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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Bibliographic Details
Published in:Journal of clinical medicine 2022-06, Vol.11 (12), p.3327
Main Authors: San-Cristobal, Rodrigo, Martín-Hernández, Roberto, Ramos-Lopez, Omar, Martinez-Urbistondo, Diego, Micó, Víctor, Colmenarejo, Gonzalo, Villares Fernandez, Paula, Daimiel, Lidia, Martínez, Jose Alfredo
Format: Article
Language:English
Subjects:
Biomarkers
Clinical medicine
Cluster analysis
Comorbidity
Coronaviruses
COVID-19
Disease transmission
Electronic health records
Fatalities
Hemoglobin
Hospitalization
Hospitals
Laboratories
Metabolism
Oxygen saturation
Pandemics
Patients
Severe acute respiratory syndrome coronavirus 2
Ventilators
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Summary: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.
ISSN:2077-0383
2077-0383
DOI:10.3390/jcm11123327