ABSTRACT
SARS-CoV-2 was discovered in Wuhan, China and quickly spread throughout the world. This deadly virus moved from person to person, resulting in severe pneumonia, fever, chills and hypoxia. Patients are still experiencing problems after recovering from COVID-19. This review covers COVID-19 and associated issues following recovery from COVID-19, as well as multiorgan damage risk factors and treatment techniques. Several unusual illnesses, including mucormycosis, white fungus infection, happy hypoxia and other systemic abnormalities, have been reported in recovered individuals. In children, multisystem inflammatory syndrome with COVID-19 (MIS-C) is identified. The reasons for this might include uncontrollable steroid usage, reduced immunity, uncontrollable diabetes mellitus and inadequate care following COVID-19 recovery.
COVID-19 infection has reported in the development several other infections and co-morbidity in patients. The present review discusses risk and management strategies in patients suffeting from co-infections caused by COVID-19 infection.
ABSTRACT
Background: and ObjectivIn the current COVID-19 outbreak, efficient testing of COVID-19 individuals has proven vital to limiting and arresting the disease's accelerated spread globally. It has been observed that the severity and mortality ratio of COVID-19 affected patients is at greater risk because of chronic pulmonary diseases. This study looks at radiographic examinations exploiting chest X-ray images (CXI), which have become one of the utmost feasible assessment approaches for pulmonary disorders, including COVID-19. Deep Learning(DL) remains an excellent image classification method and framework; research has been conducted to predict pulmonary diseases with COVID-19 instances by developing DL classifiers with nine class CXI. However, a few claim to have strong prediction results; because of noisy and small data, their recommended DL strategies may suffer from significant deviation and generality failures. Methods: Therefore, a unique CNN model(PulDi-COVID) for detecting nine diseases (atelectasis, bacterial-pneumonia, cardiomegaly, covid19, effusion, infiltration, no-finding, pneumothorax, viral-Pneumonia) using CXI has been proposed using the SSE algorithm. Several tranfer-learning models: VGG16, ResNet50, VGG19, DenseNet201, MobileNetV2, NASNetMobile, ResNet152V2, DenseNet169 are trained on CXI of chronic lung diseases and COVID-19 instances. Given that the proposed thirteen SSE ensemble models solved DL's constraints by making predictions with different classifiers rather than a single, we present PulDi-COVID, an ensemble DL model that combines DL with ensemble learning. The PulDi-COVID framework is created by incorporating various snapshots of DL models, which have spearheaded chronic lung diseases with COVID-19 cases identification process with a deep neural network produced CXI by applying a suggested SSE method. That is familiar with the idea of various DL perceptions on different classes. Results: PulDi-COVID findings were compared to thirteen existing studies for nine-class classification using COVID-19. Test results reveal that PulDi-COVID offers impressive outcomes for chronic diseases with COVID-19 identification with a 99.70% accuracy, 98.68% precision, 98.67% recall, 98.67% F1 score, lowest 12 CXIs zero-one loss, 99.24% AUC-ROC score, and lowest 1.33% error rate. Overall test results are superior to the existing Convolutional Neural Network(CNN). To the best of our knowledge, the observed results for nine-class classification are significantly superior to the state-of-the-art approaches employed for COVID-19 detection. Furthermore, the CXI that we used to assess our algorithm is one of the larger datasets for COVID detection with pulmonary diseases. Conclusion: The empirical findings of our suggested approach PulDi-COVID show that it outperforms previously developed methods. The suggested SSE method with PulDi-COVID can effectively fulfill the COVID-19 speedy detection needs with different lung diseases for physicians to minimize patient severity and mortality.
ABSTRACT
Chronic respiratory diseases have been on the rise, especially due to COVID-19, extreme air pollution, and other external circumstances. Millions of people around the world suffer from progressive lung diseases and require supplemental oxygen therapy to maintain blood oxygen (SpO2) levels above 90% to prevent hypoxic episodes that can lead to further organ damage. Today, these chronic episodes are more prevalent in aging populations suffering from Chronic Obstructive Pulmonary Disorder (COPD). Existing SpO2 measurement equipment, designed to assist with treating COPD at home, are suboptimal as they cannot measure SpO2 levels continuously, meaning supplemental oxygen devices are unable to adjust oxygen flow rates to the patient's needs. These discrepancies can result in hypoxic episodes of blood oxygen levels below 90%. Following this need, our team demonstrates preliminary results of the novel placement of a SpO2 sensor in the nasal septum to allow for comfortable and sustained SpO2 measurement. This will improve the experience of home-respiratory care with continuously obtained data from a novel location.
ABSTRACT
Coronavirus disease 2019 (COVID-19) has resulted in considerable morbidity and mortality worldwide since December 2019. In order to explore the effects of comorbid chronic diseases on clinical outcomes of COVID-19, a search was conducted in PubMed, Ovid MEDLINE, EMBASE, CDC, and NIH databases to April 25, 2020. A total of 24 peer-reviewed articles, including 10948 COVID-19 cases were selected. We found diabetes was present in 10.0%, coronary artery disease/cardiovascular disease (CAD/CVD) was in 8.0%, and hypertension was in 20.0%, which were much higher than that of chronic pulmonary disease (3.0%). Specifically, preexisting chronic conditions are strongly correlated with disease severity [Odds ratio (OR) 3.50, 95% CI 1.78 to 6.90], and being admitted to intensive care unit (ICU) (OR 3.36, 95% CI 1.67 to 6.76); in addition, compared to COVID-19 patients with no preexisting chronic diseases, COVID-19 patients who present with either diabetes, hypertension, CAD/CVD, or chronic pulmonary disease have a higher risk of developing severe disease, with an OR of 2.61 (95% CI 1.93 to 3.52), 2.84 (95% CI 2.22 to 3.63), 4.18 (95% CI 2.87 to 6.09) and 3.83 (95% CI 2.15 to 6.80), respectively. Surprisingly, we found no correlation between chronic conditions and increased risk of mortality (OR 2.09, 95% CI 0.26 to16.67). Taken together, cardio-metabolic diseases, such as diabetes, hypertension and CAD/CVD were more common than chronic pulmonary disease in COVID-19 patients, however, each comorbid disease was correlated with increased disease severity. After active treatment, increased risk of mortality in patients with preexisting chronic diseases may reduce.