dc.contributor.author | Garcia-Vidal, Carolina | |
dc.contributor.author | Teijón-Lumbreras, Christian | |
dc.contributor.author | Aiello, Tommaso Francesco | |
dc.contributor.author | Chumbita, Mariana | |
dc.contributor.author | Menendez, Rosario | |
dc.contributor.author | Mateu Subirà, Aina | |
dc.contributor.author | Peyrony, Olivier | |
dc.contributor.author | Monzó, Patricia | |
dc.contributor.author | Lopera, Carlos | |
dc.contributor.author | Gallardo-Pizarro, Antonio | |
dc.contributor.author | Méndez, Raúl | |
dc.contributor.author | Calbo, Esther | |
dc.contributor.author | Xercavins, Mariona | |
dc.contributor.author | Cuesta Chasco, Genoveva | |
dc.contributor.author | Martínez, José A. | |
dc.contributor.author | Marcos, M. Angeles | |
dc.contributor.author | Mensa, Josep | |
dc.contributor.author | Soriano, Alex | |
dc.date.accessioned | 2024-05-16T11:10:00Z | |
dc.date.available | 2024-05-16T11:10:00Z | |
dc.date.issued | 2024 | |
dc.identifier.citation | Garcia-Vidal, Carolina; Teijón-Lumbreras, Christian; Aiello, Tommaso Francesco [et al.]. K-means clustering identifies diverse clinical phenotypes in COVID-19 patients: implications for mortality risks and remdesivir impact. Infectious Diseases and Therapy, 2024, 13, p. 715-726 . Disponible en: <https://link.springer.com/article/10.1007/s40121-024-00938-x>. Fecha de acceso: 16 may. 2024. DOI: 10.1007/s40121-024-00938-x | ca |
dc.identifier.issn | 2193-8229 | ca |
dc.identifier.uri | http://hdl.handle.net/20.500.12328/4230 | |
dc.description.abstract | Introduction: The impact of remdesivir on mortality in patients with COVID-19 is still controversial. We aimed to identify clinical phenotype clusters of COVID-19 hospitalized patients with highest benefit from remdesivir use and validate these findings in an external cohort. Methods: We included consecutive patients hospitalized between February 2020 and February 2021 for COVID-19. The derivation cohort comprised subjects admitted to Hospital Clinic of Barcelona. The validation cohort included patients from Hospital Universitari Mutua de Terrassa (Terrassa) and Hospital Universitari La Fe (Valencia), all tertiary centers in Spain. We employed K-means clustering to group patients according to reverse transcription polymerase chain reaction (rRT-PCR) cycle threshold (Ct) values and lymphocyte counts at diagnosis, and pre-test symptom duration. The impact of remdesivir on 60-day mortality in each cluster was assessed. Results: A total of 1160 patients (median age 66, interquartile range (IQR) 55–78) were included. We identified five clusters, with mortality rates ranging from 0 to 36.7%. Highest mortality rate was observed in the cluster including patients with shorter pre-test symptom duration, lower lymphocyte counts, and lower Ct values at diagnosis. The absence of remdesivir administration was associated with worse outcome in the high-mortality cluster (10.5% vs. 36.7%; p < 0.001), comprising subjects with higher viral loads. These results were validated in an external multicenter cohort of 981 patients. Conclusions: Patients with COVID-19 exhibit varying mortality rates across different clinical phenotypes. K-means clustering aids in identifying patients who derive the greatest mortality benefit from remdesivir use. | en |
dc.format.extent | 12 | ca |
dc.language.iso | eng | ca |
dc.publisher | Springer Nature | ca |
dc.relation.ispartof | 12 | ca |
dc.relation.ispartofseries | 13 | |
dc.rights | This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc/4.0/. | ca |
dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/ | |
dc.subject.other | COVID 19 | ca |
dc.subject.other | Agents antivirals | ca |
dc.subject.other | Agrupació | ca |
dc.subject.other | Intel·ligència artificial | ca |
dc.subject.other | COVID-19 | ca |
dc.subject.other | Agentes antivirales | es |
dc.subject.other | Agrupación | es |
dc.subject.other | Inteligencia artificial | es |
dc.subject.other | COVID-19 | es |
dc.subject.other | Antiviral agents | en |
dc.subject.other | Clustering | en |
dc.subject.other | Artifcial intelligence | en |
dc.subject.other | COVID-19 | en |
dc.title | K-means clustering identifies diverse clinical phenotypes in COVID-19 patients: implications for mortality risks and remdesivir impact | ca |
dc.type | info:eu-repo/semantics/article | ca |
dc.description.version | info:eu-repo/semantics/publishedVersion | ca |
dc.rights.accessLevel | info:eu-repo/semantics/openAccess | |
dc.embargo.terms | cap | ca |
dc.subject.udc | 61 | ca |
dc.identifier.doi | https://dx.doi.org/10.1007/s40121-024-00938-x | ca |