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The COVEG score to predict severity and mortality among hospitalized patients with COVID-19
Mohamed El-Kassas1, Maha El Gaafary2, Mohamed Elbadry3
1Endemic Medicine Department, Faculty of Medicine, Helwan University, Cairo, Egypt. m_elkassas@yahoo.com.
Insights
A new COVEG score accurately predicts COVID-19 severity and mortality in hospitalized patients. This tool aids in determining admission criteria and reducing patient deaths.
Area of Science:
- Infectious Diseases
- Clinical Medicine
- Biostatistics
Background:
- Predicting COVID-19 severity and mortality is crucial for effective patient management and resource allocation.
- Identifying key clinical and laboratory features can inform admission criteria and improve outcomes for hospitalized patients.
Purpose of the Study:
- To evaluate clinical-laboratory features of hospitalized COVID-19 patients.
- To develop and validate a novel scoring system (COVEG score) for predicting COVID-19 severity and mortality.
Main Methods:
- Retrospective cohort study of 1308 COVID-19 patients from five Egyptian university hospitals.
- Analysis of demographics, comorbidities, clinical data, and laboratory parameters.
- Development of the COVEG severity and mortality scores using regression analysis and ROC curves.
Main Results:
- The study included 1308 patients (76.1% moderate, 23.9% severe COVID-19), with an overall mortality of 12.6%.
- Key predictors for severity included age, D-dimer, serum ferritin, C-reactive protein, and neutrophil:lymphocyte ratio.
- The COVEG severity score and COVEG mortality score demonstrated high predictive accuracy (AUC 0.882 and 0.883, respectively).
Conclusions:
- The developed COVEG score accurately predicts both the severity and mortality of COVID-19 in hospitalized patients.
- The COVEG score can be a valuable tool for clinical decision-making, potentially improving patient outcomes and reducing mortality.
Introduction:
COVID-19 severity and mortality predictors could determine admission criteria and reduce mortality. We aimed to evaluate the clinical-laboratory features of hospitalized patients with COVID-19 to develop a novel score of severity and mortality.
Methodology:
This retrospective cohort study was conducted using data from patients with COVID-19 who were admitted to five Egyptian university hospitals. Demographics, comorbidities, clinical manifestations, laboratory parameters, the duration of hospitalization, and disease outcome were analyzed, and a score to predict severity and mortality was developed.
Results:
A total of 1308 patients with COVID-19, with 996 (76.1%) being moderate and 312 (23.9%) being severe cases, were included. The mean age was 46.5 ± 17.1 years, and 61.6% were males. The overall mortality was 12.6%. Regression analysis determined significant predictors, and a ROC curve defined cut-off values. The COVEG severity score was defined by age ≥ 54, D-dimer ≥ 0.795, serum ferritin ≥ 406, C-reactive protein ≥ 30.1, and neutrophil: lymphocyte ratio ≥ 2.88. The COVEG mortality score was based on COVEG severity and the presence of cardiac diseases. Both COVEG scores had high predictive values (area under the curve 0.882 and 0.883, respectively).
Conclusions:
COVEG score predicts the severity and mortality of patients with COVID-19 accurately.
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