Study of Clinical Profile and Outcome among COVID-19 Patients during Hospitalization in a Tertiary Care Hospital in Central India

Background: The COVID-19 outbreak in 2019 has presented in the form of pneumonia of unknown etiology in Wuhan. The complete clinical profile including the prevalence of different clinical symptoms of COVID-19 infection among Indian patients who develop a severe disease is largely unknown. This study is aimed to provide a detailed clinical characterization of the cohort of patients who visited our institute with signs and symptoms of COVID-19. Material(s) and Method(s): This was for inpatient hospital (inpatient) based prospective cohort study involving 520 COVID-19 patients admitted to the hospital. The adverse outcome included death and mechanical ventilation. Result(s): Total 520 participants enrolled in the study, (6.9%) participants died, (8.3%) participants required ICU and (5.5%) participants required mechanical ventilation. only signs and symptoms suggestive of severe respiratory system involvement or widespread infection were associated with adverse outcomes, T presence of dyspnoea, cyanosis and hypoxia. The most common chronic disease among patients with adverse outcomes were diabetes, hypertension and pre-existing respiratory disease, personal habit both smoking, and alcoholism was also associated with adverse clinical outcome. Conclusion(s): The adverse clinical outcome among COVID-19 patients is determined by several factors including advanced age, multi-morbidities, and the presence of severe respiratory symptoms.Copyright © 2023, Dr Yashwant Research Labs Pvt Ltd. All rights reserved.

A Hybrid Model of Deep Learning Features and Clinical Features for Severe Cases Predication of COVID-19

COVID-19 has spread around the world since 2019. Approximately 6.5% of COVID-19 a risk of developing severe disease with high mortality rate. To reduce the mortality rate and provide appropriate treatment, this research established an integrated models with to predict the clinical outcome of COVID-19 patients with clinical, deep learning and radiomics features. To obtain the optimal feature combination for prediction, 9 clinical features combination was selected from all available clinical factors after using LASSO, 18 deep learning features from U-Net architecture, and 9 radiomics features from segmentation result. A total of 213 COVID-19 patients and 335 non-COVID-19 patients from 5 hospitals were enrolled and used as training and test sample in this research. The proposed model obtained an accuracy, precision, recall, specificity, F1-score and ROC curve of 0.971, 0.943, 0.937, 0.974, 0.941 and 0.979, respectively, which exceeds the related work using only clinical, deep learning or radiomics factors. © 2023 SPIE.

Radiological Features on HRCT Associated with Poor Outcomes among COVID-19 Patients

Background: Ever since the beginning of the COVID-19 pandemic, physicians started investigating the clinical features and lab markers that can assist in predicting the outcome among hospitalized COVID-19 patients. Aim(s): This study aimed to investigate the association between initial chest CT scan findings and adverse outcomes of COVID-19. Material(s) and Method(s): This was a single centre;hospital (inpatient) based prospective cohort study involving 497 COVID-19 patients admitted to the hospital. The adverse outcome included death and mechanical ventilation. We collected data about 14 identifiable parameters available for the HRCT scan. Result(s): Among 14 studied parameters, only 8 features differed significantly among the patients who had favourable and unfavourable outcomes. These features included number of lobes of lungs involved (3 versus 5, p = 0.008), CT Severity score (16 versus 20, p = 0.004), air bronchogram (p=0.003), crazy paving (p=0.029), consolidation (p=0.021), and pleural effusion (p=0.026). We observed that high CT scores coupled with the diffuse distribution of lung lesions were responsible for poor prognosis in most patients. Conclusion(s): Several features of HRCT when combined can accurately predict adverse outcomes among participants and help in triaging the patient for admission in ICU.Copyright © 2023, Dr Yashwant Research Labs Pvt Ltd. All rights reserved.

Explainable AI for COVID-19 prognosis from early Chest X-ray and clinical data in the context of the COVID-CXR international hackathon

Although many AI-based scientific works regarding chest X-ray (CXR) interpretation focused on COVID-19 diagnosis, fewer papers focused on other relevant tasks, like severity estimation, deterioration, and prognosis. The same holds for explainable decisions to estimate COVID-19 prognosis as well. The international hackathon launched during Dubai Expo 2020, aimed at designing machine learning solutions to help physicians formulate COVID-19 patients' prognosis, was the occasion to develop a machine learning model capable of predicting such prognoses and justifying them through interpretable explanations. The large hackathon dataset comprised subjects characterized by their CXR and numerous clinical features collected during triage. To calculate the prognostic value, our model considered both patients' CXRs and clinical features. After automatic pre-processing to improve their quality, CXRs were processed by a Deep Learning model to estimate the lung compromise degree, which has been considered as an additional clinical feature. Original clinical parameters suffered from missing values that were adequately handled. We trained and evaluated multiple models to find the best one and fine-tune it before the inference process. Finally, we produced novel explanations, both visual and numerical, to justify the model predictions. Ultimately, our model processes a CXR and several clinical data to estimate a patient's prognosis related to the COVID-19 disease. It proved to be accurate and was ranked second in the final rankings with 75%, 73.9%, and 74.4% in sensitivity, specificity, and balanced accuracy, respectively. In terms of model explainability, it was ranked first since it was agreed to be the most interpretable by health professionals. © 2023 SPIE.

Clinical features, diagnosis, and management of COVID-19 vaccine-associated Vogt-Koyanagi-Harada disease.

Vogt-Koyanagi-Harada (VKH) disease is a rare and serious ocular adverse reaction following COVID-19 vaccination. This study aimed to evaluate the clinical features, diagnosis and management of COVID-19 vaccine-associated VKH disease. Case reports of VKH disease after COVID-19 vaccination were collected up to February 11, 2023 for retrospective analysis. Twenty-one patients (9 males and 12 females) were included, with a median age of 45 years (range 19-78), from three main regions, Asia (12/21), the Mediterranean region (4/21), and South America (5/21). Fourteen patients developed symptoms after the first dose of the vaccine, and 8 after the second dose. Vaccines included mRNA vaccine (10 cases), virus vector vaccine (6 cases), and inactivated vaccine (5 cases). The average time interval from vaccination to onset of symptoms was 7.5 days (range 12 hours to 4 weeks). All 21 patients experienced visual impairment after vaccination, with 20 cases involving both eyes. Sixteen patients showed symptoms of meningitis. Serous retinal detachment was observed in 16 patients, choroidal thickening was observed in 14, aqueous cell in 9, and subretinal fluid in 6. CSF pleocytosis was detected in 7 patients and skin symptoms were found in 3 patients. All patients received corticosteroid therapy, and 8 also received immunosuppressive agents. All patients recovered well, with a mean recovery time of 2 months. Early diagnosis and early treatment are crucial to the prognosis of patients with VKH after vaccination with COVID-19 vaccine. The risk of vaccination against COVID-19 in patients with a history of VKH disease should be evaluated clinically.

Demographic characteristics, clinical presentation and in-hospital outcome among patients with Covid-19 in a Nigerian tertiary hospital

Background We described the demographic/clinical characteristics and in-hospital outcome of patients with COVID-19 at the University of Nigeria Teaching Hospital (UNTH) during the first wave to inform evidence-based responses during subsequent waves in Africa. Methodology We conducted retrospective cohort analyses of adult patients ≥18 years with PCR or GeneXpert-confirmed SARS-CoV-2 infection. Data was extracted from patients' medical records from 1st May to 30th September 2020. Based on disease severity, patients were either hospitalized (82) or managed at home (90). Logistic regression and cox-proportional hazard models were used to determine predictors of severe COVID-19 disease and in-hospital mortality, respectively. Results Of 172 cases, 113 (65.7%) were males, and the mean age was 45 ± 19 years. The majority were urban dwellers (72.1%), 19.8% had a positive history of contact with a confirmed/suspected case, 15.7% were healthcare workers while 68 (39.5%) had co-morbidities. Symptomatic patients comprised 73.3% of cases. Fever (p=0.02) and breathlessness (p=0.03) were commoner in males while diarrhoea (p<0.01) was predominant in females. On multivariate analysis, severe COVID-19 was predicted by the presence of co-morbidity (AOR= 14.44, 95% C.I= 4.79-43.58, p <0.001)and prior antibiotic/antimalarial use (AOR= 6.35, 95% C.I= 2.24-18.05, p =0.001) while being a non-healthcare worker (AOR= 0.18, 95% C.I= 0.04-0.78, p=0.02) was protective. However, none of the variables assessed predicted in-hospital mortality. Conclusion Our findings underscore the contributions of demographic variables in COVID-19 transmission and gender differences in clinical presentation. Underlying comorbidity likewise prior antimicrobial use increased the likelihood of severe COVID-19. The absence of mortality predictors in our study may be related to the relatively small number of deaths. Further studies are recommended to unravel the predominance of severe disease in healthcare workers. © 2023 Kamuzu University of Health Sciences.

Clinical and laboratory features of SARS-CoV-2 variants across multiple rounds of pandemic waves in hospitalized children in an Iranian referral hospital.

BACKGROUND: Since the onset of the COVID-19 pandemic, SARS-CoV-2 has evolved into independent new forms, variants of concern (VOCs). While epidemiological data showed increased transmissibility of VOCs, their impact on clinical outcomes is less clear. This study aimed to investigate the differences between the clinical and laboratory features of children infected with VOCs. METHODS: This study included all cases with SARS-CoV-2-positive nasopharyngeal swabs obtained from patients referred to Children's Medical Center (CMC), an Iranian referral hospital, between July 2021 and March 2022. The inclusion criteria for this study included all patients, regardless of age, who had a positive test anywhere in the hospital setting. Exclusion criteria for the study included those whose data was obtained from non-hospital outpatient settings, or referred from another hospital. The SARS-CoV-2 genome area encoding the S1 domain was amplified and sequenced. The type of variant in each sample was identified based on the mutations in the S1 gene. Demographic characteristics, clinical data, and laboratory findings were collected from the patient's medical records. RESULTS: This study included 87 pediatric cases with confirmed COVID-19, with a median age of 3.5 years (IQR: 1-8.12). Data from sequencing reveals the type of variants as 5 (5.7%) alpha, 53 (60.9%) Delta, and 29 (33.3%) Omicron. The incidence of seizure was higher in patients with Alpha and Omicron infection compared to the Delta group. A higher incidence of diarrhea was reported in Alpha-infected patients, and a higher risk of disease severity, distress, and myalgia was associated with Delta infection. CONCLUSION: Laboratory parameters did not mostly differ among the patients infected with Alpha, Delta, and Omicron. However, these variants may manifest different clinical features. Further studies with larger sample sizes are required to fully understand the clinical manifestations of each variant.

Prediction and Categorization Of COVID-19 Related Dermatological Manifestations Using Machine Learning

COVID-19 is global epidemic instigated because of 'severe acute respiratory syndrome corona virus 2 '. Fever, cough, tiredness, dyspnea, and hypogeusia/ hyposmia are all common signs. Dermatological indications have become more common in recent months among the extrapulmonary indicators associated with COVID-19. Our group proposed a taxonomy based on the polymorphic character of COVID-19-related cutaneous symptoms, which includes the following six primary clinical patterns:Urticarial rash, confluent erythematous/maculopapular/ morbilliform rash, papulovesicular exanthem, chilblain-like acral, livedo reticularis / racemosa-like, purpuric 'vasculitic' patterns. To offer an evaluation of possible pathophysiological routes of COVID19- related cutaneous symptoms, this research focuses upon that clinical features ampersand therapeutic treatment of every category. Machine learning algorithms such as SVM, RF, DT, KNN, LR, and NB are used in the analysis. © 2022 IEEE.

Current status of Guillain-Barré syndrome (GBS) in China: a 10-year comprehensive overview.

Guillain-Barré syndrome (GBS) is an acute inflammatory polyradiculoneuropathy; a disease involving the peripheral nervous system which is the most common cause of acute flaccid paralysis worldwide. So far, it is still lack of a comprehensive overview and understanding of the national epidemiological, clinical characteristics, and the risk factors of GBS in China, as well as differences between China and other countries and regions in these respects. With the global outbreak of the coronavirus disease 2019 (COVID-19), an epidemiological or phenotypic association between severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection and GBS has attracted great attention. In this review, we outlined the current clinical data of GBS in China by retrieving literature, extracting and synthesizing the data of GBS in China from 2010 to 2021. Besides, we compared the characteristics of epidemiology, preceding events and clinical profiles of GBS between China and other countries and regions. Furthermore, in addition to conventional intravenous immunoglobulin (IVIG) and plasma exchange (PE) therapy, the potential therapeutic effects with novel medications in GBS, such as complement inhibitors, etc., have become the research focus in treatments. We found that epidemiological and clinical findings of GBS in China are approximately consistent with those in the International GBS Outcome Study (IGOS) cohort. We provided an overall picture of the present clinical status of GBS in China and summarized the global research progress of GBS, aiming to further understand the characteristics of GBS and improve the future work of GBS worldwide, especially in countries with the middle and low incomes.

CNN approach for predicting survival outcome of patients with COVID-19

Coronavirus disease 2019 (COVID-19) has been challenged specifically with the new variant. The number of patients seeking treatment has increased significantly, putting tremendous pressure on hospitals and healthcare systems. With the potential of artificial intelligence (AI) to leverage clinicians to improve personalized medicine for COVID-19, we propose a deep learning model based on 1D and 3D convolutional neural networks (CNNs) to predict the survival outcome of COVID-19 patients. Our model consists of two CNN channels that operate with CT scans and the corresponding clinical variables. Specifically, each patient data set consists of CT images and the corresponding 44 clinical variables used in the 3D CNN and 1D CNN input, respectively. This model aims to combine imaging and clinical features to predict short-term from long-term survival. Our models demonstrate higher performance metrics compared to state-of-the-art models with AUC-ROC of 91.44 –91.60% versus 84.36 –88.10% and Accuracy of 83.39 –84.47% versus 79.06 –81.94% in predicting the survival groups of patients with COVID-19. Based on the findings, the combined clinical and imaging features in the deep CNN model can be used as a prognostic tool and help to distinguish censored and uncensored cases of COVID-19. IEEE

Clinical features of 153 patients with COVID-19 in Chongqing municipality.

Objective: To analyze the clinical features of patients with COVID-19 in Chongqing Municipality. Method(s): The clinical data, laboratory tests and chest imaging findings of 153 patients COVID-19 admitted in Chongqing Public Health Medical Center from January 26 to February 5, 2020 were retrospectively reviewed. According to the relevant diagnostic criteria, patients were divided into non-severe group (n=132) and severe group (n=21). The correlation between serum index changes and disease severity was analyzed. Result(s): The proportion of patients with underlying diabetes or chronic respiratory diseases in severe group was significantly higher than that in non-severe group (chi2=11.04 and 6.94, P<0.05). The proportion of symptom-free patients in non-severe group was significantly higher than that in severe group (chi2=4.09, P<0.05). The symptoms of fever, fatigue and muscle soreness in the severe group were more common than those in the non-severe group (chi2=4.40, 14.42 and 22.67, P<0.05). Among the concomitant symptoms, the proportion of cough and shortness of breath in the severe group was higher than that in the non-severe group (chi2=8.46 and 4.80, P<0.05). C-reactive protein and D-Dimer levels were higher in the severe group than those in the non-severe group (Z=-4.39 and -1.96, P<0.05), and the number of CD3+ T lymphocyte cells, CD4+ T lymphocyte cells and CD8+ T lymphocyte cells in the severe group was lower than that in the non-severe group (Z=27.25, 20.60 and 17.36, P<0.05). Compared with the non-severe group, both lungs and the right lung lower lobe were more susceptible to be involved(chi2=9.7123.61, P<0.05). Conclusion(s): There are significant differences in underlying diseases, clinical symptoms, imaging manifestations and laboratory findings between severe and non-severe patients with COVID-19.Copyright © 2020 by the Chinese Medical Association.

Clinical Features and Management of COVID-19

Much has been learnt about severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) since the beginning of theCoronavirus disease 2019 (COVID-19) pandemic, including its clinical manifestations, diagnosis, and management. Unlike its zoonotic predecessor SARS-CoV which was largely a symptomatic disease where fever was a hallmark, a significant proportion of SARS-CoV-2 infections can be asymptomatic (40%), while severe disease (requiring oxygen supplementation or ventilatory support) occurs in approximately 20%, and mortality in about 2% of infected patients. Extra-pulmonary COVID-19 manifestations are also more protean, compared to SARS. Supportive care is the mainstay of treatment for most patients, but for those who progress to severe COVID-19, antivirals such as remdesivir and immunomodulatory treatment (such as corticosteroids or theJAK-inhibitor, baricitinib) may improve outcomes. While further advances in the management of COVID-19 are anticipated (including novel therapies), prevention of infection through public health measures (including vaccination), will remain as vital facets in confronting this pandemic. © 2023 by World Scientific Publishing Co. Pte. Ltd.

Problematic consumption of online pornography during the COVID-19 pandemic: Clinical recommendations

The coronavirus disease (COVID-19) pandemic is one of the greatest contemporary challenges. Feelings of fear and uncertainty triggered by this pandemic have had noxious effects on people's mental health. This seems to have increased during quarantine and there is evidence of an intensification of reward- directed behavior. Nevertheless, there are few studies dealing with pornography consumption during this period. The aim of this manuscript is to contextualize this phenomenon during the pandemic and suggest some clinical recommendations on the matter. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Covid-19 and Pregnancy: Epidemiology, Clinical Features, Maternal and Perinatal Outcomes. A Systematic Review

Background and objectives: COVID-19 pandemic had quite a significant impact on a number of obstetric outcomes. This is often directly attributed to complications of COVID-19. This article is a systematically review literature on the epidemiology, clinical features, maternal and perinatal outcomes of COVID-19 in pregnancy. Materials and methods. A PRISMA methodology search was conducted on the databases of PubMed, Scopus, Medline, Google Scholar, Web of Science and Central BMJ using MeSH keywords or combinations of the words"COVID-19", "SARS-CoV-2", "pregnancy", "epidemiology", "comorbid disease", "pregnancy and childbirth outcome", "preeclampsia", "fetus". Only articles published between December 1, 2019 to February 28, 2022 were considered. After preliminary analysis of more than 600 publications, 21 articles were short-listed for final processing. The studies were selected using a Newcastle-Ottawa scale style questionnaire. The clinical features, risk factors, co-morbid conditions, maternal and neonatal outcomes were presented in two separate tables respectively. Results. COVID-19 incidence in pregnancy ranged from 4.9% to 10.0%. Such women were 5.4 times more likely to be hospitalized and 1.5 times more to need ICU care. Dyspnoea and hyperthermia were associated with a high risk of severe maternal (OR 2.56;95% CI 1.92-3.40) and neonatal complications (OR 4.97;95% CI 2.11-11.69). One in ten of neonates had a small weight for gestational age (9.27 +/- 3.18%) and one in three required intensive care unit observation. Conclusions. Despite the increasingly emerging evidence on the associations between pregnancy and COVID-19 infection, the data is sometimes contradictory necessitating further studies.Copyright © 2022 Trylyst. All rights reserved.

A review on the identification of COVID-19, SARS and MERS.

COVID-19 is a global pandemic, which is the third outbreak and epidemic of infectious disease caused by coronavirus in this century and constitutes a major threat to human health.In this paper, COCOVID-19, Severeacute respiratory syndrome (SARS) and Middle East Respiratory syndrome (MERS) were analyzed to distinguish their clinical features, diagnosis, prognosis and prevention, so as to better prevent and treat related diseases.Copyright © 2020 Chinese Medical Association

A review on the identification of COVID-19, SARS and MERS.

COVID-19 is a global pandemic, which is the third outbreak and epidemic of infectious disease caused by coronavirus in this century and constitutes a major threat to human health.In this paper, COCOVID-19, Severeacute respiratory syndrome (SARS) and Middle East Respiratory syndrome (MERS) were analyzed to distinguish their clinical features, diagnosis, prognosis and prevention, so as to better prevent and treat related diseases.Copyright © 2020 Chinese Medical Association

A review on the identification of COVID-19, SARS and MERS.

COVID-19 is a global pandemic, which is the third outbreak and epidemic of infectious disease caused by coronavirus in this century and constitutes a major threat to human health.In this paper, COCOVID-19, Severeacute respiratory syndrome (SARS) and Middle East Respiratory syndrome (MERS) were analyzed to distinguish their clinical features, diagnosis, prognosis and prevention, so as to better prevent and treat related diseases.Copyright © 2020 Chinese Medical Association