eprintid: 14278 rev_number: 8 eprint_status: archive userid: 2 dir: disk0/00/01/42/78 datestamp: 2024-09-19 23:30:07 lastmod: 2024-09-19 23:30:09 status_changed: 2024-09-19 23:30:07 type: article metadata_visibility: show creators_name: López-Izquierdo, Raúl creators_name: del Pozo Vegas, Carlos creators_name: Sanz-García, Ancor creators_name: Mayo Íscar, Agustín creators_name: Castro Villamor, Miguel A. creators_name: Silva Alvarado, Eduardo René creators_name: Gracia Villar, Santos creators_name: Dzul López, Luis Alonso creators_name: Aparicio Obregón, Silvia creators_name: Calderón Iglesias, Rubén creators_name: Soriano, Joan B. creators_name: Martín-Rodríguez, Francisco creators_id: creators_id: creators_id: creators_id: creators_id: creators_id: eduardo.silva@funiber.org creators_id: santos.gracia@uneatlantico.es creators_id: luis.dzul@uneatlantico.es creators_id: silvia.aparicio@uneatlantico.es creators_id: ruben.calderon@uneatlantico.es creators_id: creators_id: title: Clinical phenotypes and short-term outcomes based on prehospital point-of-care testing and on-scene vital signs ispublished: pub subjects: uneat_bm divisions: uneatlantico_produccion_cientifica divisions: unincol_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: uninipr_produccion_cientifica divisions: unic_produccion_cientifica divisions: uniromana_produccion_cientifica full_text_status: public keywords: Outcomes research; Predictive markers abstract: Emergency medical services (EMSs) face critical situations that require patient risk classification based on analytical and vital signs. We aimed to establish clustering-derived phenotypes based on prehospital analytical and vital signs that allow risk stratification. This was a prospective, multicenter, EMS-delivered, ambulance-based cohort study considering six advanced life support units, 38 basic life support units, and four tertiary hospitals in Spain. Adults with unselected acute diseases managed by the EMS and evacuated with discharge priority to emergency departments were considered between January 1, 2020, and June 30, 2023. Prehospital point-of-care testing and on-scene vital signs were used for the unsupervised machine learning method (clustering) to determine the phenotypes. Then phenotypes were compared with the primary outcome (cumulative mortality (all-cause) at 2, 7, and 30 days). A total of 7909 patients were included. The median (IQR) age was 64 (51–80) years, 41% were women, and 26% were living in rural areas. Three clusters were identified: alpha 16.2% (1281 patients), beta 28.8% (2279), and gamma 55% (4349). The mortality rates for alpha, beta and gamma at 2 days were 18.6%, 4.1%, and 0.8%, respectively; at 7 days, were 24.7%, 6.2%, and 1.7%; and at 30 days, were 33%, 10.2%, and 3.2%, respectively. Based on standard vital signs and blood test biomarkers in the prehospital scenario, three clusters were identified: alpha (high-risk), beta and gamma (medium- and low-risk, respectively). This permits the EMS system to quickly identify patients who are potentially compromised and to proactively implement the necessary interventions. date: 2024-07 publication: npj Digital Medicine volume: 7 number: 1 id_number: doi:10.1038/S41746-024-01194-6 refereed: TRUE issn: 2398-6352 official_url: http://doi.org/10.1038/S41746-024-01194-6 access: open language: en citation: Artículo Materias > Biomedicina Universidad Europea del Atlántico > Investigación > Producción Científica Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Universidad Internacional do Cuanza > Investigación > Producción Científica Universidad de La Romana > Investigación > Producción Científica Abierto Inglés Emergency medical services (EMSs) face critical situations that require patient risk classification based on analytical and vital signs. We aimed to establish clustering-derived phenotypes based on prehospital analytical and vital signs that allow risk stratification. This was a prospective, multicenter, EMS-delivered, ambulance-based cohort study considering six advanced life support units, 38 basic life support units, and four tertiary hospitals in Spain. Adults with unselected acute diseases managed by the EMS and evacuated with discharge priority to emergency departments were considered between January 1, 2020, and June 30, 2023. Prehospital point-of-care testing and on-scene vital signs were used for the unsupervised machine learning method (clustering) to determine the phenotypes. Then phenotypes were compared with the primary outcome (cumulative mortality (all-cause) at 2, 7, and 30 days). A total of 7909 patients were included. The median (IQR) age was 64 (51–80) years, 41% were women, and 26% were living in rural areas. Three clusters were identified: alpha 16.2% (1281 patients), beta 28.8% (2279), and gamma 55% (4349). The mortality rates for alpha, beta and gamma at 2 days were 18.6%, 4.1%, and 0.8%, respectively; at 7 days, were 24.7%, 6.2%, and 1.7%; and at 30 days, were 33%, 10.2%, and 3.2%, respectively. Based on standard vital signs and blood test biomarkers in the prehospital scenario, three clusters were identified: alpha (high-risk), beta and gamma (medium- and low-risk, respectively). This permits the EMS system to quickly identify patients who are potentially compromised and to proactively implement the necessary interventions. metadata López-Izquierdo, Raúl; del Pozo Vegas, Carlos; Sanz-García, Ancor; Mayo Íscar, Agustín; Castro Villamor, Miguel A.; Silva Alvarado, Eduardo René; Gracia Villar, Santos; Dzul López, Luis Alonso; Aparicio Obregón, Silvia; Calderón Iglesias, Rubén; Soriano, Joan B. y Martín-Rodríguez, Francisco mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, eduardo.silva@funiber.org, santos.gracia@uneatlantico.es, luis.dzul@uneatlantico.es, silvia.aparicio@uneatlantico.es, ruben.calderon@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2024) Clinical phenotypes and short-term outcomes based on prehospital point-of-care testing and on-scene vital signs. npj Digital Medicine, 7 (1). ISSN 2398-6352 document_url: http://repositorio.unib.org/id/eprint/14278/1/s41746-024-01194-6.pdf