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Cardiovascular Complications and its Impact on outcomes in COVID-19: An Original Research
Prashant Kumar1, Kaousthubh Tiwari2, Siva Kumar Pendyala3
1Department of Cardiology, Rajendra Institute of Medical Science, Ranchi, Jharkhand, India.
Insights
This study developed a new risk score to predict cardiovascular events in COVID-19 patients. Identifying these events can help improve patient prognosis and outcomes.
Area of Science:
- Cardiology
- Infectious Diseases
- Public Health
Background:
- COVID-19 infection presents significant mortality risks, with cardiovascular (CV) events contributing substantially.
- Pulmonary symptoms are common, but CV complications also require attention for patient outcomes.
Purpose of the Study:
- To design and validate a novel risk score for predicting cardiovascular events in COVID-19 patients.
- To evaluate the impact of cardiovascular complications on the prognosis of COVID-19.
Main Methods:
- A retrospective, multicenter, observational study involving 1000 laboratory-confirmed COVID-19 patients.
- Logistic regression analysis was used to identify independent risk factors for CV events.
Main Results:
- Ten independent risk factors for CV events were identified: male gender, older age, chronic heart disease, cough, low lymphocyte count, elevated blood urea nitrogen, reduced estimated glomerular filtration rate, prolonged activated partial thromboplastin time, elevated D-dimer, and elevated procalcitonin.
- Cardiovascular events were significantly associated with inferior prognosis (P < 0.001).
Conclusions:
- A new risk scoring system was developed as a potential predictive tool for cardiovascular complications in COVID-19 patients.
Introduction:
The viral infection COVID-19 is highly infectious and has claimed many lives till date and is still continuing to consume lives. In the COVID-19, along with pulmonary symptoms, cardiovascular (CV) events were also recorded that have known to significantly contribute to the mortality. In our study, we designed and validated a new risk score that can predict CV events, and also evaluated the effect of these complications on the prognosis in COVID-19 patients.
Materials And Methods:
A retrospective, multicenter, observational study was done among 1000 laboratory-confirmed COVID-19 patients between June 2020 and December 2020. All the data of the clinical and laboratory parameters were collected. Patients were randomly divided into two groups for testing and validating the hypothesis. The identification of the independent risk factors was done by the logistic regression analysis method.
Results:
Of all the types of the clinical and laboratory parameters, ten "independent risk factors" were identified associated with CV events in Group A: male gender, older age, chronic heart disease, cough, lymphocyte count <1.1 × 109/L at admission, blood urea nitrogen >7 mmol/L at admission, estimated glomerular filtration rate <90 ml/min/1.73 m2 at admission, activated partial thromboplastin time >37 S, D-dimer, and procalcitonin >0.5 mg/L. In our study, we found that CV events were significantly related with inferior prognosis (P < 0.001).
Conclusions:
A new risk scoring system was designed in our study, which may be used as a predictive tool for CV complications among the patients with COVID-19 infection.
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