Acceptance towards COVID-19 vaccine among Qassim populations: A cross-sectional study.

Introduction: The approval of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines and obtaining herd immunity raise the optimism about seeing the end of this pandemic but vaccine hesitancy or refusal to vaccinate is a major threat to progress toward achieving herd immunity. In this study, we identify baseline knowledge, myths, misconceptions, attitudes, beliefs, and behaviors toward the COVID-19 vaccine. This help to develop new strategies to raise awareness, correct misconceptions and improve acceptance of the COVID-19 vaccine. This study aim is to evaluate the acceptance of COVID-19 vaccine among population in Qassim region of Saudi Arabia. Methods: This cross-sectional study conducted among target people who were more than 11 years old in Qassim Region of Saudi Arabia using the snowball sample study. A self-administered online questionnaire was used that evaluates the knowledge and acceptance of COVID-19 vaccine among this population. Results: The results show that the participants' high knowledge of COVID-19 translates into good and safe practices, during the COVID-19 pandemic. Public health workers worldwide should concentrate on enlightening and building faith among the unsure and reluctant population regarding security, effectiveness, and adverse effects of the COVID-19 vaccine. Conclusions: The study findings are useful to the policymakers and healthcare professionals who are working on vaccine awareness programs of COVID-19. The findings conclude that the health education interventions should be directed to population of Qassim, Saudi Arabia, at high risk of contracting COVID-19.

Machine learning model for predicting the length of stay in the intensive care unit for Covid-19 patients in the eastern province of Saudi Arabia.

The COVID-19 virus has spread rapidally throughout the world. Managing resources is one of the biggest challenges that healthcare providers around the world face during the pandemic. Allocating the Intensive Care Unit (ICU) beds' capacity is important since COVID-19 is a respiratory disease and some patients need to be admitted to the hospital with an urgent need for oxygen support, ventilation, and/or intensive medical care. In the battle against COVID-19, many governments utilized technology, especially Artificial Intelligence (AI), to contain the pandemic and limit its hazardous effects. In this paper, Machine Learning models (ML) were developed to help in detecting the COVID-19 patients' need for the ICU and the estimated duration of their stay. Four ML algorithms were utilized: Random Forest (RF), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and Ensemble models were trained and validated on a dataset of 895 COVID-19 patients admitted to King Fahad University hospital in the eastern province of Saudi Arabia. The conducted experiments show that the Length of Stay (LoS) in the ICU can be predicted with the highest accuracy by applying the RF model for prediction, as the achieved accuracy was 94.16%. In terms of the contributor factors to the length of stay in the ICU, correlation results showed that age, C-Reactive Protein (CRP), nasal oxygen support days are the top related factors. By searching the literature, there is no published work that used the Saudi Arabia dataset to predict the need for ICU with the number of days needed. This contribution is hoped to pave the path for hospitals and healthcare providers to manage their resources more efficiently and to help in saving lives.

A rare case of grave's disease after SARS-CoV-2 vaccine: is it an adjuvant effect?

The COVID-19 virus has been responsible for the development of several systemic diseases. Recently, the COVID-19 vaccine has also been incriminated in the development of autoimmune diseases. Currently, researchers have focused on the relationship between the COVID-19 vaccine and the activation of autoimmune phenomenon. We report a case of Graves' disease (GD) whose symptoms appeared 3 days after vaccination against COVID-19. A forty-three-year-old female, without pathological history, presented with diarrhea and palpitation. She received her first SARS-CoV-2 Vaccine dose (Pfizer-BioNTech), in August 2021. Three days after the vaccine, she felt palpitations, sleep disorders, muscle weakness, and heat intolerance. On examination, her pulse was 119 beats per minute, she weighed 63 kg, and she had lost 4 kg in only two months. GD was suspected. Thyroid hormone testing showed low thyroid-stimulating hormone, and an elevated serum free thyroxine hormone T4 level. Serology tests were positive for TSH receptor autoantibodies (TRAB). A GD induced by adjuvants of SARS-CoV-2 vaccine has been retained as a final diagnosis. Several autoimmune diseases have been attributed to adjuvant-induced autoimmune/inflammatory syndrome, including systemic sclerosis, systemic lupus erythematosus and rheumatoid arthritis, and recently few cases of GD have been explained by this phenomenon.

Early Predictors of Intensive Care Unit Admission among COVID-19 Patients in Qatar

Background Coronavirus disease (COVID-19) is associated with significant morbidity and mortality. This study aimed to explore the early predictors of intensive care unit (ICU) admission and in-hospital mortality among patients diagnosed with COVID-19. Methods This was a case-control study of adult patients with confirmed COVID-19. Cases were defined as patients admitted to ICU during the period February 29 - May 29, 2020. For each case enrolled, one control was matched by age and gender. Results A total of 1560 patients with confirmed COVID-19 were included. Each group included 780 patients with a predominant male gender (89.7%) and a median age of 49 years (interquartile range = 18). Predictors independently associated with ICU admission were cardiovascular disease (CVD) (adjusted odds ratio (aOR)=1.64, 95% confidence interval (CI): 1.16 - 2.32, p=0.005), diabetes (aOR=1.52, 95% CI: 1.08 - 2.13, p= 0.016), obesity (aOR=1.46, 95% CI: 1.03-2.08, p= 0.034), lymphopenia (aOR=2.69, 95% CI: 1.80-4.02, p< 0.001), high aspartate aminotransferase (AST) (aOR= 2.59, 95% CI: 1.53-4.36, p< 0.001), high ferritin (aOR=1.96, 95% CI: 1.40-2.74, p< 0.001), high C-reactive protein (CRP) (aOR=4.09, 95% CI: 2.81-5.96, p< 0.001), and dyspnea (aOR=2.50, 95% CI: 1.77-3.54, p< 0.001). Similarly, significant predictors of mortality included CVD (aOR=2.16, 95% CI: 1.32- 3.53, p=0.002), diabetes (aOR=1.77, 95% CI: 1.07-2.90, p=0.025), cancer (aOR=4.65, 95% CI: 1.50-14.42, p= 0.008), lymphopenia (aOR=2.34, 95% CI: 1.45-3.78, p= 0.001), and high AST (aOR= 1.89, 95% CI: 1.04-3.43, p=0.036). Risk Factors for ICU admission among patients with COVID-19 (N=1560) Conclusion Having CVD, diabetes, lymphopenia, and increased AST were independent predictors for both ICU admission and in-hospital mortality in patients with COVID-19. In addition, obesity, high ferritin, and CRP levels were associated with increased risk of ICU admission, while cancer was strongly associated with in-hospital mortality. Early identification and monitoring of patients at risk is essential in planning the level of care needed to prevent delay in medical intervention. Disclosures Adel Abou-Ali, PharmD, PhD, Astellas Pharma Global Development, Inc. (Employee)

Toxicology Practice During COVID-19 Pandemic: Experience of the Dammam Poison Control Center-Eastern Province, Saudi Arabia.

BACKGROUND: The sudden emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and scarcity of the accurate information especially in the initial phase of the struggle presented a series of challenges to health systems. OBJECTIVE: To evaluate the changes in poisoning cases regarding distribution, types, and characteristics for better framing and planning of the role of our field in responding to pandemics. METHODS: Study of telephone consultation calls and toxicology analysis records of poisoning cases referred to the Dammam Poison Control Center in Saudi Arabia during the first half of 2020. Their distribution according to frequencies, causes, and other characteristics was compared to the first half of 2019. RESULTS: Analysis of telephone consultation calls revealed that the proportion of exposure to disinfectants and hand sanitizers during first half of 2020 increased to 20.4% (n = 496) and 3.4% (n = 83), respectively, compared to 9.8% (n = 215) and 0.4% (n = 10) for surface disinfectants and hand sanitizers, respectively, during the first half of 2019. In 2020, exposure to disinfectants and hand sanitizers dominated in preschool children (0-5 years). The total number of cases suspected for drugs/drugs of abuse overdose during the first 6 months of 2020 (n = 783) showed a significant decrease (P < 0.001) compared to the first 6 months of 2019 (n = 1086). CONCLUSION: The increased availability and use of disinfectants and sanitizers significantly increased the risk of poisoning, especially in preschool-aged children. Public health education for prevention of such home exposures is urgently needed to avoid unnecessary emergency medical system use in such critical time.