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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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Journal of Clinical Medicine |
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| Online Access: | https://www.mdpi.com/2077-0383/11/12/3327 |
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| author | Rodrigo San-Cristobal Roberto Martín-Hernández Omar Ramos-Lopez Diego Martinez-Urbistondo Víctor Micó Gonzalo Colmenarejo Paula Villares Fernandez Lidia Daimiel Jose Alfredo Martínez |
| author_facet | Rodrigo San-Cristobal Roberto Martín-Hernández Omar Ramos-Lopez Diego Martinez-Urbistondo Víctor Micó Gonzalo Colmenarejo Paula Villares Fernandez Lidia Daimiel Jose Alfredo Martínez |
| author_sort | Rodrigo San-Cristobal |
| collection | DOAJ |
| 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 <i>p</i> < 0.001 and 0.749 vs. 0.807 with <i>p</i> < 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. |
| first_indexed | 2024-03-09T23:28:25Z |
| format | Article |
| id | doaj.art-92a0b8aabaf04e4aa5eb51ee92ea864a |
| institution | Directory Open Access Journal |
| issn | 2077-0383 |
| language | English |
| last_indexed | 2024-03-09T23:28:25Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Clinical Medicine |
| spelling | doaj.art-92a0b8aabaf04e4aa5eb51ee92ea864a2023-11-23T17:14:10ZengMDPI AGJournal of Clinical Medicine2077-03832022-06-011112332710.3390/jcm11123327Longwise Cluster Analysis for the Prediction of COVID-19 Severity within 72 h of Admission: COVID-DATA-SAVE-LIFES CohortRodrigo San-Cristobal0Roberto Martín-Hernández1Omar Ramos-Lopez2Diego Martinez-Urbistondo3Víctor Micó4Gonzalo Colmenarejo5Paula Villares Fernandez6Lidia Daimiel7Jose Alfredo Martínez8Precision Nutrition and Cardiometabolic Health Researh Program, Institute on Food and Health Sciences (Institute IMDEA Food), 28049 Madrid, SpainBiostatistics & Bioinformatics Unit, Madrid Institute for Advanced Studies (IMDEA) Food, CEI UAM + CSIS, 28049 Madrid, SpainMedicine and Psychology School, Autonomous University of Baja California, Tijuana 22390, Baja California, MexicoInternal Medicine Department, Hospital Universitario HM Sanchinarro, 28050 Madrid, SpainPrecision Nutrition and Cardiometabolic Health Researh Program, Institute on Food and Health Sciences (Institute IMDEA Food), 28049 Madrid, SpainBiostatistics & Bioinformatics Unit, Madrid Institute for Advanced Studies (IMDEA) Food, CEI UAM + CSIS, 28049 Madrid, SpainInternal Medicine Department, Hospital Universitario HM Sanchinarro, 28050 Madrid, SpainNutritional Control of the Epigenome Group, IMDEA Food Institute, CEI UAM + CSIC, 28049 Madrid, SpainPrecision Nutrition and Cardiometabolic Health Researh Program, Institute on Food and Health Sciences (Institute IMDEA Food), 28049 Madrid, SpainThe 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 <i>p</i> < 0.001 and 0.749 vs. 0.807 with <i>p</i> < 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.https://www.mdpi.com/2077-0383/11/12/3327COVID-19Charlson Comorbidities Indexcluster analysislongitudinal clusterindividualized management |
| spellingShingle | Rodrigo San-Cristobal Roberto Martín-Hernández Omar Ramos-Lopez Diego Martinez-Urbistondo Víctor Micó Gonzalo Colmenarejo Paula Villares Fernandez Lidia Daimiel Jose Alfredo Martínez Longwise Cluster Analysis for the Prediction of COVID-19 Severity within 72 h of Admission: COVID-DATA-SAVE-LIFES Cohort Journal of Clinical Medicine COVID-19 Charlson Comorbidities Index cluster analysis longitudinal cluster individualized management |
| 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 | COVID-19 Charlson Comorbidities Index cluster analysis longitudinal cluster individualized management |
| url | https://www.mdpi.com/2077-0383/11/12/3327 |
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