Cargando…

Multiple Approaches at Admission Based on Lung Ultrasound and Biomarkers Improves Risk Identification in COVID-19 Patients

Background: Risk stratification of COVID-19 patients is fundamental to improving prognosis and selecting the right treatment. We hypothesized that a combination of lung ultrasound (LUZ-score), biomarkers (sST2), and clinical models (PANDEMYC score) could be useful to improve risk stratification. Met...

Descripción completa

Detalles Bibliográficos
Autores principales: Jorge, Rubio-Gracia, Marta, Sánchez-Marteles, Vanesa, Garcés-Horna, Luis, Martínez-Lostao, Fernando, Ruiz-Laiglesia, Silvia, Crespo-Aznarez, Natacha, Peña-Fresneda, Borja, Gracia-Tello, Alberto, Cebollada, Patricia, Carrera-Lasfuentes, Ignacio, Pérez-Calvo Juan, Ignacio, Giménez-López
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8658110/
https://www.ncbi.nlm.nih.gov/pubmed/34884180
http://dx.doi.org/10.3390/jcm10235478
_version_ 1784612652529483776
author Jorge, Rubio-Gracia
Marta, Sánchez-Marteles
Vanesa, Garcés-Horna
Luis, Martínez-Lostao
Fernando, Ruiz-Laiglesia
Silvia, Crespo-Aznarez
Natacha, Peña-Fresneda
Borja, Gracia-Tello
Alberto, Cebollada
Patricia, Carrera-Lasfuentes
Ignacio, Pérez-Calvo Juan
Ignacio, Giménez-López
author_facet Jorge, Rubio-Gracia
Marta, Sánchez-Marteles
Vanesa, Garcés-Horna
Luis, Martínez-Lostao
Fernando, Ruiz-Laiglesia
Silvia, Crespo-Aznarez
Natacha, Peña-Fresneda
Borja, Gracia-Tello
Alberto, Cebollada
Patricia, Carrera-Lasfuentes
Ignacio, Pérez-Calvo Juan
Ignacio, Giménez-López
author_sort Jorge, Rubio-Gracia
collection PubMed
description Background: Risk stratification of COVID-19 patients is fundamental to improving prognosis and selecting the right treatment. We hypothesized that a combination of lung ultrasound (LUZ-score), biomarkers (sST2), and clinical models (PANDEMYC score) could be useful to improve risk stratification. Methods: This was a prospective cohort study designed to analyze the prognostic value of lung ultrasound, sST2, and PANDEMYC score in COVID-19 patients. The primary endpoint was in-hospital death and/or admission to the intensive care unit. The total length of hospital stay, increase of oxygen flow, or escalated medical treatment during the first 72 h were secondary endpoints. Results: a total of 144 patients were included; the mean age was 57.5 ± 12.78 years. The median PANDEMYC score was 243 (52), the median LUZ-score was 21 (10), and the median sST2 was 53.1 ng/mL (30.9). Soluble ST2 showed the best predictive capacity for the primary endpoint (AUC = 0.764 (0.658–0.871); p = 0.001), towards the PANDEMYC score (AUC = 0.762 (0.655–0.870); p = 0.001) and LUZ-score (AUC = 0.749 (0.596–0.901); p = 0.002). Taken together, these three tools significantly improved the risk capacity (AUC = 0.840 (0.727–0.953); p ≤ 0.001). Conclusions: The PANDEMYC score, lung ultrasound, and sST2 concentrations upon admission for COVID-19 are independent predictors of intra-hospital death and/or the need for admission to the ICU for mechanical ventilation. The combination of these predictive tools improves the predictive power compared to each one separately. The use of decision trees, based on multivariate models, could be useful in clinical practice.
format Online
Article
Text
id pubmed-8658110
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-86581102021-12-10 Multiple Approaches at Admission Based on Lung Ultrasound and Biomarkers Improves Risk Identification in COVID-19 Patients Jorge, Rubio-Gracia Marta, Sánchez-Marteles Vanesa, Garcés-Horna Luis, Martínez-Lostao Fernando, Ruiz-Laiglesia Silvia, Crespo-Aznarez Natacha, Peña-Fresneda Borja, Gracia-Tello Alberto, Cebollada Patricia, Carrera-Lasfuentes Ignacio, Pérez-Calvo Juan Ignacio, Giménez-López J Clin Med Article Background: Risk stratification of COVID-19 patients is fundamental to improving prognosis and selecting the right treatment. We hypothesized that a combination of lung ultrasound (LUZ-score), biomarkers (sST2), and clinical models (PANDEMYC score) could be useful to improve risk stratification. Methods: This was a prospective cohort study designed to analyze the prognostic value of lung ultrasound, sST2, and PANDEMYC score in COVID-19 patients. The primary endpoint was in-hospital death and/or admission to the intensive care unit. The total length of hospital stay, increase of oxygen flow, or escalated medical treatment during the first 72 h were secondary endpoints. Results: a total of 144 patients were included; the mean age was 57.5 ± 12.78 years. The median PANDEMYC score was 243 (52), the median LUZ-score was 21 (10), and the median sST2 was 53.1 ng/mL (30.9). Soluble ST2 showed the best predictive capacity for the primary endpoint (AUC = 0.764 (0.658–0.871); p = 0.001), towards the PANDEMYC score (AUC = 0.762 (0.655–0.870); p = 0.001) and LUZ-score (AUC = 0.749 (0.596–0.901); p = 0.002). Taken together, these three tools significantly improved the risk capacity (AUC = 0.840 (0.727–0.953); p ≤ 0.001). Conclusions: The PANDEMYC score, lung ultrasound, and sST2 concentrations upon admission for COVID-19 are independent predictors of intra-hospital death and/or the need for admission to the ICU for mechanical ventilation. The combination of these predictive tools improves the predictive power compared to each one separately. The use of decision trees, based on multivariate models, could be useful in clinical practice. MDPI 2021-11-23 /pmc/articles/PMC8658110/ /pubmed/34884180 http://dx.doi.org/10.3390/jcm10235478 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Jorge, Rubio-Gracia
Marta, Sánchez-Marteles
Vanesa, Garcés-Horna
Luis, Martínez-Lostao
Fernando, Ruiz-Laiglesia
Silvia, Crespo-Aznarez
Natacha, Peña-Fresneda
Borja, Gracia-Tello
Alberto, Cebollada
Patricia, Carrera-Lasfuentes
Ignacio, Pérez-Calvo Juan
Ignacio, Giménez-López
Multiple Approaches at Admission Based on Lung Ultrasound and Biomarkers Improves Risk Identification in COVID-19 Patients
title Multiple Approaches at Admission Based on Lung Ultrasound and Biomarkers Improves Risk Identification in COVID-19 Patients
title_full Multiple Approaches at Admission Based on Lung Ultrasound and Biomarkers Improves Risk Identification in COVID-19 Patients
title_fullStr Multiple Approaches at Admission Based on Lung Ultrasound and Biomarkers Improves Risk Identification in COVID-19 Patients
title_full_unstemmed Multiple Approaches at Admission Based on Lung Ultrasound and Biomarkers Improves Risk Identification in COVID-19 Patients
title_short Multiple Approaches at Admission Based on Lung Ultrasound and Biomarkers Improves Risk Identification in COVID-19 Patients
title_sort multiple approaches at admission based on lung ultrasound and biomarkers improves risk identification in covid-19 patients
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8658110/
https://www.ncbi.nlm.nih.gov/pubmed/34884180
http://dx.doi.org/10.3390/jcm10235478
work_keys_str_mv AT jorgerubiogracia multipleapproachesatadmissionbasedonlungultrasoundandbiomarkersimprovesriskidentificationincovid19patients
AT martasanchezmarteles multipleapproachesatadmissionbasedonlungultrasoundandbiomarkersimprovesriskidentificationincovid19patients
AT vanesagarceshorna multipleapproachesatadmissionbasedonlungultrasoundandbiomarkersimprovesriskidentificationincovid19patients
AT luismartinezlostao multipleapproachesatadmissionbasedonlungultrasoundandbiomarkersimprovesriskidentificationincovid19patients
AT fernandoruizlaiglesia multipleapproachesatadmissionbasedonlungultrasoundandbiomarkersimprovesriskidentificationincovid19patients
AT silviacrespoaznarez multipleapproachesatadmissionbasedonlungultrasoundandbiomarkersimprovesriskidentificationincovid19patients
AT natachapenafresneda multipleapproachesatadmissionbasedonlungultrasoundandbiomarkersimprovesriskidentificationincovid19patients
AT borjagraciatello multipleapproachesatadmissionbasedonlungultrasoundandbiomarkersimprovesriskidentificationincovid19patients
AT albertocebollada multipleapproachesatadmissionbasedonlungultrasoundandbiomarkersimprovesriskidentificationincovid19patients
AT patriciacarreralasfuentes multipleapproachesatadmissionbasedonlungultrasoundandbiomarkersimprovesriskidentificationincovid19patients
AT ignacioperezcalvojuan multipleapproachesatadmissionbasedonlungultrasoundandbiomarkersimprovesriskidentificationincovid19patients
AT ignaciogimenezlopez multipleapproachesatadmissionbasedonlungultrasoundandbiomarkersimprovesriskidentificationincovid19patients