Objectives: The present study is aimed at estimating patient flow dynamical parameters and requirement of hospital beds. Secondly, the effects of age and gender on parameters were evaluated. Patients and Methods: In this retrospective cohort study, 987 COVID-19 patients were enrolled from SMS Medical College, Jaipur (Rajasthan, India). The survival analysis was carried out from 29 Feb to 19 May 2020 for two hazards – ‘Hazard 1’ was hospital discharge and ‘Hazard 2’ was hospital death. The starting point for survival analysis of the two hazards was considered to be hospital admission . The survival curves were estimated and additional effects of age and gender were evaluated using Cox proportional hazard regression analysis. Results: The Kaplan Meier estimates of lengths of hospital stay (Median =10 days, IQR =10 days) and median survival rate ( more than 60 days due to large amount of censored data) were obtained. The Cox Model for ‘Hazard 1’ showed no significant effect of age and gender on duration of hospital stay. Similarly, the Cox Model 2 showed no significant difference of gender on survival rate. The case fatality rate 8.1 % , recovery rate 78.8% , mortality rate 0.10 per 100 person--days and hospital admission rate 0.35 per 105 person-days were estimated.Conclusion : The study estimates hospital bed requirement based on patient flow dynamic parameters. Furthermore, study concludes that average length of hospital stay were similar for patients of both genders and all age groups.

Background and Objective Recombinant human thyrotropin (rhTSH) is currently not FDA-approved for the treatment of high-risk patients with differentiated thyroid cancer (DTC). The goal of our study was to compare the outcomes in higher risk patients with metastatic DTC prepared for radioiodine (RAI) therapy with rhTSH versus thyroid hormone withdrawal (THW). Methods Retrospective chart review of patients with metastatic DTC in follow-up at MedStar Washington Hospital Center and MedStar Georgetown University Hospital from 2009 to 2017. Patients were divided according to their preparation for RAI therapy, with assessment of progression free survival (PFS) and overall survival (OS). Results Fifty-five patients with distant metastases (16 men, 39 women) were prepared for RAI therapy exclusively either with rhTSH (n= 27) or with THW (n= 28). There were no statistically significant differences between the groups regarding clinicopathological features and history of RAI therapies. The median follow-up time for patients with rhTSH-aided therapies was 4.2 yr (range 3.3 – 5.5 yr) and for patients with THW-aided therapies was 6.8 yr (range 4.2- 11.6 yr) (p=0.002). Multivariate analysis showed that the method of TSH stimulation was not associated with a difference in PFS or OS. Conclusion As has been shown previously for low-risk DTC, this study indicates that the mode of preparation for RAI therapy does not appear to influence the outcomes of patients with metastatic DTC. PFS and OS were similar for patients with THW-aided or rhTSH-aided RAI therapies.

Abstract The forecasting of Coronavirus Disease-19 (COVID-19) dynamics is a centerpiece in evidence based disease management. Numerous approaches that use mathematical modeling have been used to predict the outcome of the pandemic, including data driven models, empirical and hybrid models. This study was aimed at prediction COVID-19 evolution in India using a model based on autoregressive integrated moving average (ARIMA). Retrieving real time data from the Johns Hopkins dashboard from 11 Mar 2020 to 25 Jun 2020 (N = 107 time points) to fit the model. The ARIMA (1,3,2) and ARIMA (3,3,1) model fit best for cumulative cases and deaths respectively with minimum Akaike Informaton Criteria. The prediction of cumulative cases and deaths for next 10 days from 26 Jun 2020 to 05 Jul 2020 showed a trend toward continuous increment. The predicted root mean square error (PredRMSE) and base root mean square error (BaseRMSE) of ARIMA(1,3,2) model was 21137 and 166330 respectively. Similarly, PredRMSE and BaseRMSE of ARIMA(3,3,1) model was 668.7 and 5431 respectively. We propose that data on COVID-19 be collected continuously, and that forecasting continue in real time. The COVID-19 forecast assist government in resource optimization and evidence based decision making for a subsequent state of affairs.

Objectives: The present study is aimed at estimating patient flow dynamic parameters and requirement for hospital beds. Second, the effects of age and gender on parameters were evaluated.Patients and Methods: In this retrospective cohort study, 987 COVID-19 patients were enrolled from SMS Medical College, Jaipur (Rajasthan, India). The survival analysis was carried out from February 29 through May 19, 2020, for two hazards: Hazard 1 was hospital discharge, and Hazard 2 was hospital death. The starting point for survival analysis of the two hazards was considered to be hospital admission. The survival curves were estimated and additional effects of age and gender were evaluated using Cox proportional hazard regression analysis.Results: The Kaplan Meier estimates of lengths of hospital stay (median = 10 days, IQR = 5–15 days) and median survival rate (more than 60 days due to a large amount of censored data) were obtained. The Cox model for Hazard 1 showed no significant effect of age and gender on duration of hospital stay. Similarly, the Cox model 2 showed no significant difference of age and gender on survival rate. The case fatality rate of 8.1%, recovery rate of 78.8%, mortality rate of 0.10 per 100 person-days, and hospital admission rate of 0.35 per 100,000 person-days were estimated.Conclusion: The study estimates hospital bed requirements based on median length of hospital stay and hospital admission rate. Furthermore, the study concludes there are no effects of age and gender on average length of hospital stay and no effects of age and gender on survival time in above-60 age groups.

Abstract Since the outbreak of coronavirus disease-19 research has been continued to explore multiple facets of the disease. The objective of the present study is to evaluate the relationship between blood group phenotypes and COVID-19 susceptibility. In this hospital based, retrospective observational study 132 COVID-19 patients were enrolled from SMS Medical College and attached Hospitals, Jaipur, India after the proper approval from the institutional ethics committee. The ABO, Rh and Kell blood group phenotypes and demographic data of the patients were recorded. The observed proportions of ‘A’ , ‘B’, ‘AB’, ‘O’, ‘Rh’, and ‘Kell’ blood groups phenotypes in COVID-19 patients were compared against the expected proportions (our null hypothesis) of the general population using multinomial tests and partition analysis. There were significant differences between observed and expected frequency in ABO and Kell blood group phenotypes. Further partition analysis of ABO blood phenotypes showed that the group ‘A’ phenotypes were more susceptible for COVID-19. The Kell negatives were also more susceptible. The blood groups ‘AB’, ‘B’, ‘O’, and ‘Rh’ phenotypes showed no significantly different susceptibility for COVID-19. The study shows relationship between ABO, Rh, and Kell blood group phenotypes and COVID-19 susceptibility. The application of these relationship in clinical practice requires more exploratory studies. The following core competencies are addressed in this article: Screening Tool, Epidemiology, Patient Management, Medical Research.