Complement C5a and Clinical Markers as Predictors of COVID-19 Disease Severity and Mortality in a Multi-Ethnic
Farhan S Cyprian1,2, Muhammad Suleman3, Ibrahim Abdelhafez1
1Biomedical and Pharmaceutical Research Unit, QU Health, Qatar University, Doha, Qatar.
Insights
A new CAL model using C5a, albumin, and lymphocyte count can predict severe COVID-19 and mortality risk early. This aids in identifying high-risk patients for targeted treatment and resource allocation.
Area of Science:
- Biomedical research
- Infectious diseases
- Clinical diagnostics
Background:
- Coronavirus disease-2019 (COVID-19) pandemic necessitates early identification of severe cases.
- Predicting disease severity and mortality is crucial for effective patient management and resource allocation.
- Existing biomarkers may not sufficiently capture the complexity of COVID-19 progression.
Purpose of the Study:
- To identify patients at high risk of severe COVID-19 and mortality upon initial diagnosis.
- To evaluate blood parameters, including inflammation and coagulation markers, as predictive indicators.
- To develop a prognostic model for early risk stratification of COVID-19 patients.
Main Methods:
- A cohort of 89 multi-ethnic COVID-19 patients in Doha, Qatar, was analyzed.
- Patients were categorized into severe, mild, and asymptomatic groups based on clinical severity.
- Routine blood tests, including complete blood count (CBC), inflammatory markers (C5a, IL-6, ferritin, CRP), and organ function tests, were performed.
Main Results:
- Complement component C5a was identified as a significant predictive marker for disease severity and mortality.
- High C5a levels, hypoalbuminemia, lymphopenia, elevated procalcitonin, neutrophilic leukocytosis, and markers of acute kidney and liver injury were associated with increased mortality risk.
- A prognostic model, the CAL model (C5a, Albumin, Lymphocyte count), demonstrated significant accuracy in predicting COVID-19 severity.
Conclusions:
- The CAL model effectively stratifies COVID-19 patients, enabling early identification of those likely to develop severe symptoms.
- This prognostic tool can guide targeted therapeutic interventions and optimize resource allocation.
- Biomarkers like C5a, albumin, and lymphocyte count are valuable for predicting COVID-19 outcomes.
Abstract:
Coronavirus disease-2019 (COVID-19) was declared as a pandemic by WHO in March 2020. SARS-CoV-2 causes a wide range of illness from asymptomatic to life-threatening. There is an essential need to identify biomarkers to predict disease severity and mortality during the earlier stages of the disease, aiding treatment and allocation of resources to improve survival. The aim of this study was to identify at the time of SARS-COV-2 infection patients at high risk of developing severe disease associated with low survival using blood parameters, including inflammation and coagulation mediators, vital signs, and pre-existing comorbidities. This cohort included 89 multi-ethnic COVID-19 patients recruited between July 14th and October 20th 2020 in Doha, Qatar. According to clinical severity, patients were grouped into severe (n=33), mild (n=33) and asymptomatic (n=23). Common routine tests such as complete blood count (CBC), glucose, electrolytes, liver and kidney function parameters and markers of inflammation, thrombosis and endothelial dysfunction including complement component split product C5a, Interleukin-6, ferritin and C-reactive protein were measured at the time COVID-19 infection was confirmed. Correlation tests suggest that C5a is a predictive marker of disease severity and mortality, in addition to 40 biological and physiological parameters that were found statistically significant between survivors and non-survivors. Survival analysis showed that high C5a levels, hypoalbuminemia, lymphopenia, elevated procalcitonin, neutrophilic leukocytosis, acute anemia along with increased acute kidney and hepatocellular injury markers were associated with a higher risk of death in COVID-19 patients. Altogether, we created a prognostic classification model, the CAL model (C5a, Albumin, and Lymphocyte count) to predict severity with significant accuracy. Stratification of patients using the CAL model could help in the identification of patients likely to develop severe symptoms in advance so that treatments can be targeted accordingly.
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