Imaging data in COVID-19 patients: focused on echocardiographic findings.
Roya Sattarzadeh BadkoubehMeysam KhoshaviVahideh Laleh FarAli MehrakizadehMasoud EslamiFaeze SalahshourAkram SardariSaeed SafariFarnoosh LartiSteven NissenPublished in: The international journal of cardiovascular imaging (2021)
To assess imaging data in COVID-19 patients and its association with clinical course and survival and 86 consecutive patients (52 males, 34 females, mean age = 58.8 year) with documented COVID-19 infection were included. Seventy-eight patients (91%) were in severe stage of the disease. All patients underwent transthoracic echocardiography. Mean LVEF was 48.1% and mean estimated systolic pulmonary artery pressure (sPAP) was 27.9 mmHg. LV diastolic dysfunction was mildly abnormal in 49 patients (57.6%) and moderately abnormal in 7 cases (8.2%). Pericardial effusion was present in 5/86 (minimal in size in 3 cases and mild- moderate in 2). In 32/86 cases (37.2%), the severity of infection progressed from "severe" to "critical". Eleven patients (12.8%) died. sPAP and computed tomography score were associated with disease progression (P value = 0.002, 0.002 respectively). Tricuspid annular plane systolic excursion (TAPSE) was significantly higher in patients with no disease progression compared with those who deteriorated (P value = 0.005). Pericardial effusion (minimal, mild or moderate) was detected more often in progressive disease (P = 0.03). sPAP was significantly lower among survivors (P value = 0.007). Echocardiographic findings (including systolic PAP, TAPSE and pericardial effusion), total CT score may have prognostic and therapeutic implication in COVID-19 patients.
Keyphrases
- ejection fraction
- end stage renal disease
- computed tomography
- left ventricular
- newly diagnosed
- chronic kidney disease
- pulmonary artery
- heart failure
- pulmonary hypertension
- blood pressure
- magnetic resonance imaging
- aortic stenosis
- coronary artery
- patient reported outcomes
- mitral valve
- electronic health record
- machine learning
- multiple sclerosis
- young adults
- contrast enhanced
- fluorescence imaging
- free survival