Identification of distinct clinical phenotypes in mechanically ventilated patients with acute brain dysfunction using cluster analysis
| Autor(a) principal: | |
|---|---|
| Data de Publicação: | 2020 |
| Outros Autores: | , , , , , , |
| Tipo de documento: | Artigo |
| Idioma: | eng |
| Título da fonte: | Repositório Institucional da FIOCRUZ (ARCA) |
| DOI: | 10.1097/MD.0000000000020041 |
| Texto Completo: | https://www.arca.fiocruz.br/handle/icict/42393 |
Resumo: | Universidade Federal do Rio de Janeiro. Faculdade de Medicina. Rio de Janeiro, RJ, Brasil. |
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Souza-Dantas, Vicente CésDal-Pizzol, FelipeTomasi, Cristiane D.Spector, NelsonSoares, MárcioBozza, Fernando A.Póvoa, PedroSalluh, Jorge I. F.2020-07-24T21:23:40Z2020-07-24T21:23:40Z2020SOUZA-DANTAS, Vicente Cés et al. Identification of distinct clinical phenotypes in mechanically ventilated patients with acute brain dysfunction using cluster analysis. Medicine, v. 99, n. 18, p. 1-7, 2020.0025-7974https://www.arca.fiocruz.br/handle/icict/4239310.1097/MD.0000000000020041engWolters Kluwer HealthIdentification of distinct clinical phenotypes in mechanically ventilated patients with acute brain dysfunction using cluster analysisinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleUniversidade Federal do Rio de Janeiro. Faculdade de Medicina. Rio de Janeiro, RJ, Brasil.Universidade do Extremo Sul Catarinense. Programa de pós-graduação em ciências da saúde. Laboratório de Fisiopatologia Experimental. Criciúma, SC, Brasil / Hospital São José. Unidade de Terapia Intensiva. Criciúma, SC, Brasil / Centro de Pesquisa do Hospital São José. Criciúma, SC, Brasil.Universidade do Extremo Sul Catarinense. Programa de pós-graduação em ciências da saúde. Laboratório de Fisiopatologia Experimental. Criciúma, SC, Brasil / Hospital São José. Unidade de Terapia Intensiva. Criciúma, SC, Brasil / Centro de Pesquisa do Hospital São José. Criciúma, SC, Brasil / Núcleo de Estudos e Pesquisas em Integralidade e Saúde. Criciúma, SC, Brasil.Universidade Federal do Rio de Janeiro. Faculdade de Medicina. Rio de Janeiro, RJ, Brasil.Instituto D'or de Pesquisa e Educação. Rio de Janeiro, RJ, Brasil / Instituto Nacional de Câncer. Programa de Pós graduação. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Rio de Janeiro, RJ, Brasil / Instituto D'or de Pesquisa e Educação. Rio de Janeiro, RJ, Brasil.São Francisco Xavier Hospital. Centro Hospitalar de Lisboa Ocidental. Polyvalent Intensive Care Unit. Lisbon, Portugal / New University of Lisbon. NOVA Medical School. Lisbon, Portugal.Instituto D'or de Pesquisa e Educação. Rio de Janeiro, RJ, Brasil / Instituto Nacional de Câncer. Programa de Pós graduação. Rio de Janeiro, RJ, Brasil.Acute brain dysfunction (ABD) is a frequent and severe syndrome occurring in critically ill patients and early identification of high-risk patients is paramount. In the present analysis, we propose a clinically applicable model for early phenotype identification of ABD at the bedside in mechanically ventilated patients, improving the recognition of patients with prolonged ABD.Prospective cohort with 629 mechanically ventilated patients in two medical-surgical intensive care units at academic centers. We applied cluster analysis to identify phenotypes using clinical and biological data. We then tested the association of phenotypes and its respective clinical outcomes. We performed a validation on a new cohort of patients select on subsequent patients admitted to the participants intensive care units.A model with 3 phenotypes best described the study population. A 4-variable model including medical admission, sepsis diagnosis, simplified acute physiologic score II and basal serum C-reactive protein (CRP) accurately classified each phenotype (area under curve 0.82; 95% CI, 0.79-0.86). Phenotype A had the shorter duration of ABD (median, 1 day), while phenotypes B and C had progressively longer duration of ABD (median, 3 and 6 days, respectively; P < .0001). There was an association between the duration of ABD and the baseline CRP levels and simplified acute physiology score II score (sensitivity and specificity of 80%). To increase the sensitivity of the model, we added CRP kinetics. By day 1, a CRP < 1.0 times the initial level was associated with a shorter duration of ABD (specificity 0.98).A model based on widely available clinical variables could provide phenotypes associated with the duration of ABD. Phenotypes with longer duration of ABD (phenotypes B and C) are characterized by more severe inflammation and by significantly worse clinical outcomes.Acute brain dysfunctionCluster analysisC-reactive proteinCritically ill patientsinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da FIOCRUZ (ARCA)instname:Fundação Oswaldo Cruz (FIOCRUZ)instacron:FIOCRUZLICENSElicense.txtlicense.txttext/plain; charset=utf-83099https://www.arca.fiocruz.br/bitstream/icict/42393/1/license.txt586c046dcfeef936e32f0323bb9a47c0MD51ORIGINALIdentification_Fernando_Bozza_etal_INI_2020.pdfIdentification_Fernando_Bozza_etal_INI_2020.pdfapplication/pdf397000https://www.arca.fiocruz.br/bitstream/icict/42393/2/Identification_Fernando_Bozza_etal_INI_2020.pdf42543410abe8a0ea51ee44349562a652MD52TEXTIdentification_Fernando_Bozza_etal_INI_2020.pdf.txtIdentification_Fernando_Bozza_etal_INI_2020.pdf.txtExtracted 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| dc.title.pt_BR.fl_str_mv |
Identification of distinct clinical phenotypes in mechanically ventilated patients with acute brain dysfunction using cluster analysis |
| title |
Identification of distinct clinical phenotypes in mechanically ventilated patients with acute brain dysfunction using cluster analysis |
| spellingShingle |
Identification of distinct clinical phenotypes in mechanically ventilated patients with acute brain dysfunction using cluster analysis Souza-Dantas, Vicente Cés Acute brain dysfunction Cluster analysis C-reactive protein Critically ill patients |
| title_short |
Identification of distinct clinical phenotypes in mechanically ventilated patients with acute brain dysfunction using cluster analysis |
| title_full |
Identification of distinct clinical phenotypes in mechanically ventilated patients with acute brain dysfunction using cluster analysis |
| title_fullStr |
Identification of distinct clinical phenotypes in mechanically ventilated patients with acute brain dysfunction using cluster analysis |
| title_full_unstemmed |
Identification of distinct clinical phenotypes in mechanically ventilated patients with acute brain dysfunction using cluster analysis |
| title_sort |
Identification of distinct clinical phenotypes in mechanically ventilated patients with acute brain dysfunction using cluster analysis |
| author |
Souza-Dantas, Vicente Cés |
| author_facet |
Souza-Dantas, Vicente Cés Dal-Pizzol, Felipe Tomasi, Cristiane D. Spector, Nelson Soares, Márcio Bozza, Fernando A. Póvoa, Pedro Salluh, Jorge I. F. |
| author_role |
author |
| author2 |
Dal-Pizzol, Felipe Tomasi, Cristiane D. Spector, Nelson Soares, Márcio Bozza, Fernando A. Póvoa, Pedro Salluh, Jorge I. F. |
| author2_role |
author author author author author author author |
| dc.contributor.author.fl_str_mv |
Souza-Dantas, Vicente Cés Dal-Pizzol, Felipe Tomasi, Cristiane D. Spector, Nelson Soares, Márcio Bozza, Fernando A. Póvoa, Pedro Salluh, Jorge I. F. |
| dc.subject.en.pt_BR.fl_str_mv |
Acute brain dysfunction Cluster analysis C-reactive protein Critically ill patients |
| topic |
Acute brain dysfunction Cluster analysis C-reactive protein Critically ill patients |
| description |
Universidade Federal do Rio de Janeiro. Faculdade de Medicina. Rio de Janeiro, RJ, Brasil. |
| publishDate |
2020 |
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2020-07-24T21:23:40Z |
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2020-07-24T21:23:40Z |
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2020 |
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info:eu-repo/semantics/publishedVersion |
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info:eu-repo/semantics/article |
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article |
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SOUZA-DANTAS, Vicente Cés et al. Identification of distinct clinical phenotypes in mechanically ventilated patients with acute brain dysfunction using cluster analysis. Medicine, v. 99, n. 18, p. 1-7, 2020. |
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https://www.arca.fiocruz.br/handle/icict/42393 |
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0025-7974 |
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10.1097/MD.0000000000020041 |
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SOUZA-DANTAS, Vicente Cés et al. Identification of distinct clinical phenotypes in mechanically ventilated patients with acute brain dysfunction using cluster analysis. Medicine, v. 99, n. 18, p. 1-7, 2020. 0025-7974 10.1097/MD.0000000000020041 |
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Wolters Kluwer Health |
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Wolters Kluwer Health |
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