Effectiveness of BBIBP-CorV vaccine in preventing SARS-CoV2 infection and severe outcomes in people living with multiple sclerosis: A population-based study.

BACKGROUND: The objective of the present study was to estimate the effectiveness of the BBIBP-CorV vaccine (VE) in preventing SARS-CoV-2 infection, related hospitalization, and death among people living with multiple sclerosis (PLWMS). METHODS: In this population-based retrospective observational study, data on all PLWMS, vaccination, SARS-CoV-2 tests, hospitalization, and deaths were collected in Isfahan, Iran between February 9, 2021, and November 4, 2021. We estimated the hazard ratio between vaccinated (partially and fully) and unvaccinated groups using the Andersen-Gill extension of the Cox proportional hazards model. We also performed Cox proportional hazards analysis to identify risk factors for breakthrough infection and COVID-19-related hospitalization in fully-immunized group. RESULTS: Of the 9869 PLWMS, 1368 were in partially-vaccinated group, 4107 were in the fully-vaccinated group, and 3794 were in the unvaccinated group. In the partially-vaccinated group, the estimated VE against COVID-19 infection was 39.3% (16%, 56.1%), hospitalization was 64.9% (1.3%, 87.5%), and mortality was 92.7% (88.8%, 100%). The respective results for the fully-vaccinated group were 63.9% (56%, 70.3%), 75.7% (57.5%, 86.1%), and 100%. Progressive MS was independently associated with a greater risk of breakthrough infection (HR=1.952, 95%CI: 1.174-3.246, p = 0.010). Older adults (≥50 years vs. 18-49 years, HR=3.115, 95%CI: 1.145-8.470, p = 0.026) and those on rituximab (HR=7.584; 95% CI: 1.864-30.854; p = 0.005) were at an increased risk of COVID-19-related hospitalization. CONCLUSION: This study showed that two doses of the BBIBP-CorV vaccine can effectively prevent COVID-19 infection and hospitalization among PLWMS. Old PLWMS and those who treating with rituximab are at increased risk of hospitalization after receiving two doses of the vaccine.

Effect of serum magnesium levels on outcomes of patients hospitalized with COVID-19.

BACKGROUND:  The coronavirus disease 2019 (COVID-19) causes acute respiratory illness and multi-organ failure. The critical roles of magnesium in human health suggest that it could have an active role in the prevention and treatment of COVID-19. We measured magnesium levels in hospitalized COVID-19 patients concerning disease progression and mortality. MATERIALS AND METHODS:  This study was conducted in 2321 hospitalized COVID-19 patients. Clinical characteristics from each patient were recorded, and blood samples were collected from all patients upon their first admission to the hospital to determine serum magnesium levels. Patients were divided into two groups based on discharge or death. The effects of magnesium on death, severity, and hospitalization duration were estimated by crude and adjusted odds ratio using Stata Crop (version 12) software. RESULTS:  Mean magnesium levels in patients who died were higher than in discharged patients (2.10 vs 1.96 mg/dl, p 0.05). CONCLUSIONS: We found no relation between hypomagnesaemia on COVID-19 progression, although hypermagnesaemia could affect COVID-19 mortality (Tab. 4, Ref. 34).

Effectiveness of BBIBP-CorV vaccine in preventing SARS-CoV2 infection and severe outcomes in people living with multiple sclerosis: A population-based study

Background The objective of the present study was to estimate the effectiveness of the BBIBP-CorV vaccine (VE) in preventing SARS-CoV-2 infection, related hospitalization, and death among people living with multiple sclerosis (PLWMS). Methods In this population-based retrospective observational study, data on all PLWMS, vaccination, SARS-CoV-2 tests, hospitalization, and deaths were collected in Isfahan, Iran between February 9, 2021, and November 4, 2021. We estimated the hazard ratio between vaccinated (partially and fully) and unvaccinated groups using the Andersen-Gill extension of the Cox proportional hazards model. We also performed Cox proportional hazards analysis to identify risk factors for breakthrough infection and COVID-19-related hospitalization in fully-immunized group. Results Of the 9869 PLWMS, 1368 were in partially-vaccinated group, 4107 were in the fully-vaccinated group, and 3794 were in the unvaccinated group. In the partially-vaccinated group, the estimated VE against COVID-19 infection was 39.3% (16%, 56.1%), hospitalization was 64.9% (1.3%, 87.5%), and mortality was 92.7% (88.8%, 100%). The respective results for the fully-vaccinated group were 63.9% (56%, 70.3%), 75.7% (57.5%, 86.1%), and 100%. Progressive MS was independently associated with a greater risk of breakthrough infection (HR=1.952, 95%CI: 1.174-3.246, p=0.010). Older adults (≥50 years vs. 18-49 years, HR=3.115, 95%CI: 1.145-8.470, p=0.026) and those on rituximab (HR=7.584;95% CI: 1.864-30.854;p=0.005) were at an increased risk of COVID-19-related hospitalization. Conclusion This study showed that two doses of the BBIBP-CorV vaccine can effectively prevent COVID-19 infection and hospitalization among PLWMS. Old PLWMS and those who treating with rituximab are at increased risk of hospitalization after receiving two doses of the vaccine.

Risk and severity of SARS-CoV-2 reinfection among patients with multiple sclerosis vs. the general population: a population-based study.

BACKGROUND: We conducted this study to compare the risk of reinfection between multiple sclerosis (MS) patients and a control group without MS. METHOD: In this retrospective study, data of all SARS-CoV-2 tests (n = 793,301) and almost all MS patients (n = 10,639) in Isfahan province were collected from January 01, 2020 to August 22, 2021. Of the 2196 MS patients and 793,301 persons from the general population who had been tested at least once, 3 control for each MS patient were identified, leaving 1560 MS patients and 4680 controls without MS. We compared the risk of reinfection after 90 days of a primary infection between those with and without a previous positive COVID-19 test. RESULTS: 736 (47.2%) MS patients and 2013 (43.0%) control individuals had at least one positive test. A total of 17 (2.3%) and 22 (1.1%) possible reinfections in MS and control groups were observed. The estimated protection against reinfection in all MS patients, MS patients on rituximab, MS patients on DMTs rather than rituximab, and controls were 68.2% (46.2, 81.2%), 57.4% (- 0.1, 83.1%), 71.5% (45.5, 85.2%), and 82.1% (72.1, 88.5%), respectively. We found no statistically significant difference in estimated protection (p = 0.123) and odd of reinfection (adjusted OR: 2.01 [0.98, 4.08]) between all MS patients and control group. Two patients were hospitalized at first infection but none required hospitalization at reinfection event. CONCLUSIONS: MS patients on rituximab may be at a greater risk of reinfection. Further studies are required to assess the risk of the second reinfection among the MS population.

Vitamin D Level in Laboratory Confirmed COVID-19 and Disease Progression.

OBJECTIVE: There is no conclusive evidence to suggest vitamin D level can prevent or treat infection with the new coronavirus disease 2019. This study aimed to investigate the effects of serum level of vitamin D in patients with coronavirus disease 2019 on death, severity, and hospitalization duration. MATERIALS AND METHODS: Baseline characteristic of patients was extracted from the Isfahan coronavirus disease 2019 registry database (I-CORE). Blood samples were taken from all patients to measure the level of vitamin D (25-hydroxyvitamin D) and categorized. The effect of 25(OH) D on death, severity, and hospitalization duration was analyzed by logistic regression. RESULTS: Among our study patients, 5.5% had a severe deficiency of vitamin D, 23.7% deficiency, and 24.8% insufficiency. Of the 107 patients who died, 7.5% were severely deficient in vitamin D. We found that vitamin D deficiency had no significant effect on death, disease severity, and hospitalization (P > .05). However, having at least one comorbidity increased the odds of death five times after adjusting age > 60 years and gender (P < .0001). The results showed that among all comorbidities, diabetes has the greatest impact on the outcomes as it raised the odds of death, disease severity, and length of hospital stay by 2.23,1.72, and 1.48, respectively, after controlling the age > 60 and gender (P = .0002, P=.08, P=.012). CONCLUSIONS: The mortality, disease severity, and hospitalization of coronavirus disease 2019 patients seem to be not affected by the low levels of 25(OH)D. However, the synergy between vitamin D levels and comorbidities, age, and gender could affect the outcome of coronavirus disease 2019 patients.

The value of thoracic computed tomography scan comparing to reverse transcription-polymerase chain reaction for the diagnosis of COVID-19.

BACKGROUND: Novel coronavirus disease of 2019 (COVID-19) is the current pandemic causing massive morbidity and mortality worldwide. The gold standard diagnostic method in use is reverse transcription-polymerase chain reaction (RT-PCR) which cannot be solely relied upon. Computed tomography (CT) scan is a method currently used for diagnosis of lung disease and can play a substantial role if proved helpful in COVID-19 diagnosis. We conducted this study to evaluate the diagnostic value of CT scan compared to RT-PCR in the diagnosis of COVID-19. MATERIALS AND METHODS: We recruited 291 hospitalized patients suspicious of COVID-19 according to typical clinical findings during February-March 2020. The patients underwent CT-scan and RT-PCR procedures on the day of hospital admission. CT scans were reported by two radiologists as typical, indeterminate, negative, and atypical. Statistical indices were calculated twice: once considering "typical" and "indeterminate" categories as positive and the other time counting "typical" results as positive. RESULTS: The CT reports were classified as typical (64.95%), indeterminate (10.31%), atypical (11%), and negative (13.75%). Considering "typical" and "intermediate" as positive, sensitivity and specificity were 85.3% and 38.8%, respectively, and using the second assumption, the mentioned indices were 75.9% and 50.4%, respectively. CONCLUSION: According to our study, CT results do not create enough diagnostic benefit and could result in incorrect confidence if negative. Since widely available, CT integration in the clinical process may be helpful in screening of suspected patients in epidemics. Yet, suspected patients should be isolated till confirmed by (multiple) PCRs.

Red Blood Cell Distribution Width (RDW) as a Predictor of In-Hospital Mortality in COVID-19 Patients; a Cross Sectional Study.

INTRODUCTION: Red blood cell distribution width (RDW) has been introduced as a predictive factor for mortality in several critical illnesses and infectious diseases. This study aimed to assess the possible relationship between RDW on admission and COVID-19 in-hospital mortality. METHOD: This cross-sectional study was performed using the Isfahan COVID-19 registry. Adult confirmed cases of COVID-19 admitted to four hospitals affiliated with Isfahan University of Medical Sciences in Iran were included. Age, sex, O2 saturation, RDW on admission, Intensive Care Unit admission, laboratory data, history of comorbidities, and hospital outcome were extracted from the registry. Cox proportional hazard regression was used to study the independent association of RDW with mortality. RESULTS: 4152 patients with the mean age of 61.1 ± 16.97 years were included (56.2% male). 597 (14.4%) cases were admitted to intensive care unit (ICU) and 477 (11.5%) cases died. The mortality rate of patients with normal and elevated RDW was 7.8% and 21.2%, respectively (OR= 3.1, 95%CI: 2.6-3.8), which remained statistically significant after adjusting for age, O2 saturation, comorbidities, and ICU admission (2.03, 95% CI: 1.68-2.44). Moreover, elevated RDW mortality Hazard Ratio in patients who were not admitted to ICU was higher than ICU-admitted patients (3.10, 95% CI: 2.35-4.09 vs. 1.47, 95% CI: 1.15-1.88, respectively). CONCLUSION: The results support the presence of an association between elevated RDW and mortality in patients with COVID-19, especially those who were not admitted to ICU. It seems that elevated RDW can be used as a predictor of mortality in COVID-19 cases.

Web-Based Medical Examinations During the COVID-19 Era: Reconsidering Learning as the Main Goal of Examination.

Like other aspects of the health care system, medical education has been greatly affected by the COVID-19 pandemic. To follow the requirements of lockdown and virtual education, the performance of students has been evaluated via web-based examinations. Although this shift to web-based examinations was inevitable, other mental, educational, and technical aspects should be considered to ensure the efficiency and accuracy of this type of evaluation in this era. The easiest way to address the new challenges is to administer traditional questions via a web-based platform. However, more factors should be accounted for when designing web-based examinations during the COVID-19 era. This article presents an approach in which the opportunity created by the pandemic is used as a basis to reconsider learning as the main goal of web-based examinations. The approach suggests using open-book examinations, using questions that require high cognitive domains, using real clinical scenarios, developing more comprehensive examination blueprints, using advanced platforms for web-based questions, and providing feedback in web-based examinations to ensure that the examinees have acquired the minimum competency levels defined in the course objectives.

Model Prediction for In-Hospital Mortality in Patients with COVID-19: A Case-Control Study in Isfahan, Iran.

The COVID-19 pandemic has now imposed an enormous global burden as well as a large mortality in a short time period. Although there is no promising treatment, identification of early predictors of in-hospital mortality would be critically important in reducing its worldwide mortality. We aimed to suggest a prediction model for in-hospital mortality of COVID-19. In this case-control study, we recruited 513 confirmed patients with COVID-19 from February 18 to March 26, 2020 from Isfahan COVID-19 registry. Based on extracted laboratory, clinical, and demographic data, we created an in-hospital mortality predictive model using gradient boosting. We also determined the diagnostic performance of the proposed model including sensitivity, specificity, and area under the curve (AUC) as well as their 95% CIs. Of 513 patients, there were 60 (11.7%) in-hospital deaths during the study period. The diagnostic values of the suggested model based on the gradient boosting method with oversampling techniques using all of the original data were specificity of 98.5% (95% CI: 96.8-99.4), sensitivity of 100% (95% CI: 94-100), negative predictive value of 100% (95% CI: 99.2-100), positive predictive value of 89.6% (95% CI: 79.7-95.7), and an AUC of 98.6%. The suggested model may be useful in making decision to patient's hospitalization where the probability of mortality may be more obvious based on the final variable. However, moderate gaps in our knowledge of the predictors of in-hospital mortality suggest further studies aiming at predicting models for in-hospital mortality in patients with COVID-19.

Challenges and opportunities of digital health in a post-COVID19 world.

Digital health as a rapidly growing medical field relies comprehensively on human health data. Conventionally, the collection of health data is mediated by officially diagnostic instruments, operated by health professionals in clinical environments and under strict regulatory conditions. Mobile health, telemedicine, and other smart devices with Internet connections are becoming the future choices for collecting patient information. Progress of technologies has facilitated smartphones, wearable devices, and miniaturized health-care devices. These devices allow the gathering of an individual's health-care information at the patient's home. The data from these devices will be huge, and by integrating such enormous data using Artificial Intelligence, more detailed phenotyping of disease and more personalized medicine will be realistic. The future of medicine will be progressively more digital, and recognizing the importance of digital technology in this field and pandemic preparedness planning has become urgent.