COVID-19 patient profiles over four waves in Barcelona metropolitan area: a clustering approach

Objectives: Identifying profiles of hospitalized COVID-19 patients and explore their association with different degrees of severity of COVID-19 outcomes (i.e. in-hospital mortality, ICU assistance, and invasive mechanical ventilation). The findings of this study could inform the development of multi...

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Detalles Bibliográficos
Autores: Fernández Martínez, Daniel|||0000-0003-0012-2094, Pérez Álvarez, Nuria|||0000-0001-6582-1553, Molist Señé, Gemma
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/407845
Acceso en línea:https://hdl.handle.net/2117/407845
https://dx.doi.org/10.1371/journal.pone.0302461
Access Level:acceso abierto
Palabra clave:COVID-19 (Disease) -- Barcelona -- Statistics
Cluster analysis
COVID-19
Clustering
Classification tree
KAMILA
kAy-means
COVID-19 (Malaltia) -- Barcelona (Catalunya : Àrea metropolitana) -- Estadístiques
Anàlisi de conglomerats
Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística aplicada::Estadística biosanitària
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network_name_str España
repository_id_str
dc.title.none.fl_str_mv COVID-19 patient profiles over four waves in Barcelona metropolitan area: a clustering approach
title COVID-19 patient profiles over four waves in Barcelona metropolitan area: a clustering approach
spellingShingle COVID-19 patient profiles over four waves in Barcelona metropolitan area: a clustering approach
Fernández Martínez, Daniel|||0000-0003-0012-2094
COVID-19 (Disease) -- Barcelona -- Statistics
Cluster analysis
COVID-19
Clustering
Classification tree
KAMILA
kAy-means
COVID-19 (Malaltia) -- Barcelona (Catalunya : Àrea metropolitana) -- Estadístiques
Anàlisi de conglomerats
Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística aplicada::Estadística biosanitària
title_short COVID-19 patient profiles over four waves in Barcelona metropolitan area: a clustering approach
title_full COVID-19 patient profiles over four waves in Barcelona metropolitan area: a clustering approach
title_fullStr COVID-19 patient profiles over four waves in Barcelona metropolitan area: a clustering approach
title_full_unstemmed COVID-19 patient profiles over four waves in Barcelona metropolitan area: a clustering approach
title_sort COVID-19 patient profiles over four waves in Barcelona metropolitan area: a clustering approach
dc.creator.none.fl_str_mv Fernández Martínez, Daniel|||0000-0003-0012-2094
Pérez Álvarez, Nuria|||0000-0001-6582-1553
Molist Señé, Gemma
author Fernández Martínez, Daniel|||0000-0003-0012-2094
author_facet Fernández Martínez, Daniel|||0000-0003-0012-2094
Pérez Álvarez, Nuria|||0000-0001-6582-1553
Molist Señé, Gemma
author_role author
author2 Pérez Álvarez, Nuria|||0000-0001-6582-1553
Molist Señé, Gemma
author2_role author
author
dc.subject.none.fl_str_mv COVID-19 (Disease) -- Barcelona -- Statistics
Cluster analysis
COVID-19
Clustering
Classification tree
KAMILA
kAy-means
COVID-19 (Malaltia) -- Barcelona (Catalunya : Àrea metropolitana) -- Estadístiques
Anàlisi de conglomerats
Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística aplicada::Estadística biosanitària
topic COVID-19 (Disease) -- Barcelona -- Statistics
Cluster analysis
COVID-19
Clustering
Classification tree
KAMILA
kAy-means
COVID-19 (Malaltia) -- Barcelona (Catalunya : Àrea metropolitana) -- Estadístiques
Anàlisi de conglomerats
Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística aplicada::Estadística biosanitària
description Objectives: Identifying profiles of hospitalized COVID-19 patients and explore their association with different degrees of severity of COVID-19 outcomes (i.e. in-hospital mortality, ICU assistance, and invasive mechanical ventilation). The findings of this study could inform the development of multiple care intervention strategies to improve patient outcomes. Methods: Prospective multicentre cohort study during four different waves of COVID-19 from March 1st, 2020 to August 31st, 2021 in four health consortiums within the southern Barcelona metropolitan region. From a starting point of over 292 demographic characteristics, comorbidities, vital signs, severity scores, and clinical analytics at hospital admission, we used both clinical judgment and supervised statistical methods to reduce to the 36 most informative completed covariates according to the disease outcomes for each wave. Patients were then grouped using an unsupervised semiparametric method (KAMILA). Results were interpreted by clinical and statistician team consensus to identify clinically-meaningful patient profiles. Results: The analysis included nw1 = 1657, nw2 = 697, nw3 = 677, and nw4 = 787 hospitalized-COVID-19 patients for each of the four waves. Clustering analysis identified 2 patient profiles for waves 1 and 3, while 3 profiles were determined for waves 2 and 4. Patients allocated in those groups showed a different percentage of disease outcomes (e.g., wave 1: 15.9% (Cluster 1) vs. 31.8% (Cluster 2) for in-hospital mortality rate). The main factors to determine groups were the patient’s age and number of obese patients, number of comorbidities, oxygen support requirement, and various severity scores. The last wave is also influenced by the massive incorporation of COVID-19 vaccines. Conclusion: Our study suggests that a single care model at hospital admission may not meet the needs of hospitalized-COVID-19 adults. A clustering approach appears to be appropriate for helping physicians to differentiate patients and, thus, apply multiple care intervention strategies, as another way of responding to new outbreaks of this or future diseases.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-05-07
2024
2024-05-10
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/407845
https://dx.doi.org/10.1371/journal.pone.0302461
url https://hdl.handle.net/2117/407845
https://dx.doi.org/10.1371/journal.pone.0302461
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2019-104830RB-I00 METODOLOGIAS ESTADISTICAS PARA DATOS CLINICOS Y OMICOS Y SUS APLICACIONES EN CIENCIAS DE LA SALUD
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Public Library of Science (PLOS)
publisher.none.fl_str_mv Public Library of Science (PLOS)
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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spelling COVID-19 patient profiles over four waves in Barcelona metropolitan area: a clustering approachFernández Martínez, Daniel|||0000-0003-0012-2094Pérez Álvarez, Nuria|||0000-0001-6582-1553Molist Señé, GemmaCOVID-19 (Disease) -- Barcelona -- StatisticsCluster analysisCOVID-19ClusteringClassification treeKAMILAkAy-meansCOVID-19 (Malaltia) -- Barcelona (Catalunya : Àrea metropolitana) -- EstadístiquesAnàlisi de conglomeratsÀrees temàtiques de la UPC::Matemàtiques i estadística::Estadística aplicada::Estadística biosanitàriaObjectives: Identifying profiles of hospitalized COVID-19 patients and explore their association with different degrees of severity of COVID-19 outcomes (i.e. in-hospital mortality, ICU assistance, and invasive mechanical ventilation). The findings of this study could inform the development of multiple care intervention strategies to improve patient outcomes. Methods: Prospective multicentre cohort study during four different waves of COVID-19 from March 1st, 2020 to August 31st, 2021 in four health consortiums within the southern Barcelona metropolitan region. From a starting point of over 292 demographic characteristics, comorbidities, vital signs, severity scores, and clinical analytics at hospital admission, we used both clinical judgment and supervised statistical methods to reduce to the 36 most informative completed covariates according to the disease outcomes for each wave. Patients were then grouped using an unsupervised semiparametric method (KAMILA). Results were interpreted by clinical and statistician team consensus to identify clinically-meaningful patient profiles. Results: The analysis included nw1 = 1657, nw2 = 697, nw3 = 677, and nw4 = 787 hospitalized-COVID-19 patients for each of the four waves. Clustering analysis identified 2 patient profiles for waves 1 and 3, while 3 profiles were determined for waves 2 and 4. Patients allocated in those groups showed a different percentage of disease outcomes (e.g., wave 1: 15.9% (Cluster 1) vs. 31.8% (Cluster 2) for in-hospital mortality rate). The main factors to determine groups were the patient’s age and number of obese patients, number of comorbidities, oxygen support requirement, and various severity scores. The last wave is also influenced by the massive incorporation of COVID-19 vaccines. Conclusion: Our study suggests that a single care model at hospital admission may not meet the needs of hospitalized-COVID-19 adults. A clustering approach appears to be appropriate for helping physicians to differentiate patients and, thus, apply multiple care intervention strategies, as another way of responding to new outbreaks of this or future diseases."DF, NP, and GM have been supported by l’Agència de Gestio ´ d’Ajuts Universitaris i de Recerca (AGAUR) de la Generalitat de Catalunya (Spain) [2020PANDE00148] (https://agaur.gencat. cat/en/inici/index.html). DF and NP have been supported by the Ministerio de Ciencia e Innovacio ´n (Spain) [PID2019-104830RB-I00/ DOI (AEI): 10.13039/501100011033] (https://www.aei. gob.es/en/announcements/announcements-finder/ proyectos-idi-2019-modalidades-retosinvestigacion-generacion) and by grant 2021 SGR 01421 (GRBIO, https://agaur.gencat.cat/web/ shared/OVT/Departaments/REU/A_Universitats/ AGAUR/Documents/RECERCA/SGR/Resolucio_ definitiva_SGR-Cat_2021.pdf) administrated by the Departament de Recerca i Universitats de la Generalitat de Catalunya (Spain)."Peer ReviewedObjectius de Desenvolupament Sostenible::3 - Salut i BenestarPublic Library of Science (PLOS)20242024-05-0720242024-05-10journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/407845https://dx.doi.org/10.1371/journal.pone.0302461reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)InglésengAgencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2019-104830RB-I00 METODOLOGIAS ESTADISTICAS PARA DATOS CLINICOS Y OMICOS Y SUS APLICACIONES EN CIENCIAS DE LA SALUDopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/4078452026-05-27T15:37:01Z
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