Clinical characteristics and predictors of mortality among COVID-19 patients in Saudi Arabia.

BACKGROUND: The new coronavirus disease (COVID-19) has caused more than 1.8 million deaths, with a fatality rate of 2.5% in more than 200 countries as of January 4, 2021. Analysis of COVID-19 clinical features can help predict disease severity and risk of mortality, early identification of high-risk patients, and provide knowledge to inform clinical interventions. OBJECTIVE: The purpose of this study is to investigate the clinical characteristics and possible predictors associated with mortality in patients with COVID-19 admitted to King Fahad (KFH), Ohood, and Miqat hospitals in Madina, Saudi Arabia. METHODS: This retrospective observational study to investigate the clinical characteristic and possible predictors associated with mortality for those 119 mild, moderate, or critically ill patients confirmed by laboratory results to have COVID-19 who were admitted to three hospitals in Madina, Saudi Arabia, from March 25, 2020, to July 30, 2020. Data were collected from December 1, 2020, to December 14, 2020. RESULTS: Of the 119 patients included in the study, the mean age was 54.2 (±15.7) years, with 78.2% survivors and 21.8% non-survivors. The demographic analysis indicated that the likelihood of mortality for patients in the older age group (i.e., ≥65 years) was five times higher than those in the younger age group (OR = 5.34, 95% CI 1.71-16.68, p = 0.004). The results also indicated those patients who admitted to the intensive care unit (ICU) was approximately seven times higher odds of mortality compare with those who were not admitted (OR = 6.48, 95% CI 2.52-16.63, p < 0.001). In addition, six laboratory parameters were positively associated with the odds of mortality: white blood cell count (OR = 1.11, 95% CI 1.02-1.21, p = 0.018), neutrophil (OR = 1.11, 95% CI 1.02-1.22, p = 0.020), creatine kinase myocardial band (OR = 1.02, 95% CI 1.00-1.03, p = 0.030), C-reactive protein (OR = 1.01, 95% CI 1.00-1.01, p = 0.002), urea (OR = 1.06, 95% CI 1.01-1.11, p = 0.026), and lactate dehydrogenase (OR = 1.00, 95% CI 1.00-1.01, p = 0.020). CONCLUSIONS: In this cohort, COVID-19 patients within the older age group (≥65 years) admitted to the ICU with increased C-reactive protein levels in particular, were associated with increased odds of mortality. Further clinical observations are warranted to support these findings and enhance the mapping and control of this pandemic.

Epidemiological Modeling of COVID-19 in Saudi Arabia: Spread Projection, Awareness, and Impact of Treatment

The first case of COVID-19 originated in Wuhan, China, after which it spread across more than 200 countries. By 21 July 2020, the rapid global spread of this disease had led to more than 15 million cases of infection, with a mortality rate of more than 4.0% of the total number of confirmed cases. This study aimed to predict the prevalence of COVID-19 and to investigate the effect of awareness and the impact of treatment in Saudi Arabia. In this paper, COVID-19 data were sourced from the Saudi Ministry of Health, covering the period from 31 March 2020 to 21 July 2020. The spread of COVID-19 was predicted using four different epidemiological models, namely the susceptible-infectious-recovered (SIR), generalized logistic, Richards, and Gompertz models. The assessment of models"fit was performed and compared using four statistical indices (root-mean-square error (RMSE), R squared (R2), adjusted R2 ( Radj2), and Akaike"s information criterion (AIC)) in order to select the most appropriate model. Modified versions of the SIR model were utilized to assess the influence of awareness and treatment on the prevalence of COVID-19. Based on the statistical indices, the SIR model showed a good fit to reported data compared with the other models (RMSE = 2790.69, R2 = 99.88%, Radj2 = 99.98%, and AIC = 1796.05). The SIR model predicted that the cumulative number of infected cases would reach 359,794 and that the pandemic would end by early September 2020. Additionally, the modified version of the SIR model with social distancing revealed that there would be a reduction in the final cumulative epidemic size by 9.1% and 168.2% if social distancing were applied over the short and long term, respectively. Furthermore, different treatment scenarios were simulated, starting on 8 July 2020, using another modified version of the SIR model. Epidemiological modeling can help to predict the cumulative number of cases of infection and to understand the impact of social distancing and pharmaceutical intervention on the prevalence of COVID-19. The findings from this study can provide valuable information for governmental policymakers trying to control the spread of this pandemic.

Cytokine Blood Filtration Responses in COVID-19.

The real issue with the COVID-19 pandemic is that a rapidly increasing number of patients with life-threatening complications are admitted in hospitals and are not well-administered. Although a limited number of patients use the intensive care unit (ICU), they consume medical resources, safety equipment, and enormous equipment with little possibility of rapid recovery and ICU discharge. This work reviews effective methods of using filtration devices in treatment to reduce the level of various inflammatory mediators and discharge patients from the ICU faster. Extracorporeal technologies have been reviewed as a medical approach to absorb cytokines. Although these devices do not kill or remove the virus, they are a promising solution for treating patients and their faster removal from the ICU, thus relieving the bottleneck.