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Clinical Phenotypes and Mortality Biomarkers: A Study Focused on COVID-19 Patients with Neurological Diseases in Intensive Care Units

Purpose: To identify clinical phenotypes and biomarkers for best mortality prediction considering age, symptoms and comorbidities in COVID-19 patients with chronic neurological diseases in intensive care units (ICUs). Subjects and Methods: Data included 1252 COVID-19 patients admitted to ICUs in Cub...

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Autores principales: Morales Chacón, Lilia María, Galán García, Lídice, Cruz Hernández, Tania Margarita, Pavón Fuentes, Nancy, Maragoto Rizo, Carlos, Morales Suarez, Ileana, Morales Chacón, Odalys, Abad Molina, Elianne, Rocha Arrieta, Luisa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9312189/
https://www.ncbi.nlm.nih.gov/pubmed/35877304
http://dx.doi.org/10.3390/bs12070234
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author Morales Chacón, Lilia María
Galán García, Lídice
Cruz Hernández, Tania Margarita
Pavón Fuentes, Nancy
Maragoto Rizo, Carlos
Morales Suarez, Ileana
Morales Chacón, Odalys
Abad Molina, Elianne
Rocha Arrieta, Luisa
author_facet Morales Chacón, Lilia María
Galán García, Lídice
Cruz Hernández, Tania Margarita
Pavón Fuentes, Nancy
Maragoto Rizo, Carlos
Morales Suarez, Ileana
Morales Chacón, Odalys
Abad Molina, Elianne
Rocha Arrieta, Luisa
author_sort Morales Chacón, Lilia María
collection PubMed
description Purpose: To identify clinical phenotypes and biomarkers for best mortality prediction considering age, symptoms and comorbidities in COVID-19 patients with chronic neurological diseases in intensive care units (ICUs). Subjects and Methods: Data included 1252 COVID-19 patients admitted to ICUs in Cuba between January and August 2021. A k-means algorithm based on unsupervised learning was used to identify clinical patterns related to symptoms, comorbidities and age. The Stable Sparse Classifiers procedure (SSC) was employed for predicting mortality. The classification performance was assessed using the area under the receiver operating curve (AUC). Results: Six phenotypes using a modified v-fold cross validation for the k-means algorithm were identified: phenotype class 1, mean age 72.3 years (ys)—hypertension and coronary artery disease, alongside typical COVID-19 symptoms; class 2, mean age 63 ys—asthma, cough and fever; class 3, mean age 74.5 ys—hypertension, diabetes and cough; class 4, mean age 67.8 ys—hypertension and no symptoms; class 5, mean age 53 ys—cough and no comorbidities; class 6, mean age 60 ys—without symptoms or comorbidities. The chronic neurological disease (CND) percentage was distributed in the six phenotypes, predominantly in phenotypes of classes 3 (24.72%) and 4 (35,39%); χ² (5) 11.0129 p = 0.051134. The cerebrovascular disease was concentrated in classes 3 and 4; χ² (5) = 36.63, p = 0.000001. The mortality rate totaled 325 (25.79%), of which 56 (17.23%) had chronic neurological diseases. The highest in-hospital mortality rates were found in phenotypes 1 (37.22%) and 3 (33.98%). The SSC revealed that a neurological symptom (ageusia), together with two neurological diseases (cerebrovascular disease and Parkinson’s disease), and in addition to ICU days, age and specific symptoms (fever, cough, dyspnea and chilliness) as well as particular comorbidities (hypertension, diabetes and asthma) indicated the best prediction performance (AUC = 0.67). Conclusions: The identification of clinical phenotypes and mortality biomarkers using practical variables and robust statistical methodologies make several noteworthy contributions to basic and experimental investigations for distinguishing the COVID-19 clinical spectrum and predicting mortality.
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spelling pubmed-93121892022-07-26 Clinical Phenotypes and Mortality Biomarkers: A Study Focused on COVID-19 Patients with Neurological Diseases in Intensive Care Units Morales Chacón, Lilia María Galán García, Lídice Cruz Hernández, Tania Margarita Pavón Fuentes, Nancy Maragoto Rizo, Carlos Morales Suarez, Ileana Morales Chacón, Odalys Abad Molina, Elianne Rocha Arrieta, Luisa Behav Sci (Basel) Article Purpose: To identify clinical phenotypes and biomarkers for best mortality prediction considering age, symptoms and comorbidities in COVID-19 patients with chronic neurological diseases in intensive care units (ICUs). Subjects and Methods: Data included 1252 COVID-19 patients admitted to ICUs in Cuba between January and August 2021. A k-means algorithm based on unsupervised learning was used to identify clinical patterns related to symptoms, comorbidities and age. The Stable Sparse Classifiers procedure (SSC) was employed for predicting mortality. The classification performance was assessed using the area under the receiver operating curve (AUC). Results: Six phenotypes using a modified v-fold cross validation for the k-means algorithm were identified: phenotype class 1, mean age 72.3 years (ys)—hypertension and coronary artery disease, alongside typical COVID-19 symptoms; class 2, mean age 63 ys—asthma, cough and fever; class 3, mean age 74.5 ys—hypertension, diabetes and cough; class 4, mean age 67.8 ys—hypertension and no symptoms; class 5, mean age 53 ys—cough and no comorbidities; class 6, mean age 60 ys—without symptoms or comorbidities. The chronic neurological disease (CND) percentage was distributed in the six phenotypes, predominantly in phenotypes of classes 3 (24.72%) and 4 (35,39%); χ² (5) 11.0129 p = 0.051134. The cerebrovascular disease was concentrated in classes 3 and 4; χ² (5) = 36.63, p = 0.000001. The mortality rate totaled 325 (25.79%), of which 56 (17.23%) had chronic neurological diseases. The highest in-hospital mortality rates were found in phenotypes 1 (37.22%) and 3 (33.98%). The SSC revealed that a neurological symptom (ageusia), together with two neurological diseases (cerebrovascular disease and Parkinson’s disease), and in addition to ICU days, age and specific symptoms (fever, cough, dyspnea and chilliness) as well as particular comorbidities (hypertension, diabetes and asthma) indicated the best prediction performance (AUC = 0.67). Conclusions: The identification of clinical phenotypes and mortality biomarkers using practical variables and robust statistical methodologies make several noteworthy contributions to basic and experimental investigations for distinguishing the COVID-19 clinical spectrum and predicting mortality. MDPI 2022-07-15 /pmc/articles/PMC9312189/ /pubmed/35877304 http://dx.doi.org/10.3390/bs12070234 Text en © 2022 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
Morales Chacón, Lilia María
Galán García, Lídice
Cruz Hernández, Tania Margarita
Pavón Fuentes, Nancy
Maragoto Rizo, Carlos
Morales Suarez, Ileana
Morales Chacón, Odalys
Abad Molina, Elianne
Rocha Arrieta, Luisa
Clinical Phenotypes and Mortality Biomarkers: A Study Focused on COVID-19 Patients with Neurological Diseases in Intensive Care Units
title Clinical Phenotypes and Mortality Biomarkers: A Study Focused on COVID-19 Patients with Neurological Diseases in Intensive Care Units
title_full Clinical Phenotypes and Mortality Biomarkers: A Study Focused on COVID-19 Patients with Neurological Diseases in Intensive Care Units
title_fullStr Clinical Phenotypes and Mortality Biomarkers: A Study Focused on COVID-19 Patients with Neurological Diseases in Intensive Care Units
title_full_unstemmed Clinical Phenotypes and Mortality Biomarkers: A Study Focused on COVID-19 Patients with Neurological Diseases in Intensive Care Units
title_short Clinical Phenotypes and Mortality Biomarkers: A Study Focused on COVID-19 Patients with Neurological Diseases in Intensive Care Units
title_sort clinical phenotypes and mortality biomarkers: a study focused on covid-19 patients with neurological diseases in intensive care units
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9312189/
https://www.ncbi.nlm.nih.gov/pubmed/35877304
http://dx.doi.org/10.3390/bs12070234
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