Metastatic Differentiated Thyroid Cancer Survival Is Unaffected by Mode of Preparation for 131I Administration

Context

Recombinant human thyrotropin (rhTSH) is currently not Food and Drug Administration approved for the treatment of high-risk patients with differentiated thyroid cancer (DTC).

Objective

The goal of our study was to compare the outcomes in higher-risk patients with metastatic DTC prepared for radioiodine (RAI) therapy with rhTSH vs thyroid hormone withdrawal (THW).

Methods

A retrospective chart review was performed 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 years (range, 3.3-5.5 years) and for patients with THW-aided therapies was 6.8 years (range, 4.2-11.6 years) (P = .002). Multivariate analysis showed that the method of thyrotropin 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.

Indian COVID-19 dynamics: Prediction using autoregressive integrated moving average modelling

Background: The forecasting of Coronavirus Disease-19 (COVID-19) dynamics is a centrepiece in evidence-based disease management. Numerous approaches that use mathematical modelling have been used to predict the outcome of the pandemic, including data-driven models, empirical and hybrid models. This study was aimed at prediction of COVID-19 evolution in India using a model based on autoregressive integrated moving average (ARIMA). Material and Methods: Real-time Indian data of cumulative cases and deaths of COVID-19 was retrieved from the Johns Hopkins dashboard. The dataset from 11 March 2020 to 25 June 2020 (n = 107 time points) was used to fit the autoregressive integrated moving average model. The model with minimum Akaike Information Criteria was used for forecasting. The predicted root mean square error (PredRMSE) and base root mean square error (BaseRMSE) were used to validate the model. Results: The ARIMA (1,3,2) and ARIMA (3,3,1) model fit best for cumulative cases and deaths, respectively, with minimum Akaike Information Criteria. The prediction of cumulative cases and deaths for next 10 days from 26 June 2020 to 5 July 2020 showed a trend toward continuous increment. The PredRMSE and BaseRMSE of ARIMA (1,3,2) model were 21,137 and 166,330, respectively. Similarly, PredRMSE and BaseRMSE of ARIMA (3,3,1) model were 668.7 and 5,431, respectively. Conclusion: It is proposed that data on COVID-19 be collected continuously, and that forecasting continue in real time. The COVID-19 forecast assist government in resource optimisation and evidence-based decision making for a subsequent state of affairs.

Evidence-based decision making and covid-19: what a posteriori probability distributions speak

Background: In the absence of any pharmaceutical interventions, the management of the COVID-19 pandemic is based on public health measures. The present study fosters evidence-based decision making by estimating various “a posteriori probability distributions" from COVID-19 patients.  Methods: In this retrospective observational study, 987 RT-PCR positive COVID-19 patients from SMS Medical College, Jaipur, India, were enrolled after approval of the institutional ethics committee. The data regarding age, gender, and outcome were collected. The univariate and bivariate distributions of COVID-19 cases with respect to age, gender, and outcome were estimated. The age distribution of COVID-19 cases was compared with the general population's age distribution using the goodness of fit c2 test. The independence of attributes in bivariate distributions was evaluated using the chi-square test for independence. Results: The age group ‘25-29’ has shown highest probability of COVID-19 cases (P [25-29] = 0.14, 95% CI: 0.12- 0.16). The men (P [Male] = 0.62, 95%CI: 0.59-0.65) were dominant sufferers. The most common outcome was recovery (P [Recovered] = 0.79, 95%CI: 0.76-0.81) followed by admitted cases (P [Active]= 0.13, 95%CI: 0.11-0.15) and death (P [Death] = 0.08, 95%CI: 0.06-0.10). The age distribution of COVID-19 cases differs significantly from the age distribution of the general population (c2  =399.04, P < 0.001). The bivariate distribution of COVID-19 across age and outcome was not independent (c2 =106.21, df = 32, P < 0.001). Conclusion: The knowledge of disease frequency patterns helps in the optimum allocation of limited resources and manpower. The study provides information to various epidemiological models for further analysis.

Patient Flow Dynamics in Hospital Systems During Times of COVID-19: Cox Proportional Hazard Regression Analysis

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.

Relationship between ABO blood group phenotypes and nCOVID-19 susceptibility: A retrospective observational study

Background: Since the outbreak of coronavirus disease-19 (COVID-19) research has been continued to explore multiple facets of the disease. The objective of the present study was to evaluate the relationship between blood group phenotypes and COVID-19 susceptibility. Methods: In this retrospective observational study 132 hospitalised COVID-19 patients were enrolled from the Swai Man Singh (SMS) Medical Hospital in Jaipur, India after receiving approval from the Institutional ethics committee. The ABO, Rh and Kell blood group phenotypes along with demographic data of the patients were recorded. The observed proportions of 'A', 'B', 'AB', 'O', 'Rh' and 'Kell' blood groups in COVID-19 patients were compared against the expected proportions (the null hypothesis) of the general population using Pearson's Chi-squared test and partition analysis. Results: There were significant differences between observed and expected frequency for the ABO and Kell blood phenotypes. Further partition analysis of ABO phenotypes showed that the group 'A' phenotypes were more susceptible to COVID-19. The Kell negatives were also more susceptible. The blood groups 'AB', 'B', 'O' and 'Rh' showed no significant difference for susceptibility to COVID-19. Conclusion: The study shows a relationship between ABO, Rh and Kell blood groups and COVID-19 susceptibility. The application of these relationships in clinics should be explored in future studies.

[Therapeutic itinerary and radiological features of positive microscopy tuberculosis patients at Centre Hospitalier National Universitaire de Fann (CHNUF), Dakar]

INTRODUCTION

The long diagnostic delay is responsible for the extension of radiological lesions and spread of TB in the community. These radiological lesions can leave significant scars responsible for respiratory disability.

OBJECTIVE

The aim of the study is to characterize radiologic features according to tuberculosis diagnostic delay.

METHODS

We conducted a prospective cross-sectional study, about 66 patients with positive sputum.

RESULTS

The average diagnostic delay was 16 (±15) weeks; whether 22 (±14.1) weeks in the female gender versus 14.6 (±14.3) weeks in the male gender (P=0.10). The first care use was respectively health centers (62.1%), drugstores (51.9%), and traditional medicine (28.7%). Only 27.2% of patients used the marabouts care. Bilateral radiographic abnormalities in bivariate analysis were associated with consultation at the health center (66.7% versus 33.3%; P<0.031), as is the extension of the lesions (70.2% versus 29.8% RP=1.66 [1.05 to 2.91]; P<0.03). Lake of knowledge of the symptoms of TB was associated with the use of marabouts care (12.5% versus 87.5; PR=0.35 [0.11 to 1.08], P<0.04).

CONCLUSION

These results should prompt consideration in an emergency, appropriate control interventions, advocacy, patient information and medical personnel on the reality of tuberculosis to prevent its spread often causing respiratory disability with radological effects.

[Antithrombin ii in eclampsia: estimation of predictive value]

We have observed in our study that antithrombin III activity decreases very significatively in eclampsia (p < 0.0001). A level of 90% was defined as a threshold. All the rates which are under or equal to 90% have 78.3% as a positive predictive value and those over 90% have a 98.7% as a negative predictive value for the overcoming of eclampsia. We have concluded that the 90% antithrombin III activity represents the alarm level for over coming eclamptic crises. The determination of the antithrombin III activity must be systematically done in every hypertensive pregnancy with proteinuria.