The COVEG score to predict severity and mortality among hospitalized patients with COVID-19

El-Kassas, Mohamed; Gaafary, Maha El; Elbadry, Mohamed; Medhat, Mohamed Ahmed; Dabbous, Hany; Hassan, Essam A.; Abdelmoaty, Ahmed; Karam-Allah, Haidi; Sayed, Hamdy; Abdalazeem, Amr; Sherief, Ahmed Fouad; Fares, Eman; Alsadik, Maha E.; Moustafa, E. S. A.; Anwar, Wagida A.; Abdelghaffar, Hossam M.; Abdelghaffar, Khaled

doi:10.3855/jidc.15984

Cited by 2 publications

(1 citation statement)

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“…Another scoring system that was developed on data from 452 severe COVID-19 patients included D-dimer, lymphopenia, procalcitonin, age > 60 years, and coronary heart disease [ 17 ]. The mortality score COVEG was also created and defined by age ≥ 54, neutrophil–lymphocyte ratio ≥ 2.88, D-dimer ≥ 0.795, C-reactive protein ≥ 30.1, serum ferritin ≥ 406, and the presence of cardiac diseases [ 41 ]. Also, a more complex model was built incorporating twelve patient’s characteristics that could challenge its practical applicability [ 15 ].…”

Section: Discussionmentioning

confidence: 99%

Developing a mortality risk prediction model using data of 3663 hospitalized COVID-19 patients: a retrospective cohort study in an Egyptian University Hospital

et al. 2023

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Purpose Since the declaration of COVID-19 as a pandemic, a wide between-country variation was observed regarding in-hospital mortality and its predictors. Given the scarcity of local research and the need to prioritize the provision of care, this study was conducted aiming to measure the incidence of in-hospital COVID-19 mortality and to develop a simple and clinically applicable model for its prediction. Methods COVID-19-confirmed patients admitted to the designated isolation areas of Ain-Shams University Hospitals (April 2020–February 2021) were included in this retrospective cohort study (n = 3663). Data were retrieved from patients’ records. Kaplan–Meier survival and Cox proportional hazard regression were used. Binary logistic regression was used for creating mortality prediction models. Results Patients were 53.6% males, 4.6% current smokers, and their median age was 58 (IQR 41–68) years. Admission to intensive care units was 41.1% and mortality was 26.5% (972/3663, 95% CI 25.1–28.0%). Independent mortality predictors—with rapid mortality onset—were age ≥ 75 years, patients’ admission in critical condition, and being symptomatic. Current smoking and presence of comorbidities particularly, obesity, malignancy, and chronic haematological disorders predicted mortality too. Some biomarkers were also recognized. Two prediction models exhibited the best performance: a basic model including age, presence/absence of comorbidities, and the severity level of the condition on admission (Area Under Receiver Operating Characteristic Curve (AUC) = 0.832, 95% CI 0.816–0.847) and another model with added International Normalized Ratio (INR) value (AUC = 0.842, 95% CI 0.812–0.873). Conclusion Patients with the identified mortality risk factors are to be prioritized for preventive and rapid treatment measures. With the provided prediction models, clinicians can calculate mortality probability for their patients. Presenting multiple and very generic models can enable clinicians to choose the one containing the parameters available in their specific clinical setting, and also to test the applicability of such models in a non-COVID-19 respiratory infection.

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Section: Discussionmentioning

confidence: 99%

Developing a mortality risk prediction model using data of 3663 hospitalized COVID-19 patients: a retrospective cohort study in an Egyptian University Hospital

et al. 2023

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Platelet aggregates detected using quantitative phase imaging associate with COVID-19 severity

Klenk,

Erber,

Fresacher

et al. 2023

Commun Med

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Background The clinical spectrum of acute SARS-CoV-2 infection ranges from an asymptomatic to life-threatening disease. Considering the broad spectrum of severity, reliable biomarkers are required for early risk stratification and prediction of clinical outcomes. Despite numerous efforts, no COVID-19-specific biomarker has been established to guide further diagnostic or even therapeutic approaches, most likely due to insufficient validation, methodical complexity, or economic factors. COVID-19-associated coagulopathy is a hallmark of the disease and is mainly attributed to dysregulated immunothrombosis. This process describes an intricate interplay of platelets, innate immune cells, the coagulation cascade, and the vascular endothelium leading to both micro- and macrothrombotic complications. In this context, increased levels of immunothrombotic components, including platelet and platelet-leukocyte aggregates, have been described and linked to COVID-19 severity. Methods Here, we describe a label-free quantitative phase imaging approach, allowing the identification of cell-aggregates and their components at single-cell resolution within 30 min, which prospectively qualifies the method as point-of-care (POC) testing. Results We find a significant association between the severity of COVID-19 and the amount of platelet and platelet-leukocyte aggregates. Additionally, we observe a linkage between severity, aggregate composition, and size distribution of platelets in aggregates. Conclusions This study presents a POC-compatible method for rapid quantitative analysis of blood cell aggregates in patients with COVID-19.

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The COVEG score to predict severity and mortality among hospitalized patients with COVID-19

Cited by 2 publications

References 58 publications

Developing a mortality risk prediction model using data of 3663 hospitalized COVID-19 patients: a retrospective cohort study in an Egyptian University Hospital

Developing a mortality risk prediction model using data of 3663 hospitalized COVID-19 patients: a retrospective cohort study in an Egyptian University Hospital

Platelet aggregates detected using quantitative phase imaging associate with COVID-19 severity

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