000109712 001__ 109712
000109712 005__ 20240118091947.0
000109712 0247_ $$2doi$$a10.3390/jcm10235478
000109712 0248_ $$2sideral$$a126778
000109712 037__ $$aART-2021-126778
000109712 041__ $$aeng
000109712 100__ $$aRubio-Gracia, J
000109712 245__ $$aMultiple approaches at admission based on lung ultrasound and biomarkers improves risk identification in COVID-19 patients
000109712 260__ $$c2021
000109712 5060_ $$aAccess copy available to the general public$$fUnrestricted
000109712 5203_ $$aBackground: 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. © 2021 by the authors. 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/).
000109712 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000109712 590__ $$a4.964$$b2021
000109712 592__ $$a1.04$$b2021
000109712 594__ $$a4.4$$b2021
000109712 591__ $$aMEDICINE, GENERAL & INTERNAL$$b55 / 172 = 0.32$$c2021$$dQ2$$eT1
000109712 593__ $$aMedicine (miscellaneous)$$c2021$$dQ1
000109712 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000109712 700__ $$aSánchez-Marteles, M
000109712 700__ $$aGarcés-Horna, V
000109712 700__ $$aMartínez-Lostao, L
000109712 700__ $$aRuiz-Laiglesia, F
000109712 700__ $$aCrespo-Aznarez, S
000109712 700__ $$aPeña-Fresneda, N
000109712 700__ $$aGracia-Tello, B
000109712 700__ $$aCebollada, A
000109712 700__ $$aCarrera-Lasfuentes, P
000109712 700__ $$0(orcid)0000-0003-2361-9941$$aPérez-Calvo, JI$$uUniversidad de Zaragoza
000109712 700__ $$0(orcid)0000-0002-6043-4869$$aGiménez-López, I$$uUniversidad de Zaragoza
000109712 7102_ $$11012$$2410$$aUniversidad de Zaragoza$$bDpto. Farmac.Fisiol.y Med.L.F.$$cÁrea Fisiología
000109712 7102_ $$11007$$2610$$aUniversidad de Zaragoza$$bDpto. Medicina, Psiqu. y Derm.$$cArea Medicina
000109712 773__ $$g10, 23 (2021), 5478 [11 pp]$$pJ. clin.med.$$tJournal of Clinical Medicine$$x2077-0383
000109712 8564_ $$s1177126$$uhttps://zaguan.unizar.es/record/109712/files/texto_completo.pdf$$yVersión publicada
000109712 8564_ $$s2732115$$uhttps://zaguan.unizar.es/record/109712/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000109712 909CO $$ooai:zaguan.unizar.es:109712$$particulos$$pdriver
000109712 951__ $$a2024-01-18-09:12:19
000109712 980__ $$aARTICLE