Telemedicine for follow up of paediatric urology patients during COVID 19 pandemic: Professional and patient satisfaction, a questionnaires based cross sectional study in a tertiary care hospital in South India

Introduction and Objective: Telemedicine is an established modality to deliver health care to patients remotely. Its role in pediatric urology followup among middle-class semi-urban families is unknown. We conducted a prospective observational questionnaire-based study to assess the patient and provider(urologist) satisfaction and feasibility of teleconsultation across different socioeconomic strata in follow-up of paediatric urology patients during the COVID 19 pandemic. Method(s): The guardians of children treated earlier and due for follow-up were explained and the appointment for teleconsultation was fixed using a video conferencing app. After consultation, consenting caregivers were explained about study and the provider survey was filled by consulting urologist, while the patient questionnaire was filled by principal investigator telephonically. Result(s): A total of 77 virtual visits were completed over 10 months. Median age was 8 years(IQR= 4 to 12) and 82% were boys. The clinical conditions were posterior urethral valves(22%), hypospadias(18%), PUJ obstruction(18%), vesicoureteric reflux(12%) and others(30%). Clinicians found that virtual visits were effective(78%) in deliverance of the care equivalent to the inpatient visit. Patients were equally satisfied(75%). Technical difficulties due to internet connectivity were faced in 24 visits(31%). Video clarity and inability to examine were additional limitations faced(23%). Majority(90%) belonged to the middle socioeconomic strata as per modified Kuppuswamy scale. Families were estimated to have saved a mean of 26,934 rupees(SD +- 7998.06) and a median of 7 days(Range 1-15) of travel time. Conclusion(s): Telemedicine has potential for successful follow-up with cost and time savings. With improving internet connectivity, its potential is likely to increase in future.

The impact of COVID-19 on major trauma (ISS>15) in London, across its four Level 1 centres.

INTRODUCTION: The COVID-19 pandemic has led to reconfiguration of healthcare resources to manage increased demand for acute hospital beds and intensive care places. Concerns were raised regarding continuing provision of critical care for non-COVID patients during the pandemic. The aim of this study was to assess the impact of the COVID-19 pandemic on patients admitted with major trauma (Injury Severity Score >15) across the four Level 1 trauma centres in London. METHODS: Data were collected from all four major trauma centres (MTCs) in London using the Trauma Audit and Research Network database and from local databases at each centre. A 2-month period from 5 March to 5 May 2020 was selected and the same period during 2019 was used to compare changes due to the pandemic. RESULTS: There was a 31% decrease in overall number of patients presenting to the four MTCs during the COVID-19 period compared with 2019. There was no difference in patient demographics or mechanism of injury between the two periods. Sports-related injuries and proportion of self-presentation to hospital were reduced slightly during the pandemic, although the differences were not statistically significant. The mortality rate and association between mortality and injury severity were similar. Proportion of patients requiring intensive care unit facilities also did not change. CONCLUSION: Despite diversion of critical care resources to deal with COVID-related admissions, we did not observe a change in mortality rate or proportion of severely injured patients requiring critical care. Our results suggest London MTCs were able to provide their usual standard of care for critically injured major trauma (Injury Severity Score >15) patients during the pandemic.

Plasma Von Willebrand Factor Levels Predict Survival in COVID-19 Patients Across the Entire Spectrum of Disease Severity.

BACKGROUND: Characterization of reticulo-endothelial activation in COVID-19 may guide treatment. OBJECTIVES: To assess reticulo-endothelial activation and its correlation with disease severity and death in patients across the entire spectrum of COVID-19 severity. METHODS: Consecutive hospitalized COVID-19 patients were studied, with similar number of patients in each disease severity category. Baseline serum ferritin, sCD163 (macrophage activation markers) and plasma von Willebrand factor (VWF) antigen (endothelial activation marker) levels were studied. Clinical parameters and plasma D-dimer levels were also studied. The study parameters were correlated with COVID-19 severity and survival. RESULTS: The 143 patients (104 males [80%], age 54 [42 - 65] years, median [inter-quartile range]) presented 4 (3-7) days after symptom onset. Thirty-four patients had mild disease, 36 had moderate disease, 36 had severe disease and 37 had critical disease at baseline. With increasing COVID-19 severity, ferritin, sCD163, VWF and D-dimer levels significantly increased at baseline, however, 139 patients had normal sCD163 levels. Of the reticulo-endothelial markers, VWF level independently correlated with COVID-19 severity and with survival. VWF level > 332.6 units/dl correlated with COVID-19 severity (odds ratio [OR]: 2.77 [95% confidence interval (C.I): 1.1 - 6.99], p value: 0.031) and in-hospital death (OR [95% CI]: 29.28 [5.2 - 165], p value < 0.001). CONCLUSIONS: Reticulo-endothelial activation markers increased incrementally with worsening COVID-19 severity. Baseline endothelial activation marker (VWF), and not macrophage activation markers, independently correlated with COVID-19 severity and death.

Modelling of reproduction number for COVID-19 in India and high incidence states.

BACKGROUND: Since the onset of the COVID-19 in China, forecasting and projections of the epidemic based on epidemiological models have been in the centre stage. Researchers have used various models to predict the maximum extent of the number of cases and the time of peak. This yielded varying numbers. This paper aims to estimate the effective reproduction number (R) for COVID-19 over time using incident number of cases that are reported by the government. METHODS: Exponential Growth method to estimate basic reproduction rate R0, and Time dependent method to calculate the effective reproduction number (dynamic) were used. "R0" package in R software was used to estimate these statistics. RESULTS: The basic reproduction number (R0) for India was estimated at 1.379 (95% CI: 1.375, 1.384). This was 1.450 (1.441, 1.460) for Maharashtra, 1.444 (1.430, 1.460) for Gujarat, 1.297 (1.284, 1.310) for Delhi and 1.405 (1.389, 1.421) for Tamil Nadu. In India, the R at the first week from March 2-8, 2020 was 3.2. It remained around 2 units for three weeks, from March 9-29, 2020. After March 2020, it started declining and reached around 1.3 in the following week suggesting a stabilisation of the transmissibility rate. CONCLUSION: The study estimated a baseline R0 of 1.379 for India. It also showed that the R was getting stabilised from first week of April (with an average R of 1.29), despite the increase in March. This suggested that in due course there will be a reversal of epidemic. However, these analyses should be revised periodically.

Is BCG associated with reduced incidence of COVID-19? A meta-regression of global data from 160 countries.

BACKGROUND: Global research is running towards to find a vaccine to stop the threat of the COVID-19. The Bacillus Calmette-Guérin (BCG) vaccine that prevents severe forms of tuberculosis is getting more attention in this scenario. The objective of our study was to determine the association between BCG vaccine coverage and incidence of COVID-19 at a national-level across the Globe. METHODS: The data of 160 countries were included in the study. Meta-regression was done to estimate the difference in the incidence of COVID-19 cases between countries with BCG vaccination coverage. BCG coverage was categorized as ≤70%, >70% and no vaccination. The analyses were carried out by adjusting for factors such as population density, income group, latitude, and percentage of the total population under age groups 15-64 and above 65 years of each country. RESULTS: The countries that had ≤70% coverage of BCG vaccine reported 6.5 (95% CI: -8.4 to -4.5) less COVID-19 infections per 10,000 population as compared to countries that reported no coverage. Those that had >70% coverage reported 10.1 (95% CI: -11.4 to -8.7) less infections per 10,000 population compared to those with no BCG countries. CONCLUSION: Our analysis suggests that BCG is associated with reduced COVID-19 infections if the BCG vaccine coverage is over 70%. The region-wise analyses also suggested similar findings, except the Middle East and North African region.

Forecasting COVID-19 epidemic in India and high incidence states using SIR and logistic growth models.

BACKGROUND: Ever since the Coronavirus disease (COVID-19) outbreak emerged in China, there has been several attempts to predict the epidemic across the world with varying degrees of accuracy and reliability. This paper aims to carry out a short-term projection of new cases; forecast the maximum number of active cases for India and selected high-incidence states; and evaluate the impact of three weeks lock down period using different models. METHODS: We used Logistic growth curve model for short term prediction; SIR models to forecast the maximum number of active cases and peak time; and Time Interrupted Regression model to evaluate the impact of lockdown and other interventions. RESULTS: The predicted cumulative number of cases for India was 58,912 (95% CI: 57,960, 59,853) by May 08, 2020 and the observed number of cases was 59,695. The model predicts a cumulative number of 1,02,974 (95% CI: 1,01,987, 1,03,904) cases by May 22, 2020. As per SIR model, the maximum number of active cases is projected to be 57,449 on May 18, 2020. The time interrupted regression model indicates a decrease of about 149 daily new cases after the lock down period, which is statistically not significant. CONCLUSION: The Logistic growth curve model predicts accurately the short-term scenario for India and high incidence states. The prediction through SIR model may be used for planning and prepare the health systems. The study also suggests that there is no evidence to conclude that there is a positive impact of lockdown in terms of reduction in new cases.