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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9224935 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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