Electrocardiographic Changes among Moderate and Severe COVID-19 Patients in a Tertiary Care Teaching Hospital at Shahdol, Madhya Pradesh, India: A Record-based Study

Introduction: Electrocardiographic (ECG) abnormalities in Coronavirus Disease 2019 (COVID-2019) patients are largely unknown. ECG changes in COVID-19 disease may guide to initiate therapeutic anticoagulation, more so in moderate and severe disease. Aims: To identify various ECG changes in moderate and severe COVID-19 patients and to ascertain the association between initial ECG changes and disease outcome. Materials and Methods: This was retrospective record-based study was conducted in the Department of Internal Medicine, Birsa Munda Medical College, Shahdol, Madhya Pradesh, India, on 216 patients with laboratory-confirmed COVID-19 in a tertiary care teaching hospital from March 2021 to June 2021. Demographic and clinical data including ECG were extracted from medical records of the patients and if needed, the patients were followed-up till outcome. COVID-19 disease severity was considered based on oxygen saturation at room air (moderate: 94%-90%;severe: <90%). Data were entered using the Epicollect5 mobile application to minimise errors. Results: A total of 216 patients were included (35 to 54 years), the majority were male. Mortality rate was 46.3%. Total 57.4% of ECG changes were classified as abnormal. Sinus tachycardia was the most common abnormality followed by ischaemic changes. Left axis deviation in ECG was more commonly seen than right axis deviation. Total 53.2% of patients with abnormal ECG findings and 36.9% with normal ECG findings died. Mortality was very high in patients with ischaemic changes. Conclusion: COVID-19 patients with ischaemic changes in ECG were significantly associated with increased mortality. Hence, early detection of these changes in COVID-19 patients is vital and will help primary care physicians to intervene early and help in deciding therapeutic anticoagulation requirements in patients with COVID-19. [ FROM AUTHOR] Copyright of Journal of Clinical & Diagnostic Research is the property of JCDR Research & Publications Private Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Clinical virology and effect of Covid-19 vaccination and monoclonal antibodies against highly infectious SARS- CoV-2 omicron sub variant BF.7 (BA.5.2.1.7): A systematic review.

Over time, the SARS-CoV-2 virus has acquired several genetic mutations, particularly on the receptor-binding domain (RBD) spike glycoprotein. The Omicron variant is highly infectious, with enhanced immune escape activity, and has given rise to various sub-lineages due to mutations. However, there has been a sudden increase in COVID-19 reports of the Omicron subvariant BF.7 (BA.2.75.2), which has the highest number of reported cases, accounting for 76.2% of all cases worldwide. Hence, the present systematic review aimed to understand the viral mutations and factors associated with the increase in the reports of COVID-19 cases and to assess the effectiveness of vaccines and mAbs against the novel Omicron variant BF.7. The R346T mutation on the spike glycoprotein RBD might be associated with increased infection rates, severity, and resistance to vaccines and mAbs. Booster doses of COVID-19 vaccination with bivalent mRNA booster vaccine shots are effective in curtailing infections and decreasing the severity and mortality by enhancing the neutralizing antibodies (Abs) against the emerging Omicron subvariants of SARS-CoV-2, including BF.7 and future VOCs.

Global prevalence and effect of comorbidities and smoking status on severity and mortality of COVID-19 in association with age and gender: a systematic review, meta-analysis and meta-regression.

A COVID-19 patient often presents with multiple comorbidities and is associated with adverse outcomes. A comprehensive assessment of the prevalence of comorbidities in patients with COVID-19 is essential. This study aimed to assess the prevalence of comorbidities, severity and mortality with regard to geographic region, age, gender and smoking status in patients with COVID-19. A systematic review and multistage meta-analyses were reported using PRISMA guidelines. PubMed/MEDLINE, SCOPUS, Google Scholar and EMBASE were searched from January 2020 to October 2022. Cross-sectional studies, cohort studies, case series studies, and case-control studies on comorbidities reporting among the COVID-19 populations that were published in English were included. The pooled prevalence of various medical conditions in COVID-19 patients was calculated based on regional population size weights. Stratified analyses were performed to understand the variations in the medical conditions based on age, gender, and geographic region. A total of 190 studies comprising 105 million COVID-19 patients were included. Statistical analyses were performed using STATA software, version 16 MP (StataCorp, College Station, TX). Meta-analysis of proportion was performed to obtain pooled values of the prevalence of medical comorbidities: hypertension (39%, 95% CI 36-42, n = 170 studies), obesity (27%, 95% CI 25-30%, n = 169 studies), diabetes (27%, 95% CI 25-30%, n = 175), and asthma (8%, 95% CI 7-9%, n = 112). Moreover, the prevalence of hospitalization was 35% (95% CI 29-41%, n = 61), intensive care admissions 17% (95% CI 14-21, n = 106), and mortality 18% (95% CI 16-21%, n = 145). The prevalence of hypertension was highest in Europe at 44% (95% CI 39-47%, n = 68), obesity and diabetes at 30% (95% CI, 26-34, n = 79) and 27% (95%CI, 24-30, n = 80) in North America, and asthma in Europe at 9% (95% CI 8-11, n = 41). Obesity was high among the ≥ 50 years (30%, n = 112) age group, diabetes among Men (26%, n = 124) and observational studies reported higher mortality than case-control studies (19% vs. 14%). Random effects meta-regression found a significant association between age and diabetes (p < 0.001), hypertension (p < 0.001), asthma (p < 0.05), ICU admission (p < 0.05) and mortality (p < 0.001). Overall, a higher global prevalence of hypertension (39%) and a lower prevalence of asthma (8%), and 18% of mortality were found in patients with COVID-19. Hence, geographical regions with respective chronic medical comorbidities should accelerate regular booster dose vaccination, preferably to those patients with chronic comorbidities, to prevent and lower the severity and mortality of COVID-19 disease with novel SARS-CoV-2 variants of concern (VOC).

Automated COVID-19 detection in chest X-ray images using fine-tuned deep learning architectures

Abstract The COVID-19 pandemic has a significant impact on human health globally. The illness is due to the presence of a virus manifesting itself in a widespread disease resulting in a high mortality rate in the whole world. According to the study, infected patients have distinct radiographic visual characteristics as well as dry cough, breathlessness, fever, and other symptoms. Although, the reverse transcription polymerase-chain reaction (RT-PCR) test has been used for COVID-19 testing its reliability is very low. Therefore, computed tomography and X-ray images have been widely used. Artificial intelligence coupled with X-ray technologies has recently shown to be more effective in the diagnosis of this disease. With this motivation, a comparative analysis of fine-tuned deep learning architectures has been made to speed up the detection and classification of COVID-19 patients from other pneumonia groups. The models used for this analysis are MobileNetV2, ResNet50, InceptionV3, NASNetMobile, VGG16, Xception, InceptionResNetV2 DenseNet121, which have been fine-tuned using a new set of layers replaced with the head of the network. This research work has carried out an analysis on two datasets. Dataset-1 includes the images of three classes: Normal, COVID, and Pneumonia. Dataset-2, in contrast, contains the same classes with more focus on two prominent pneumonia categories: bacterial pneumonia and viral pneumonia. The research was conducted on 959 X-ray images (250 of Bacterial Pneumonia, 250 of Viral Pneumonia, 209 of COVID, and 250 of Normal cases). Using the confusion matrix, the required results of different models have been computed. For the first dataset, DenseNet121 has obtained a 97% accuracy, while for the second dataset, MobileNetV2 has performed best with an accuracy of 81%.