Publications by authors named "J M Casas-Rojo"
Prognostic Value of D-dimer to Lymphocyte Ratio (DLR) in Hospitalized Coronavirus Disease 2019 (COVID-19) Patients: A Validation Study in a National Cohort.
Viruses
February 2024
Background: This study aimed to validate the role of the D-dimer to lymphocyte ratio (DLR) for mortality prediction in a large national cohort of hospitalized coronavirus disease 2019 (COVID-19) patients.
Methods: A retrospective, multicenter, observational study that included hospitalized patients due to SARS-CoV-2 infection in Spain was conducted from March 2020 to March 2022. All biomarkers and laboratory indices analyzed were measured once at admission.
Article Synopsis
- The study analyzed COVID-19 hospital admissions from 2020 in Spain, focusing on older patients during two pandemic waves, to evaluate mortality differences.
- Results showed a mortality rate of 20.4% during the first wave and 17.2% during the second wave, particularly among patients aged 70 and older, who had a significant mortality reduction of 7.6%.
- The lower mortality in the second wave may be due to improved healthcare standards, greater clinical experience, or less strain on the healthcare system, independent of patient or disease severity.
Article Synopsis
- New variants of SARS-CoV-2, changes in public health measures, and decreased immunity in high-risk groups are leading to predictions of increased hospitalizations and intensive care admissions, highlighting a need for effective Early Warning Scores (EWSs) to predict patient complications within 24-48 hours.* -
- The developed COVID-19 Early Warning Score (COEWS) relies on easily accessible laboratory parameters, distinguishing it from existing models like NEWS2, and assesses risk in both vaccinated and unvaccinated patients.* -
- The COEWS model incorporates key lab results, transforming predictive coefficients into individual scores that help identify patients at risk of mechanical ventilation or death; its predictive performance shows promising results with a discrimination score of
Article Synopsis
- Researchers developed a machine learning model using Gradient Boosting Decision Trees (GBDT) to predict mortality in COVID-19 hospitalized patients, utilizing data from the Spanish SEMI-COVID-19 registry which included over 24,000 cases.
- The model employed advanced classifiers like CatBoost and BorutaShap to identify key indicators and risk levels for mortality, achieving a notable AUC performance of 84.76 in a test group likely containing vaccinated individuals.
- The study highlights the model's high predictive capacity despite needing a significant number of predictors, indicating its potential utility in clinical settings for managing COVID-19 patient care.
The aim of this study was to analyze whether the coronavirus disease 2019 (COVID-19) vaccine reduces mortality in patients with moderate or severe COVID-19 disease requiring oxygen therapy. A retrospective cohort study, with data from 148 hospitals in both Spain (111 hospitals) and Argentina (37 hospitals), was conducted. We evaluated hospitalized patients for COVID-19 older than 18 years with oxygen requirements.
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