In:
Clinical Cardiology, Wiley, Vol. 45, No. 9 ( 2022-09), p. 943-951
Abstract:
Cardiovascular risk factors and comorbidities are highly prevalent among COVID‐19 patients and are associated with worse outcomes. Hypothesis We therefore investigated if established cardiovascular risk assessment models could efficiently predict adverse outcomes in COVID‐19. Furthermore, we aimed to generate novel risk scores including various cardiovascular parameters for prediction of short‐ and midterm outcomes in COVID‐19. Methods We included 441 consecutive patients diagnosed with SARS‐CoV‐2 infection. Patients were followed‐up for 30 days after the hospital admission for all‐cause mortality (ACM), venous/arterial thromboembolism, and mechanical ventilation. We further followed up the patients for post‐COVID‐19 syndrome for 6 months and occurrence of myocarditis, heart failure, acute coronary syndrome (ACS), and rhythm events in a 12‐month follow‐up. Discrimination performance of DAPT, GRACE 2.0, PARIS‐CTE, PREDICT‐STABLE, CHA2‐DS2‐VASc, HAS‐BLED, PARIS‐MB, PRECISE‐DAPT scores for selected endpoints was evaluated by ROC‐analysis. Results Out of established risk assessment models, GRACE 2.0 score performed best in predicting combined endpoint and ACM. Risk assessment models including age, cardiovascular risk factors, echocardiographic parameters, and biomarkers, were generated and could successfully predict the combined endpoint, ACM, venous/arterial thromboembolism, need for mechanical ventilation, myocarditis, ACS, heart failure, and rhythm events. Prediction of post‐COVID‐19 syndrome was poor. Conclusion Risk assessment models including age, laboratory parameters, cardiovascular risk factors, and echocardiographic parameters showed good discrimination performance for adverse short‐ and midterm outcomes in COVID‐19 and outweighed discrimination performance of established cardiovascular risk assessment models.
Type of Medium:
Online Resource
ISSN:
0160-9289
,
1932-8737
Language:
English
Publisher:
Wiley
Publication Date:
2022
detail.hit.zdb_id:
2048223-1
