Novel Prehospital Phenotypes and Outcomes in Adult-Patients with Acute Disease

Autores
Martín Rodríguez, Francisco; López Izquierdo, Raúl; Sanz García, Ancor; del Pozo Vegas, Carlos; Castro Villamor, Miguel Ángel; Mayo Iscar, Agustín; Martín Conty, José L.; Ortega, Guillermo José
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
An early identification of prehospital phenotypes may allow health care workers to speed up and improve patients’ treatment. To determine emergency phenotypes by exclusively using prehospital clinical data, a multicenter, prospective, and observational ambulance-based study was conducted with a cohort of 3,853 adult patients treated consecutively and transferred with high priority from the scene to the hospital emergency department. Cluster analysis determined three clusterswith highly different outcome scores and pathological characteristics. The first cluster presented a 30-day mortality after the index event of 45.9%. The second cluster presented a mortality of 26.3%, while mortality of the third cluster was 5.1%. This study supports the detection of three phenotypes with different risk stages and with different clinical, therapeutic, and prognostic considerations. This evidence could allow adapting treatment to each phenotype thereby helping in the decisionmakingprocess.
Fil: Martín Rodríguez, Francisco. Universidad de Valladolid; España
Fil: López Izquierdo, Raúl. Universidad de Valladolid; España
Fil: Sanz García, Ancor. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; España
Fil: del Pozo Vegas, Carlos. Universidad de Valladolid; España
Fil: Castro Villamor, Miguel Ángel. Universidad de Valladolid; España
Fil: Mayo Iscar, Agustín. Universidad de Valladolid; España
Fil: Martín Conty, José L.. Universidad de Castilla-La Mancha; España
Fil: Ortega, Guillermo José. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; España. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Quilmes; Argentina
Materia
Clinical Decision-Making
Emergency Medical Services
Clinical Phenotypes
Machine Learning
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/238172
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/238172
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Novel Prehospital Phenotypes and Outcomes in Adult-Patients with Acute DiseaseMartín Rodríguez, FranciscoLópez Izquierdo, RaúlSanz García, Ancordel Pozo Vegas, CarlosCastro Villamor, Miguel ÁngelMayo Iscar, AgustínMartín Conty, José L.Ortega, Guillermo JoséClinical Decision-MakingEmergency Medical ServicesClinical PhenotypesMachine Learninghttps://purl.org/becyt/ford/3.2https://purl.org/becyt/ford/3An early identification of prehospital phenotypes may allow health care workers to speed up and improve patients’ treatment. To determine emergency phenotypes by exclusively using prehospital clinical data, a multicenter, prospective, and observational ambulance-based study was conducted with a cohort of 3,853 adult patients treated consecutively and transferred with high priority from the scene to the hospital emergency department. Cluster analysis determined three clusterswith highly different outcome scores and pathological characteristics. The first cluster presented a 30-day mortality after the index event of 45.9%. The second cluster presented a mortality of 26.3%, while mortality of the third cluster was 5.1%. This study supports the detection of three phenotypes with different risk stages and with different clinical, therapeutic, and prognostic considerations. This evidence could allow adapting treatment to each phenotype thereby helping in the decisionmakingprocess.Fil: Martín Rodríguez, Francisco. Universidad de Valladolid; EspañaFil: López Izquierdo, Raúl. Universidad de Valladolid; EspañaFil: Sanz García, Ancor. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; EspañaFil: del Pozo Vegas, Carlos. Universidad de Valladolid; EspañaFil: Castro Villamor, Miguel Ángel. Universidad de Valladolid; EspañaFil: Mayo Iscar, Agustín. Universidad de Valladolid; EspañaFil: Martín Conty, José L.. Universidad de Castilla-La Mancha; EspañaFil: Ortega, Guillermo José. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; España. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Quilmes; ArgentinaSpringer2022-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/238172Martín Rodríguez, Francisco; López Izquierdo, Raúl; Sanz García, Ancor; del Pozo Vegas, Carlos; Castro Villamor, Miguel Ángel ; et al.; Novel Prehospital Phenotypes and Outcomes in Adult-Patients with Acute Disease; Springer; Journal of Medical Systems; 46; 7; 6-2022; 1-101573-689XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1007/s10916-022-01825-zinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:10:04Zoai:ri.conicet.gov.ar:11336/238172instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-10 13:10:05.061CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Novel Prehospital Phenotypes and Outcomes in Adult-Patients with Acute Disease
title Novel Prehospital Phenotypes and Outcomes in Adult-Patients with Acute Disease
spellingShingle Novel Prehospital Phenotypes and Outcomes in Adult-Patients with Acute Disease
Martín Rodríguez, Francisco
Clinical Decision-Making
Emergency Medical Services
Clinical Phenotypes
Machine Learning
title_short Novel Prehospital Phenotypes and Outcomes in Adult-Patients with Acute Disease
title_full Novel Prehospital Phenotypes and Outcomes in Adult-Patients with Acute Disease
title_fullStr Novel Prehospital Phenotypes and Outcomes in Adult-Patients with Acute Disease
title_full_unstemmed Novel Prehospital Phenotypes and Outcomes in Adult-Patients with Acute Disease
title_sort Novel Prehospital Phenotypes and Outcomes in Adult-Patients with Acute Disease
dc.creator.none.fl_str_mv Martín Rodríguez, Francisco
López Izquierdo, Raúl
Sanz García, Ancor
del Pozo Vegas, Carlos
Castro Villamor, Miguel Ángel
Mayo Iscar, Agustín
Martín Conty, José L.
Ortega, Guillermo José
author Martín Rodríguez, Francisco
author_facet Martín Rodríguez, Francisco
López Izquierdo, Raúl
Sanz García, Ancor
del Pozo Vegas, Carlos
Castro Villamor, Miguel Ángel
Mayo Iscar, Agustín
Martín Conty, José L.
Ortega, Guillermo José
author_role author
author2 López Izquierdo, Raúl
Sanz García, Ancor
del Pozo Vegas, Carlos
Castro Villamor, Miguel Ángel
Mayo Iscar, Agustín
Martín Conty, José L.
Ortega, Guillermo José
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Clinical Decision-Making
Emergency Medical Services
Clinical Phenotypes
Machine Learning
topic Clinical Decision-Making
Emergency Medical Services
Clinical Phenotypes
Machine Learning
purl_subject.fl_str_mv https://purl.org/becyt/ford/3.2
https://purl.org/becyt/ford/3
dc.description.none.fl_txt_mv An early identification of prehospital phenotypes may allow health care workers to speed up and improve patients’ treatment. To determine emergency phenotypes by exclusively using prehospital clinical data, a multicenter, prospective, and observational ambulance-based study was conducted with a cohort of 3,853 adult patients treated consecutively and transferred with high priority from the scene to the hospital emergency department. Cluster analysis determined three clusterswith highly different outcome scores and pathological characteristics. The first cluster presented a 30-day mortality after the index event of 45.9%. The second cluster presented a mortality of 26.3%, while mortality of the third cluster was 5.1%. This study supports the detection of three phenotypes with different risk stages and with different clinical, therapeutic, and prognostic considerations. This evidence could allow adapting treatment to each phenotype thereby helping in the decisionmakingprocess.
Fil: Martín Rodríguez, Francisco. Universidad de Valladolid; España
Fil: López Izquierdo, Raúl. Universidad de Valladolid; España
Fil: Sanz García, Ancor. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; España
Fil: del Pozo Vegas, Carlos. Universidad de Valladolid; España
Fil: Castro Villamor, Miguel Ángel. Universidad de Valladolid; España
Fil: Mayo Iscar, Agustín. Universidad de Valladolid; España
Fil: Martín Conty, José L.. Universidad de Castilla-La Mancha; España
Fil: Ortega, Guillermo José. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; España. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Quilmes; Argentina
description An early identification of prehospital phenotypes may allow health care workers to speed up and improve patients’ treatment. To determine emergency phenotypes by exclusively using prehospital clinical data, a multicenter, prospective, and observational ambulance-based study was conducted with a cohort of 3,853 adult patients treated consecutively and transferred with high priority from the scene to the hospital emergency department. Cluster analysis determined three clusterswith highly different outcome scores and pathological characteristics. The first cluster presented a 30-day mortality after the index event of 45.9%. The second cluster presented a mortality of 26.3%, while mortality of the third cluster was 5.1%. This study supports the detection of three phenotypes with different risk stages and with different clinical, therapeutic, and prognostic considerations. This evidence could allow adapting treatment to each phenotype thereby helping in the decisionmakingprocess.
publishDate 2022
dc.date.none.fl_str_mv 2022-06
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/238172
Martín Rodríguez, Francisco; López Izquierdo, Raúl; Sanz García, Ancor; del Pozo Vegas, Carlos; Castro Villamor, Miguel Ángel ; et al.; Novel Prehospital Phenotypes and Outcomes in Adult-Patients with Acute Disease; Springer; Journal of Medical Systems; 46; 7; 6-2022; 1-10
1573-689X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/238172
identifier_str_mv Martín Rodríguez, Francisco; López Izquierdo, Raúl; Sanz García, Ancor; del Pozo Vegas, Carlos; Castro Villamor, Miguel Ángel ; et al.; Novel Prehospital Phenotypes and Outcomes in Adult-Patients with Acute Disease; Springer; Journal of Medical Systems; 46; 7; 6-2022; 1-10
1573-689X
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1007/s10916-022-01825-z
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1842980503484891136
score 12.993085

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