ABSTRACT
AIMS: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is associated with endothelial dysfunction. We aimed to determine the effects of prior coronavirus disease 2019 (COVID-19) on the coronary microvasculature accounting for time from COVID-19, disease severity, SARS-CoV-2 variants, and in subgroups of patients with diabetes and those with no known coronary artery disease. METHODS AND RESULTS: Cases consisted of patients with previous COVID-19 who had clinically indicated positron emission tomography (PET) imaging and were matched 1:3 on clinical and cardiovascular risk factors to controls having no prior infection. Myocardial flow reserve (MFR) was calculated as the ratio of stress to rest myocardial blood flow (MBF) in mL/min/g of the left ventricle. Comparisons between cases and controls were made for the odds and prevalence of impaired MFR (MFR < 2). We included 271 cases matched to 815 controls (mean ± SD age 65 ± 12 years, 52% men). The median (inter-quartile range) number of days between COVID-19 infection and PET imaging was 174 (58-338) days. Patients with prior COVID-19 had a statistically significant higher odds of MFR <2 (adjusted odds ratio 3.1, 95% confidence interval 2.8-4.25 P < 0.001). Results were similar in clinically meaningful subgroups. The proportion of cases with MFR <2 peaked 6-9 months from imaging with a statistically non-significant downtrend afterwards and was comparable across SARS-CoV-2 variants but increased with increasing severity of infection. CONCLUSION: The prevalence of impaired MFR is similar by duration of time from infection up to 1 year and SARS-CoV-2 variants, but significantly differs by severity of infection.
ABSTRACT
Recent reports linked acute COVID-19 infection in hospitalized patients to cardiac abnormalities. Studies have not evaluated presence of abnormal cardiac structure and function before scanning in setting of COVD-19 infection. We sought to examine cardiac abnormalities in consecutive group of patients with acute COVID-19 infection according to the presence or absence of cardiac disease based on review of health records and cardiovascular imaging studies. We looked at independent contribution of imaging findings to clinical outcomes. After excluding patients with previous left ventricular (LV) systolic dysfunction (global and/or segmental), 724 patients were included. Machine learning identified predictors of in-hospital mortality and in-hospital mortality + ECMO. In patients without previous cardiovascular disease, LV EF < 50% occurred in 3.4%, abnormal LV global longitudinal strain (< 16%) in 24%, and diastolic dysfunction in 20%. Right ventricular systolic dysfunction (RV free wall strain < 20%) was noted in 18%. Moderate and large pericardial effusion were uncommon with an incidence of 0.4% for each category. Forty patients received ECMO support, and 79 died (10.9%). A stepwise increase in AUC was observed with addition of vital signs and laboratory measurements to baseline clinical characteristics, and a further significant increase (AUC 0.91) was observed when echocardiographic measurements were added. The performance of an optimized prediction model was similar to the model including baseline characteristics + vital signs and laboratory results + echocardiographic measurements.