Visualization of Covid-19 Pandemic Data: An Analysis

This paper focuses on data visualization techniques and its efficiency. Data visualization refers to pictorial representation of data. Though it is easy to view and understand, the time is yet to come when this technique would see its full utilization. It has a lot of applications in data presentation as well as data exploration, pattern mining and predictive analysis. Its applications would be studied in relation to Covid-19 pandemic. The world is suffering from novel corona virus since December 2019. It has been more than two years and there seems no end to this pandemic. Data visualization techniques will be applied using R programming language, it has been widely adopted by statisticians to study and analyse data, so it will be used here as a tool to study data generated by corona virus outbreak in the last two years. However, the scope of the study is limited to data generated in India for number of confirmed cases, deaths and recovered cases. © 2022 IEEE.

Can Pre-Morbid Echocardiography, Beyond Clinical Risk Factors, Predict Need for Hospitalizing in COVID-19 Patients?

Study Objective: Age and medical co-morbidities are well-known risk factors for need for hospitalization in COVID-19. It is unclear whether, and which, baseline echocardiographic abnormalities may refine triage in the emergency department beyond clinical risk factors, and hence help identify patients at higher risk for need for hospitalization. We aimed to investigate echocardiographic variables associated with risk of hospitalization in COVID-19 patients. Methods: Electronic health records (EHR) were screened retrospectively to identify adults with a positive COVID-19 test throughout St. Luke’s University Health Network from March 1, 2020-October 31, 2020, and had a transthoracic echocardiogram (TTE) within 15-180 days prior. Baseline medical co-morbidities and echocardiographic variables were compared between patients stratified by hospitalization. Continuous variables were compared using Student’s t-test or Mann-Whitney U-test;categorical variables using the χ 2-test or Fisher’s Exact test. Univariate logistic regression was used to select significant predictors for multivariate analysis. Backward stepwise logistic regression was performed to identify predictors of need for hospitalization, a surrogate for mild versus moderate-severe disease. Results: 193 patients met inclusion criteria (83 hospitalized). Mean TTE to COVID19 positivity time was 86±52 days. Hospitalized patients were older and more likely to suffer co-morbidities (Table 1). Age, medical co-morbidities and several echocardiographic variables predicted need for hospitalization. Multivariate analysis revealed age, coronary disease, COPD, and left atrial (LA) enlargement (≥4 cm) independently predicting hospitalization with excellent discrimination (AUC 0.809, figure 1). Estimates plots are depicted in Figure 2. Conclusion: We present, to our knowledge the first cohort indicating that LA enlargement, in a largely unselected population, is an independent marker of need for hospitalization (a surrogate for worse than mild disease) among COVID-19 patients, and could perhaps be considered in addition to clinical risk assessment in the ED, when available. Being “upstream” from the left ventricle (LV), LA enlargement is an indicator of sustained LV pressure and/or volume overload resulting from diverse etiologies, including hypertension, valvular heart disease, and ischemic heart disease. Hence, LA size has long been known to be an independent predictor of cardiovascular events, stroke, and all-cause mortality among patients with underlying cardiovascular disease as well as the general population. Importantly, LA diameter emerged as a more powerful predictor than LV hypertrophy of COVID-19 severity, as indicated by need for hospitalization. [Formula presented] [Formula presented] [Formula presented]

White Swan - The pandemic crisis, lockdown and unlock effect on the Indian stock market

The COVID-19 pandemic crisis, the White Swan event, has pushed the world markets to crash to levels that have not been witnessed since the 2008 Global Financial Crisis. Therefore, this study investigates the impact of the first wave of pandemic COVID-19, nationwide lockdown and unlock on the Indian stock market. The findings reveal that the lockdown has a significant positive impact on the volatility of BSE returns. Secondly, this study investigates the relationship between daily confirmed cases of COVID-19 and the closing price of BSE Sensex using Johansen's cointegration test. The results of cointegration test indicate that there is a long run relationship between daily confirmed cases and closing price of Sensex. Therefore, the findings of this research are beneficial to investors of all categories and portfolio managers. Copyright © 2022 Inderscience Enterprises Ltd.

Clinical and echocardiographic risk score predicts need for hospitalization among patients with COVID-19

Background: Age and medical co-morbidities are known predictors of disease severity in coronavirus disease-2019 (COVID-19). Whether baseline transthoracic echocardiographic (TTE) abnormalities could refine riskstratification in this context remains unknown. Purpose: To analyze performance of a risk score combining clinical and pre-morbid TTE features in predicting risk of hospitalization among patients with COVID-19. Methods: Adult patients testing positive for COVID-19 between March 1st and October 31st, 2020 with pre-infection TTE (within 15-180 days) were selected. Those with severe valvular disease, acute cardiac events between TTE and COVID-19, or asymptomatic carriers of virus (on employment screening/nursing home placement) were excluded. Baseline demographic, clinical co-morbidities, and TTE findings were extracted from electronic health records and compared between groups stratified by hospital admission. Total sample was randomly split into training (≈70%) and validation (≈30%) sets. Age was transformed into ordered categories based on cubic spline regression. Regression model was developed on the training set. Variables found significant (at p<0.10) on univariate analysis were selected for multivariate analysis with hospital admission as outcome. β-coefficients were obtained from 5000 bootstrapped samples after forced entry of significant variables, and scores assigned using Schneeweiss's scoring system. Final risk score performance was compared between training/ validation cohorts using receiver-operating curve (ROC) and calibration curve analyses. Results: 192 patients were included, 83 (43.2%) were admitted. Clinical/ TTE characteristics stratified by hospitalization are in Table 1. Moderate or worse pulmonary hypertension and left atrial enlargement were only TTE parameters with coefficients deserving a score (Table 1). The risk score had excellent discrimination in training and validation sets (figure 1 left panel;AUC 0.785 versus 0.836, p=0.452). Calibration curves showed strong linear correlation between predicted and observed probabilities of hospitalization in both training and validation sets (Figure 1, middle and right panels, respectively). ROC analysis revealed a score ≥7 as having best overall quality with sensitivity and specificity of 70-75% in both training and validation sets. A score ≥12 had 98% and 97% specificity and ≥14 had 100% specificity. Conclusion: A combined clinical and echocardiographic risk score shows promise in predicting risk of hospitalization among patients with COVID- 19, and hence help anticipate resource utilization. External validation and comparison against clinical risk score alone is worth further investigation. (Figure Presented).

Impact of the COVID–19, lockdown and unlock on the Indian stock market and its international linkage with the Chinese stock market

This study investigates the impact of pandemic COVID–19, nationwide lockdown and unlock on the Indian stock market. Firstly, we analyse the impact of lockdown and unlock episodes on the volatility of the Indian stock market returns by employing the EGARCH model. The findings reveal that lockdown has a significant positive impact on the volatility of BSE returns. Secondly, this study investigates the interlinkage between Indian and Chinese markets using cointegration and causality technique in the pre and during COVID periods. Cointegration test indicates that there is no long run relationship between India and China in both the sub–periods. However, the causality results reveal unidirectional causality between India and China in the pre COVID–19 period. Therefore, the findings of this research are beneficial to investors of all categories and portfolio managers. Copyright © 2021 Inderscience Enterprises Ltd.

Predictors of mortality in hospitalized patients with COVID-19;a single centered retrospective analysis

Background: The Coronavirus disease-2019 (COVID-19) has been responsible for the death of over 400,000 people with a continuous rise in prevalence and mortality globally. Identifying hospitalized patients at high mortality risk is critical for triage and health-care resource management regionally, nationally, and globally. We present a retrospective analysis of predictors of mortality in hospitalized COVID-19 patients. Methods: Electronic health records (EHR) of patients admitted between March 1 and April 18, 2020 to St. Luke's University Hospital with a primary diagnosis of COVID-19 were reviewed for medical co-morbidities and initial biochemical/inflammatory markers. Survivors vs non-survivors were compared using χ 2 test, Student's t-test, and Mann-Whitney U-test as appropriate. Univariate logistic regression was used to identify candidate variables for multivariate analysis, which were then included in stepwise backward logistic regression. Statistical analyses were done on SPSS v26 software (IBM, Armonk, NY). Results: Clinical characteristics, biochemical abnormalities and results of univariate regression in our cohort of 560 patients are noted in table 1. Multivariate regression revealed age, congestive heart failure (CHF), and creatinine≥ 1.5 mg/dl as significant predictors of mortality while race (Caucasian), vascular disease, lymphopenia, and elevated ferritin approached significance (Table 2). Table 1: Baseline clinical characteristics, overall and by mortality. Continuous variables are presented as median (25th-75th percentile), and categorical variables as n (%) Significance of difference between subgroups (survivors versus non-survivors) ∗p≤0.05, ∗ ∗p≤0.01, ∗ ∗ ∗p≤0.001 Table 2: Results of stepwise backward conditional logistic regression for predicting mortality among hospitalized COVID-19 patients. (n=334, 287 survivors and 47 non-survivors). ALC - Absolute lymphocyte count, S.E. - Standard error of B. Conclusion: We present one of the largest cohorts to date of hospitalized COVID-19 patients. Age, CHF, and renal disease were significant independent predictors of mortality. Though several inflammatory markers (d-dimer, CRP, procalcitonin) initially predicted mortality, they failed in multivariate analysis, questioning their role in risk-stratifying COVID-19 hospitalized patients. Interestingly, IL-6 used in those severely ill patients to assess candidacy for IL-6 inhibitor therapy (Tocilizumab) failed to predict mortality in our study. Our analysis was limited due to its retrospective nature and unfortunately large amounts of data were missing for some variables (ESR, BNP, IL-6 levels). The missing data was due to rapidly evolving institutional protocols early during the pandemic, leading to non-uniform assessment of these markers.

Impact of COVID-19 pandemic on urology practice: Review of literature

Corona Virus Disease (COVID-19) pandemic is a challenge to the healthcare system including urology which is big and formidable. The present scenario has changed the health preferences to emergency and essential services only. Reallocation of healthcare providers, wards and equipments resulted in suspension of all outpatient and elective activities to select only non-deferrable and critical procedures. Consequently, all health care workers including urologists must abide by the recommendations when dealing with the COVID-19 patients. This pandemic has also disrupted the training and education programs of urology residents also. Subsequently, in this review article, authors have discussed the influence of COVID-19 on urological practice. Authors have also reviewed the recommendations on triaging of urology procedures (emergent and non-emergent), office based urological procedures, oncologic surgeries, paediatric urology, urology-pathology interaction and economic burden on healthcare system during COVID-19 pandemic.