Predicting clinical outcome with phenotypic clusters in COVID-19 pneumonia: an analysis of 12,066 hospitalized patients from the spanish registry SEMI-COVID-19
Autor/a
Rubio-Rivas, Manuel
Corbella Virós, Xavier
Mora-Luján, José María
Loureiro-Amigo, José
López Sampalo, Almudena
Yera Bergua, Carmen
Esteve Atiénzar, Pedro Jesús
Díez García, Luis Felipe
Gonzalez Ferrer, Ruth
Plaza Canteli, Susana
Pérez Piñeiro, Antía
Cortés Rodríguez, Begoña
Jorquer Vidal, Leyre
Pérez Catalán, Ignacio
León Téllez, Marta
Martín Oterino, José Ángel
Martín González, María Candelaria
Serrano Carrillo de Albornoz, José Luis
García Sardon, Eva
Alcalá Pedrajas, José Nicolás
Martin-Urda Diez-Canseco, Anabel
Esteban Giner, María José
Tellería Gómez, Pablo
Ramos-Rincón, José Manuel
Gómez-Huelgas, Ricardo
Fecha de publicación
2020ISSN
2077-0383
Resumen
(1) Background: Different clinical presentations in COVID-19 are described to date, from mild to severe cases. This study aims to identify different clinical phenotypes in COVID-19 pneumonia using cluster analysis and to assess the prognostic impact among identified clusters in such patients. (2) Methods: Cluster analysis including 11 phenotypic variables was performed in a large cohort of 12,066 COVID-19 patients, collected and followed-up from 1 March to 31 July 2020, from the nationwide Spanish Society of Internal Medicine (SEMI)-COVID-19 Registry. (3) Results: Of the total of 12,066 patients included in the study, most were males (7052, 58.5%) and Caucasian (10,635, 89.5%), with a mean age at diagnosis of 67 years (standard deviation (SD) 16). The main pre-admission comorbidities were arterial hypertension (6030, 50%), hyperlipidemia (4741, 39.4%) and diabetes mellitus (2309, 19.2%). The average number of days from COVID-19 symptom onset to hospital admission was 6.7 (SD 7). The triad of fever, cough, and dyspnea was present almost uniformly in all 4 clinical phenotypes identified by clustering. Cluster C1 (8737 patients, 72.4%) was the largest, and comprised patients with the triad alone. Cluster C2 (1196 patients, 9.9%) also presented with ageusia and anosmia; cluster C3 (880 patients, 7.3%) also had arthromyalgia, headache, and sore throat; and cluster C4 (1253 patients, 10.4%) also manifested with diarrhea, vomiting, and abdominal pain. Compared to each other, cluster C1 presented the highest in-hospital mortality (24.1% vs. 4.3% vs. 14.7% vs. 18.6%; p < 0.001). The multivariate study identified age, gender (male), body mass index (BMI), arterial hypertension, chronic obstructive pulmonary disease (COPD), ischemic cardiopathy, chronic heart failure, chronic hepatopathy, Charlson’s index, heart rate and respiratory rate upon admission >20 bpm, lower PaO2/FiO2 at admission, higher levels of C-reactive protein (CRP) and lactate dehydrogenase (LDH), and the phenotypic cluster as independent factors for in-hospital death. (4) Conclusions: The present study identified 4 phenotypic clusters in patients with COVID-19 pneumonia, which predicted the in-hospital prognosis of clinical outcomes.
Tipo de documento
Artículo
Versión del documento
Versión publicada
Lengua
English
Materias (CDU)
61 - Medicina
Palabras clave
COVID-19 (malaltia)
Pneumònia
Hipertensió
COVID-19
Neumonía
Hipertensión
COVID-19
Pneumonia
Hypertension
Páginas
19
Publicado por
MDPI
Colección
9; 11
Publicado en
Journal of Clinical Medicine
Citación
Rubio-Rivas, Manuel; Corbella Viròs, Xavier; Mora-Luján, José María [et al.]. Predicting clinical outcome with phenotypic clusters in COVID-19 pneumonia: an analysis of 12,066 hospitalized patients from the spanish registry SEMI-COVID-19. Journal of Clinical Medicine, 2020, 9(11), p. 1-19. Disponible en: <https://www.mdpi.com/2077-0383/9/11/3488>. Fecha de acceso: 17 dic. 2020. DOI: 10.3390/jcm9113488
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© 2020 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 (http://creativecommons.org/licenses/by/4.0/).
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