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Predicting Clinical Outcome with Phenotypic Clusters in COVID-19 Pneumonia: An Analysis of 12,066 Hospitalized Patients from the Spanish Registry SEMI-COVID-19
(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) Me...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7693215/ https://www.ncbi.nlm.nih.gov/pubmed/33137919 http://dx.doi.org/10.3390/jcm9113488 |
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author | Rubio-Rivas, Manuel Corbella, Xavier Mora-Luján, José María Loureiro-Amigo, Jose 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 |
author_facet | Rubio-Rivas, Manuel Corbella, Xavier Mora-Luján, José María Loureiro-Amigo, Jose 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 |
author_sort | Rubio-Rivas, Manuel |
collection | PubMed |
description | (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. |
format | Online Article Text |
id | pubmed-7693215 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-76932152020-11-28 Predicting Clinical Outcome with Phenotypic Clusters in COVID-19 Pneumonia: An Analysis of 12,066 Hospitalized Patients from the Spanish Registry SEMI-COVID-19 Rubio-Rivas, Manuel Corbella, Xavier Mora-Luján, José María Loureiro-Amigo, Jose 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 J Clin Med Article (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. MDPI 2020-10-29 /pmc/articles/PMC7693215/ /pubmed/33137919 http://dx.doi.org/10.3390/jcm9113488 Text en © 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/). |
spellingShingle | Article Rubio-Rivas, Manuel Corbella, Xavier Mora-Luján, José María Loureiro-Amigo, Jose 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 Predicting Clinical Outcome with Phenotypic Clusters in COVID-19 Pneumonia: An Analysis of 12,066 Hospitalized Patients from the Spanish Registry SEMI-COVID-19 |
title | Predicting Clinical Outcome with Phenotypic Clusters in COVID-19 Pneumonia: An Analysis of 12,066 Hospitalized Patients from the Spanish Registry SEMI-COVID-19 |
title_full | Predicting Clinical Outcome with Phenotypic Clusters in COVID-19 Pneumonia: An Analysis of 12,066 Hospitalized Patients from the Spanish Registry SEMI-COVID-19 |
title_fullStr | Predicting Clinical Outcome with Phenotypic Clusters in COVID-19 Pneumonia: An Analysis of 12,066 Hospitalized Patients from the Spanish Registry SEMI-COVID-19 |
title_full_unstemmed | Predicting Clinical Outcome with Phenotypic Clusters in COVID-19 Pneumonia: An Analysis of 12,066 Hospitalized Patients from the Spanish Registry SEMI-COVID-19 |
title_short | Predicting Clinical Outcome with Phenotypic Clusters in COVID-19 Pneumonia: An Analysis of 12,066 Hospitalized Patients from the Spanish Registry SEMI-COVID-19 |
title_sort | predicting clinical outcome with phenotypic clusters in covid-19 pneumonia: an analysis of 12,066 hospitalized patients from the spanish registry semi-covid-19 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7693215/ https://www.ncbi.nlm.nih.gov/pubmed/33137919 http://dx.doi.org/10.3390/jcm9113488 |
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