ABSTRACT
Objectives: At present, clinicians face plenty of patients complaining of post-COVID-19 chest pain and dyspnea. However, it remains to be seen if these symptoms indicate pathology of the cardiovascular system. We aimed to evaluate heart functions in outpatients with post-COVID-19 chest pain and dyspnea, using 2D speckle-tracking echocardiography (2D-STE). Method(s): This cross-sectional study recruited consecutive patients who presented to cardiology outpatient clinics between June 15, 2021, and July 15, 2021. A total of 78 patients had recovered from COVID-19 1-2 months before admission were included in the study. ECG and echocardiography, including 2D-STE images, were obtained for all patients. Findings were compared with sex- and an age-matched control group of 67 healthy adults. Result(s): The median age was 38 (IQR, 34-45) years, and 64.1% were female. There were no significant differences between the patients and control group regarding laboratory, ECG, and echocardiography findings. Moreover, the left ventricle global longitudinal strain measurements in both the patient and control groups were within the normal ranges and did not show a significant difference (-20.5 [-21.8- -17.9] vs. -19.8 [-21.4-18.9], p=0.894). Conclusion(s): Post-COVID-19 chest pain and dyspnea are unlikely signs of cardiovascular involvement in outpatient young adults who have not been hospitalized with COVID-19.©Copyright 2022 by The Cardiovascular Thoracic Anaesthesia and Intensive Care.
ABSTRACT
Aim: Coronavirus disease 2019 (COVID-19) has caused thrombotic disease. In this study, we aimed to determine the demographic and clinical characteristics of acute coronary syndrome (ACS) patients infected with COVID-19 and to investigate whether they differ from patients with ACS without COVID-19 in terms of these characteristics. Material(s) and Method(s): The study was designed as a single-center retrospective study. Thirty-three COVID-19 infected ACS patients (Group 1) and 100 ACS patients without COVID-19 infection (Group 2) were included in the study. Result(s): The groups were compared in terms of coronary angiographic data. Twenty-eight (84.8%) patients in Group 1 and 74 (74%) patients in Group 2 were presented as non-ST elevation myocardial infarctus. Patients were compared in terms of baseline Thrombolysis in Myocardial Infarctus (TIMI) flow, thrombus stage, myocardial blush (end), using of thrombus aspiration catheter, stent thrombosis, and TIMI flow after percutaneous coronary intervention, and it was observed that there was no statistical difference between the groups (p> 0.05). Discussion(s): COVID-19 infection can cause plaque rupture, myocardial damage, coronary spasm and cytokine storm by triggering the coagulation and inflammation process. The fact is that we did not encounter an increased thrombus load in this study.Copyright © 2022, Derman Medical Publishing. All rights reserved.
ABSTRACT
Background. Multisystem Inflammatory Syndrome (MIS-C), a new entity in children which developed 2-4 weeks after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, is a severe condition. It can affect the multisystem, while the most severe manifestation is cardiac involvement. Left ventricular dysfunction, cardiogenic shock, coronary artery dilatation/aneurysm, valvulitis, pericardial effusion, arrhythmia, and conduction abnormalities were reported in approximately 80% of children with cardiovascular system involvement. It is still unclear the duration of the cardiac symptoms, and even they are permanent or persistent. Few studies evaluated persistent cardiac abnormalities by cardiac magnetic resonance imaging (MRI). Therefore, we aimed to assess persisting cardiac abnormalities with MIS-C by cardiac MRI and compare them with echocardiograms. Methods. A retrospective study was conducted at a tertiary-level University Hospital between June 2020-July 2021. Thirty-four children diagnosed with MIS-C according to the criteria defined by the Centers for Disease Control and Prevention were retrospectively evaluated. Results. The study included 17 males and 17 females with a mean age of 9.31 +/-4.72 years. Initial echocardiographic evaluation showed cardiac abnormality in 18 (52.9%) patients;4 (11.8%) pericardial effusion, 4 (11.8%) left ventricular ejection fraction (LVEF) < 55%, 5 (14.7%) LV fractional shortening < 30%, 5 (14.7%) coronary artery dilatation. Echocardiography showed normal LV systolic function in all patients at follow-up;coronary dilatation persisted in 2 of 5 (40 %) patients at the 6th-month visit. Cardiac MRI was performed in 31 (91.2%) patients. We didn't detect abnormal T1 levels, whereas 9 (29%) had isolated elevated T2 values. 19 (61.3%) of 31 patients had at least one of the followingfindings: pericardial effusion, right ventricular dysfunction, LVEF abnormality. Conclusion. Cardiac involvement persisted at a higher rate which was shown by cardiac MRI in the late period, particularly pericardial effusion. Cardiac MRI may be suggested for all MIS-C patients at a later phase. Prospective studies with larger sample sizes are needed to determine long-term cardiac effects.
ABSTRACT
Background/Introduction: In recent years there has been a growing interest in artificial intelligence (AI) applications in the echocardiography field. This is in order to simplify, reduce time and amplify the use of advanced analyses in the echo lab. Purpose: to compare results of the fully automated analysis and manual tracing analysis using a new intuitive software. Methods: 28 consecutive previously healthy patients less than 18 years old who were screened at our Center for cardiac evaluation within 6 months after an asymptomatic or paucisymptomatic COVID19 infection were enrolled. All they were in sinus rhythm. Standard transthoracic echocardiography (TTE) was performed for each patient using Canon Aplio i900, software 2D Wall Motion Tracking. Optimized apical 4-, 3- and 2- chamber views, mitral valve inflow pattern and LVOT Doppler interrogation were collected. Off-line data analysis of each examination was performed by both fully automated analysis (AI) and pediatric cardiologists with experience in echocardiography i.e. by manual tracing, evaluation and adjustment of the track by the operator (Echocardiographers). Operators were blinded to the AI analysis. To measure intraobserver variability, evaluations of 16 patients datasets were performed twice by both operators and AI. Results: Patients' demographic data were: age 9,8+/-4,7 years;males 22 (78%);height 134,3+/- 34,9 cm;weight 41,8+/-28,7 kg;BSA 1,2+/-0,4 mq, HR 85+/-15/min. The time taken for off-line analysis by AI and echocardiographers was 4-5 and 13-20 minutes, respectively. Reproducibility of echocardiographers' analysis was found to be excellent for left ventricle assessment (IC from 0,88 to 0,98);moderate for LVOT mean gradient (IC 0,73), RV end diastolic area (IC 0,69) and right atrial strain (IC 0,59);poor for deceleration time (IC 0,5), left ventricle strain (IC 0,49), RV FAC and strain (IC from 0,27 to 0,45). Conversely, reproducibility of the AI analysis was found to be excellent for any parameter (ICC from 0,87 to 0,99) (Table 1). About the mitralic valve inflow pattern assessment, despite the excellent reproducibility of AI analysis, the margin of error was found to be high. Particularly, a systematic error was observed with a tendency of the AI to overestimate deceleration time (DT-AI 176,6 ± 63,8 vs DTEcocardiographers 150,4 ± 24,3). Conclusion(s): Fully automated analysis is technically simple, less time consuming and highly reproducible. AI analysis of the mitralic inflow pattern should be optimized, having found a systematic error in the calculation of deceleration time. Reproducibility is the strong point of AI. This reduces the variability of manual measurements between different sonographers and at different times.
ABSTRACT
Background/Introduction: Ejection fraction (EF) is a parameter widely used in Echolab to evaluate left ventricular function. Recently, in parallel with the growing interest in artificial intelligence (AI), attemps have been made to create automated systems for EF assessment, in order to reduce time and improve the accuracy of the analysis. Purpose: to compare results of different methods of EF assessment: visual estimation (visual EF), manual and fully automated analysis. Methods: 28 consecutive pediatric patients were enrolled. This cohort of previously healthy patients was screened at our Center for cardiac evaluation within 6 months after an asymptomatic or paucisymptomatic COVID19 infection. All they were in sinus rhythm. Optimized apical 4- and 2- chamber views were collected for each patient using Canon Aplio i900. Off-line EF assessment was first evaluated visually by pediatric cardiologists with experience in echocardiography, then performed by both fully automated analysis (AI) using two different methods (Automatic Simpson -AI Simpson- and Wall Motion Tracking -AI WMT-) and pediatric cardiologists through manual tracing of endocardial border (Manual Simpson and Manual WMT respectively). Operators were blinded to the AI analysis. To measure intraobserver variability, evaluations of 16 patients' datasets were performed twice by both operators and AI. Results: Patients' demographic data were: age 9,8+/-4,7 years;males 22 (78%);height 134,3+/- 34,9 cm;weight 41,8+/-28,7 kg;BSA 1,2+/-0,4 mq, HR 85+/-15/min. The time taken for off-line analysis was 0.3-0.7 minutes, 1-1.5 minutes, 1-3 minutes and 3-4 minutes, respectively for AI WMT, AI Simpson, Manual WMT and Manual Simpson. As expected, visual EF showed high intraobserver variability and a poor reproducibility (ICC 43%). AI analysis revealed a good to excellent reproducibility (ICC from 80% to 99%, depending on the method used). WMT methods had the best reproducibility both for manual tracing of endocardial border and fully automated analysis (Table 1). The comparison between different methods (Table 2) showed a good agreement between AI Simpson and AI WMT (mean bias 2,9, from -3,2 to 9,0, ICC 86%). A moderate correlation was found between different methods of AI analysis while only poor correlation was found between manual Simpson and manual WMT (Table 2). Conclusion(s): Automatic Simpson and Wall Motion Tracking are two different fully automated methods which can be used for left ventricular function assessment. AI reproducibility is high for both methods, higher for WMT. WMT method is also less time consuming and improves reproducibility of manual tracing of endocardial borderd analysis.