An Atypical Case of Prolonged COVID-19 Infection.

Immunocompromised patients with COVID-19 can have prolonged disease courses that require escalation in care to inpatient or ICU settings. We report a case of a prolonged, active COVID-19 infection in an immunocompromised 61-year-old female with a history of non-Hodgkin's lymphoma. During her hospitalization, her cycle thresholds (CT) continued to worsen despite clinical improvement. We compared our patient's course and CTs to other reported cases in immunocompromised patients, investigating the efficacy of CTs and their use in evaluating disease progression and severity. RT-PCR tests targeting specific types of replicative viral RNA may have more utility in assessing disease severity and infectivity in immunocompromised patients. Our patient's disease course, similar to other reported cases, illustrates the need for improved treatment protocols and infection prevention for the immunocompromised population against SARS-CoV-2.

Trends in COVID-19 Publications: Streamlining Research Using NLP and LDA.

Background: Research publications related to the novel coronavirus disease COVID-19 are rapidly increasing. However, current online literature hubs, even with artificial intelligence, are limited in identifying the complexity of COVID-19 research topics. We developed a comprehensive Latent Dirichlet Allocation (LDA) model with 25 topics using natural language processing (NLP) techniques on PubMed® research articles about "COVID." We propose a novel methodology to develop and visualise temporal trends, and improve existing online literature hubs. Our results for temporal evolution demonstrate interesting trends, for example, the prominence of "Mental Health" and "Socioeconomic Impact" increased, "Genome Sequence" decreased, and "Epidemiology" remained relatively constant. Applying our methodology to LitCovid, a literature hub from the National Center for Biotechnology Information, we improved the breadth and depth of research topics by subdividing their pre-existing categories. Our topic model demonstrates that research on "masks" and "Personal Protective Equipment (PPE)" is skewed toward clinical applications with a lack of population-based epidemiological research.

An Atypical Case of Prolonged COVID-19 Infection

Immunocompromised patients with COVID-19 can have prolonged disease courses that require escalation in care to inpatient or ICU settings. We report a case of a prolonged, active COVID-19 infection in an immunocompromised 61-year-old female with a history of non-Hodgkin’s lymphoma. During her hospitalization, her cycle thresholds (CT) continued to worsen despite clinical improvement. We compared our patient’s course and CTs to other reported cases in immunocompromised patients, investigating the efficacy of CTs and their use in evaluating disease progression and severity. RT-PCR tests targeting specific types of replicative viral RNA may have more utility in assessing disease severity and infectivity in immunocompromised patients. Our patient’s disease course, similar to other reported cases, illustrates the need for improved treatment protocols and infection prevention for the immunocompromised population against SARS-CoV-2.

Developing a machine learning framework to determine the spread of COVID-19 in the USA using meteorological, social, and demographic factors

Coronavirus disease of 2019 (COVID-19) has become a pandemic in the matter of a few months, since the outbreak in December 2019 in Wuhan, China. We study the impact of weather factors including temperature and pollution on the spread of COVID-19. We also include social and demographic variables such as per capita gross domestic product (GDP) and population density. Adapting the theory from the field of epidemiology, we develop a framework to build analytical models to predict the spread of COVID-19. In the proposed framework, we employ machine learning methods including linear regression, linear kernel support vector machine (SVM), radial kernel SVM, polynomial kernel SVM, and decision tree. Given the nonlinear nature of the problem, the radial kernel SVM performs the best and explains 95% more variation than the existing methods. In line with the literature, our study indicates the population density is the critical factor to determine the spread. The univariate analysis shows that a higher temperature, air pollution, and population density can increase the spread. On the other hand, a higher per capita GDP can decrease the spread.

SARS-CoV-2 Infection and COVID-19 Outcomes in Rheumatic Diseases: A Systematic Literature Review and Meta-Analysis.

OBJECTIVE: The relative risk of SARS-CoV-2 infection and COVID-19 disease severity among people with rheumatic and musculoskeletal diseases (RMDs) compared to those without RMDs is unclear. This study was undertaken to quantify the risk of SARS-CoV-2 infection in those with RMDs and describe clinical outcomes of COVID-19 in these patients. METHODS: We conducted a systematic literature review using 14 databases from January 1, 2019 to February 13, 2021. We included observational studies and experimental trials in RMD patients that described comparative rates of SARS-CoV-2 infection, hospitalization, oxygen supplementation/intensive care unit (ICU) admission/mechanical ventilation, or death attributed to COVID-19. Methodologic quality was evaluated using the Joanna Briggs Institute critical appraisal tools or the Newcastle-Ottawa scale. Risk ratios (RRs) and odds ratios (ORs) with 95% confidence intervals (95% CIs) were calculated, as applicable for each outcome, using the Mantel-Haenszel formula with random effects models. RESULTS: Of the 5,799 abstracts screened, 100 studies met the criteria for inclusion in the systematic review, and 54 of 100 had a low risk of bias. Among the studies included in the meta-analyses, we identified an increased prevalence of SARS-CoV-2 infection in patients with an RMD (RR 1.53 [95% CI 1.16-2.01]) compared to the general population. The odds of hospitalization, ICU admission, and mechanical ventilation were similar in patients with and those without an RMD, whereas the mortality rate was increased in patients with RMDs (OR 1.74 [95% CI 1.08-2.80]). In a smaller number of studies, the adjusted risk of outcomes related to COVID-19 was assessed, and the results varied; some studies demonstrated an increased risk while other studies showed no difference in risk in patients with an RMD compared to those without an RMD. CONCLUSION: Patients with RMDs have higher rates of SARS-CoV-2 infection and an increased mortality rate.

Clinical Profiles and Outcomes of COVID-19 in Kidney Transplant Recipients: Experience From a High-Volume Public Sector Transplant Center in India.

OBJECTIVES: Data are so far limited on outcomes of kidney transplant recipients with COVID-19 seen at public sector hospitals in developing countries with limited resources. MATERIALS AND METHODS: We retrospectively investigated a cohort of 157 kidney transplant recipients (75% living and 25% deceased donors) seen at a public sector transplant hospital in India from March to December 2020 who had reverse-transcriptase polymerase chain reaction tests that confirmed COVID-19. Demographic data, immunosuppression regimens, clinical profiles, treatments, and outcomes were analyzed. In our center, maintenance immunosuppression was reduced according to disease severity and case-by-case evaluations. There were also 53 patients with asymptomatic or mild COVID-19 symptoms who received home care to optimize the utilization of scarce resources during travel restrictions. RESULTS: In our kidney transplant recipient group, median age was 43 years (133 male; 24 female patients); recipients presented at a median of 4 years after transplant. The most common comorbidities included arterial hypertension (73%) and diabetes (24%); presenting symptoms at the time of COVID-19 positivity included cough (49%), fever (58%), and sputum production (32%). Clinical severity ranged from asymptomatic (4%), mild (45%), moderate (31%), and severe (20%) disease. Statistically significant risk factors for mortality included older age, dyspnea, severe disease, obesity, allograft dysfunction prior to COVID-19, acute kidney injury, higher levels of inflammatory markers (C-reactive protein, interleukin 6, procalcitonin), abnormality in chest radiography, and intensive care/ventilator requirements (P < .05). Overall patient mortality was 9.5% (15/157) in hospitalized patients, 21% (15/71) in patients in the intensive care unit, 100% (15/15) in patients who required ventilation, and 0% among those in home treatment. CONCLUSIONS: The mortality rate in kidney transplant recipients with COVID-19 was higher than in the nonimmunosuppressed general population (1.2%) in India. To our knowledge, this is a largest single-center study of kidney transplant recipients with COVID-19 so far.

The Impact of COVID-19 Pandemic on Nephrology and Transplant Services and Clinical Training in India.

OBJECTIVES: COVID-19 has emerged as a global pandemic with significant impacts on health care systems. The present study was conducted to analyze the effects of the COVID-19 pandemic on nephrology and transplant services and clinical training at our center. MATERIALS AND METHODS: This observational study was conducted at the Institute of Kidney Disease and Research Centre (Ahmedabad, India). Our institute is one of the largest tertiary care centers of its kind in India with around 400 total inpatient beds for nephrology, urology, and transplant patients. In 2019, our center had annual outpatient and inpatient numbers of 132 181 and 7471, respectively, and conducted 412 renal transplant procedures. For this study, monthly data on number of outpatients, inpatients, and patients undergoing renal transplant, as well as various nonelective procedures, conducted in 2019 and 2020 were collected and analyzed. We investigated the impact of the COVID-19 pandemic on various non-COVID-19-related health care facilities and on clinical training and research activities at our institute. RESULTS: During the 2020 COVID-19 period, the number of outpatients and inpatients was greatly reduced compared with data from 2019. A similar decrease was seen in patients undergoing hemodialysis, renal transplant, and nonelective procedures at our center. The COVID-19 period also greatly affected clinical training of residents enrolled at our institute and research activities, as a result of focus on COVID-19 as a priority. CONCLUSIONS: The effects of reduced numbers of outpatients and inpatients on workflow, as well as reduced numbers of renal transplants and nonelective procedures on the health of our patients, are unknown. Hence, a strategic scheme is needed to develop new health care models that can help manage the COVID-19 pandemic at present and any further waves arising in the future.

Feasibility of Convalescent Plasma Therapy in Kidney Transplant Recipients With Severe COVID-19: A Single-Center Prospective Cohort Study.

OBJECTIVES: There are limited clinical data on feasibility and safety of convalescent plasma therapy in kidney transplant recipients with severe COVID-19. The present study was conducted to explore the feasibility of convalescent plasma treatment in 10 kidney transplant recipients with severe COVID-19. MATERIALS AND METHODS: The prospective observational cohort study was conducted at the Institute of Kidney Disease and Research Centre, Ahmedabad, India. All patients were admitted to the intensive care unit and received antiviral therapy, glucocorticoids, and other supportive care. Two doses of 200 mL each of convalescent plasma with neutralization activity of >1:640 were transfused into patients 24 hours apart following the World Health Organization blood transfusion protocol. The endpoints were the improvement of clinical symptoms and laboratory parameters within 1 day and 7 days after convalescent plasma transfusion. RESULTS: The patients showed resolution of clinical symptoms, and there was a significant decrease in inflammatory markers (P < .05) within 7 days of convalescent plasma transfusion. Of the 10 patients, 9 patients had full recovery and 1 patient died. CONCLUSIONS: Convalescent plasma therapy is highly safe and clinically feasible and reduces mortality in kidney transplant recipients with severe COVID-19. Larger clinical registries and randomized clinical trials should be conducted to further explore the clinical outcomes associated with convalescent plasma use in kidney transplant recipients with severe COVID-19.

AI-Enabled COVID-19 Outbreak Analysis and Prediction: Indian States <i>vs.</i> Union Territories

The COVID-19 disease has already spread to more than 213 countries and territories with infected (confirmed) cases of more than 27 million people throughout the world so far, while the numbers keep increasing. In India, this deadly disease was first detected on January 30, 2020, in a student of Kerala who returned from Wuhan. Because of India’s high population density, different cultures, and diversity, it is a good idea to have a separate analysis of each state. Hence, this paper focuses on the comprehensive analysis of the effect of COVID-19 on Indian states and Union Territories and the development of a regression model to predict the number of discharge patients and deaths in each state. The performance of the proposed prediction framework is determined by using three machine learning regression algorithms, namely Polynomial Regression (PR), Decision Tree Regression, and Random Forest (RF) Regression. The results show a comparative analysis of the states and union territories having more than 1000 cases, and the trained model is validated by testing it on further dates. The performance is evaluated using the RMSE metrics. The results show that the Polynomial Regression with an RMSE value of 0.08, shows the best performance in the prediction of the discharged patients. In contrast, in the case of prediction of deaths, Random Forest with a value of 0.14, shows a better performance than other techniques.