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Longwise Cluster Analysis for the Prediction of COVID-19 Severity within 72 h of Admission: COVID-DATA-SAVE-LIFES Cohort

The use of routine laboratory biomarkers plays a key role in decision making in the clinical practice of COVID-19, allowing the development of clinical screening tools for personalized treatments. This study performed a short-term longitudinal cluster from patients with COVID-19 based on biochemical...

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Autores principales: San-Cristobal, Rodrigo, Martín-Hernández, Roberto, Ramos-Lopez, Omar, Martinez-Urbistondo, Diego, Micó, Víctor, Colmenarejo, Gonzalo, Villares Fernandez, Paula, Daimiel, Lidia, Martínez, Jose Alfredo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9224935/
https://www.ncbi.nlm.nih.gov/pubmed/35743398
http://dx.doi.org/10.3390/jcm11123327
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author San-Cristobal, Rodrigo
Martín-Hernández, Roberto
Ramos-Lopez, Omar
Martinez-Urbistondo, Diego
Micó, Víctor
Colmenarejo, Gonzalo
Villares Fernandez, Paula
Daimiel, Lidia
Martínez, Jose Alfredo
author_facet San-Cristobal, Rodrigo
Martín-Hernández, Roberto
Ramos-Lopez, Omar
Martinez-Urbistondo, Diego
Micó, Víctor
Colmenarejo, Gonzalo
Villares Fernandez, Paula
Daimiel, Lidia
Martínez, Jose Alfredo
author_sort San-Cristobal, Rodrigo
collection PubMed
description The use of routine laboratory biomarkers plays a key role in decision making in the clinical practice of COVID-19, allowing the development of clinical screening tools for personalized treatments. This study performed a short-term longitudinal cluster from patients with COVID-19 based on biochemical measurements for the first 72 h after hospitalization. Clinical and biochemical variables from 1039 confirmed COVID-19 patients framed on the “COVID Data Save Lives” were grouped in 24-h blocks to perform a longitudinal k-means clustering algorithm to the trajectories. The final solution of the three clusters showed a strong association with different clinical severity outcomes (OR for death: Cluster A reference, Cluster B 12.83 CI: 6.11–30.54, and Cluster C 14.29 CI: 6.66–34.43; OR for ventilation: Cluster-B 2.22 CI: 1.64–3.01, and Cluster-C 1.71 CI: 1.08–2.76), improving the AUC of the models in terms of age, sex, oxygen concentration, and the Charlson Comorbidities Index (0.810 vs. 0.871 with p < 0.001 and 0.749 vs. 0.807 with p < 0.001, respectively). Patient diagnoses and prognoses remarkably diverged between the three clusters obtained, evidencing that data-driven technologies devised for the screening, analysis, prediction, and tracking of patients play a key role in the application of individualized management of the COVID-19 pandemics.
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spelling pubmed-92249352022-06-24 Longwise Cluster Analysis for the Prediction of COVID-19 Severity within 72 h of Admission: COVID-DATA-SAVE-LIFES Cohort San-Cristobal, Rodrigo Martín-Hernández, Roberto Ramos-Lopez, Omar Martinez-Urbistondo, Diego Micó, Víctor Colmenarejo, Gonzalo Villares Fernandez, Paula Daimiel, Lidia Martínez, Jose Alfredo J Clin Med Article The use of routine laboratory biomarkers plays a key role in decision making in the clinical practice of COVID-19, allowing the development of clinical screening tools for personalized treatments. This study performed a short-term longitudinal cluster from patients with COVID-19 based on biochemical measurements for the first 72 h after hospitalization. Clinical and biochemical variables from 1039 confirmed COVID-19 patients framed on the “COVID Data Save Lives” were grouped in 24-h blocks to perform a longitudinal k-means clustering algorithm to the trajectories. The final solution of the three clusters showed a strong association with different clinical severity outcomes (OR for death: Cluster A reference, Cluster B 12.83 CI: 6.11–30.54, and Cluster C 14.29 CI: 6.66–34.43; OR for ventilation: Cluster-B 2.22 CI: 1.64–3.01, and Cluster-C 1.71 CI: 1.08–2.76), improving the AUC of the models in terms of age, sex, oxygen concentration, and the Charlson Comorbidities Index (0.810 vs. 0.871 with p < 0.001 and 0.749 vs. 0.807 with p < 0.001, respectively). Patient diagnoses and prognoses remarkably diverged between the three clusters obtained, evidencing that data-driven technologies devised for the screening, analysis, prediction, and tracking of patients play a key role in the application of individualized management of the COVID-19 pandemics. MDPI 2022-06-10 /pmc/articles/PMC9224935/ /pubmed/35743398 http://dx.doi.org/10.3390/jcm11123327 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
San-Cristobal, Rodrigo
Martín-Hernández, Roberto
Ramos-Lopez, Omar
Martinez-Urbistondo, Diego
Micó, Víctor
Colmenarejo, Gonzalo
Villares Fernandez, Paula
Daimiel, Lidia
Martínez, Jose Alfredo
Longwise Cluster Analysis for the Prediction of COVID-19 Severity within 72 h of Admission: COVID-DATA-SAVE-LIFES Cohort
title Longwise Cluster Analysis for the Prediction of COVID-19 Severity within 72 h of Admission: COVID-DATA-SAVE-LIFES Cohort
title_full Longwise Cluster Analysis for the Prediction of COVID-19 Severity within 72 h of Admission: COVID-DATA-SAVE-LIFES Cohort
title_fullStr Longwise Cluster Analysis for the Prediction of COVID-19 Severity within 72 h of Admission: COVID-DATA-SAVE-LIFES Cohort
title_full_unstemmed Longwise Cluster Analysis for the Prediction of COVID-19 Severity within 72 h of Admission: COVID-DATA-SAVE-LIFES Cohort
title_short Longwise Cluster Analysis for the Prediction of COVID-19 Severity within 72 h of Admission: COVID-DATA-SAVE-LIFES Cohort
title_sort longwise cluster analysis for the prediction of covid-19 severity within 72 h of admission: covid-data-save-lifes cohort
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9224935/
https://www.ncbi.nlm.nih.gov/pubmed/35743398
http://dx.doi.org/10.3390/jcm11123327
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