Index of cardiac electrophysiological balance (iCEB) in patients with COVID-19

Aims: We aimed to examine whether there is abnormal value of index of cardiac electrophysiological balance (iCEB=QT/QRS) in patients with confirmed coronavirus disease 2019 (COVID-19), which can predict ventricular arrhythmias (VAs), including non-Torsades de Pointes-like ventricular tachycardia/ventricular fibrillation (non- TdPs-like VT/VF) in low iCEB and Torsades de Pointes (TdPs) in high iCEB. We also investigated low voltage ECG among COVID-19 group. Methods and Results: This is a cross-sectional, single center study with a total of 53 newly diagnosed COVID-19 patients (confirmed with polymerase chain reaction (PCR) test) and 63 age and sex-matched control subjects were included in the study. Electrocardiographic marker of iCEB were calculated manually from 12-lead ECG. Low voltage ECG defined as peak-to-peak QRS voltage less than 5mm in all limb leads and less than 10mm in all precordial leads. Patients with COVID-19 more often had low iCEB, defined as iCEB below 3.24 compared to control group (56.6% vs 11.1%), (OR=10.435;95%CI 4.015 - 27.123;p=0.000). There were no significant association between COVID-19 and high iCEB, defined as iCEB above 5.24 (OR=1.041;95%CI 0.485 - 2.235;p=0.917). There were no significant difference of the number of low voltage ECG between COVID-19 and control groups (15.1% vs 6.3%), (OR=2.622;95%CI 0.743 - 9.257, p=0.123). Conclusion: In this study showed that patients with COVID-19 are more likely to have low iCEB, suggesting that patients with COVID-19 may be proarrhytmic (towards non- TdPs-like VT/VF event), due to the alleged myocardial involvement in SARS-CoV-2 infection.

Evaluation of P wave abnormalities in newly diagnosed COVID-19

Aims: The aim of the current study was to evaluate P wave abnormalities, including prolonged P wave duration, prolonged P wave dispersion (PWD) and abnormal P wave axis (PWA) in newly diagnosed COVID-19 patients. Methods and Results: This is a cross-sectional, single center study with a total of 53 newly diagnosed COVID-19 patients (confirmed with polymerase chain reaction (PCR) test) and 63 age and sex-matched control subjects were included in the study. P wave dispersion, maximum P wave duration, and PWA were calculated manually from 12-lead ECG. Patients with COVID-19 more often had prolonged PWD, defined as PWD ≥ 36 ms compared to control group (37,7% vs 15,9%), (OR=3,212;95%CI 1,34 - 7,70;p=0,007). There were no significant association between COVID-19 and prolonged P wave (defined as maximum P wave duration > 106 ms) (OR=1,446;95%CI 0,454 - 4,6;p=0,531) and abnormal PWA (defined as any value of PWA outside 0 to 75°) (OR=5,061;95%CI 0,548 - 46,74;p=0,115). Conclusion: Our study showed that COVID-19 patients are more likely to have prolonged PWD compared to control patients. However, COVID-19 was not significantly associated with prolonged P wave and abnormal PWA. We believe that initial noninvasive evaluation of PWD may serve as a predictor of atrial arrhytmias often found in COVID-19 patients.

Automated Detection of COVID-19 Infected Lesion on Computed Tomography Images Using Faster-RCNNs

The gold standard of a definitive test for the 2019 novel Corona Virus (SARS-CoV-2) is reverse-transcription polymerase chain reaction (RT-PCR). However, its sensitivity ranged between 50% - 90% with high false negatives. Currently, false negatives are real clinical problems, caused by the absence of antibodies formation during sampling (incubation period), impaired antibody formation in immunocompromised patients, apart from sample acquirement technique and transportation issue. Thus, repeated RT-PCR testing is often needed at the early stage of the disease, which may prove to be difficult in a pandemic situation. In some research, the chest computed tomography (CT) image was a rapid and reliable method to diagnose patients with suspected SARS-CoV-2 with higher sensitivity compared to RT-PCR test, particularly the lab test is negative. In this study, 420 CT images with 2,697 features from seven patients infected by SARS-CoV-2 and 200 CT images from healthy individuals are used for analyzing. The convolutional neural networks (CNNs) with Faster-RCNNs architecture is proposed to process the infected lesion detection. As a result, the proposed model shows 90.41% mAP, 99% accuracy, 98% sensitivity, 100% specificity, and 100% precision of classifier performances. All performance produces a 100% score when it tests on external data CT image. It can be seen from the detection result that Ground-glass opacities (GGO)-principal lesions on CT images in the peripheral and posterior sections of the lungs should be strongly suspected of developing SARS-CoV-2 pneumonia. On average, it took less than 0.3 seconds per image to detect the abnormalities from a CT image from data pre-processing to the output of the report. For a frontline clinical doctor, the proposed model may be a promising, supplementary diagnostic process.