directed by machine learning. Methods: The following variables were included in the analysis: average bed to nurse, physiotherapist and physician ratios, presence of 24/7 board-certifed intensivists and dedicated pharmacists in the ICU, and nurse and physiotherapist autonomy scores. Clusters were defned using the partition around medoids method. We assessed the association between clusters and hospital mortality using logistic regression and with ICU LOS and MV duration using competing risk regression. Results: Analysis included data from 129,680 patients admitted to 93 ICUs (2014–2015). Three clusters were identifed. The features distinguishing between the clusters were: the presence of board-certifed intensivists in the ICU 24/7 (present in Cluster 3), dedicated pharmacists (present in Clusters 2 and 3) and the extent of nurse autonomy (which increased from Clusters 1 to 3). The patients in Cluster 3 exhibited the best outcomes, with lower adjusted hospital mortality [odds ratio 0.92 (95% confdence interval (CI), 0.87–0.98)], shorter ICU LOS [subhazard ratio (SHR) for patients surviving to ICU discharge 1.24 (95% CI 1.22–1.26)] and shorter durations of MV [SHR for undergoing extubation 1.61(95% CI 1.54–1.69)]. Cluster 1 had the worst outcomes. Conclusion: Patients treated in ICUs combining 24/7 expert intensivist coverage, a dedicated pharmacist and nurses with greater autonomy had the best outcomes. All of these features represent achievable targets that should be considered by policy makers with an interest in promoting equal and optimal ICU care.pt_BR
dc.language.isoen_USpt_BR
dc.publisherSpringer Open Choicept_BR
dc.rightsOpen Accessen
dc.subjectIntensive care unitpt_BR
dc.subjectOutcomespt_BR
dc.subjectCluster analysispt_BR
dc.subjectNurse autonomypt_BR
dc.subjectStafng featurespt_BR
dc.subjectICU organizationpt_BR
dc.titleICU staffing feature phenotypes and their relationship with patients' outcomes: an unsupervised machine learning analysispt_BR
dc.typeArtigopt_BR
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