Development of a clinical risk score to predict death in patients with COVID-19.

OBJECTIVE: To build a clinical risk score to aid risk stratification among hospitalised COVID-19 patients. METHODS: The score was built using data of 417 consecutive COVID-19 in patients from Kuwait. Risk factors for COVID-19 mortality were identified by multivariate logistic regressions and assigned weighted points proportional to their beta coefficient values. A final score was obtained for each patient and tested against death to calculate an Receiver-operating characteristic curve. Youden's index was used to determine the cut-off value for death prediction risk. The score was internally validated using another COVID-19 Kuwaiti-patient cohort of 923 patients. External validation was carried out using 178 patients from the Italian CoViDiab cohort. RESULTS: Deceased COVID-19 patients more likely showed glucose levels of 7.0-11.1 mmol/L (34.4%, p < 0.0001) or >11.1 mmol/L (44.3%, p < 0.0001), and comorbidities such as diabetes and hypertension compared to those who survived (39.3% vs. 20.4% [p = 0.0027] and 45.9% vs. 26.6% [p = 0.0036], respectively). The risk factors for in-hospital mortality in the final model were gender, nationality, asthma, and glucose categories (<5.0, 5.5-6.9, 7.0-11.1, or 11.1 > mmol/L). A score of ≥5.5 points predicted death with 75% sensitivity and 86.3% specificity (area under the curve (AUC) 0.901). Internal validation resulted in an AUC of 0.826, and external validation showed an AUC of 0.687. CONCLUSION: This clinical risk score was built with easy-to-collect data and had good probability of predicting in-hospital death among COVID-19 patients.

Previous COVID-19 Infection and Antibody Levels After Vaccination.

Background: The emergence of new COVID-19 variants of concern coupled with a global inequity in vaccine access and distribution has prompted many public health authorities to circumvent the vaccine shortages by altering vaccination protocols and prioritizing persons at high risk. Individuals with previous COVID-19 infection may not have been prioritized due to existing humoral immunity. Objective: We aimed to study the association between previous COVID-19 infection and antibody levels after COVID-19 vaccination. Methods: A serological analysis to measure SARS-CoV-2 immunoglobulin (Ig)G, IgA, and neutralizing antibodies was performed on individuals who received one or two doses of either BNT162b2 or ChAdOx1 vaccines in Kuwait. A Student t-test was performed and followed by generalized linear regression models adjusted for individual characteristics and comorbidities were fitted to compare the average levels of IgG and neutralizing antibodies between vaccinated individuals with and without previous COVID-19 infection. Results: A total of 1,025 individuals were recruited. The mean levels of IgG, IgA, and neutralizing antibodies were higher in vaccinated subjects with previous COVID-19 infections than in those without previous infection. Regression analysis showed a steeper slope of decline for IgG and neutralizing antibodies in vaccinated individuals without previous COVID-19 infection compared to those with previous COVID-19 infection. Conclusion: Previous COVID-19 infection appeared to elicit robust and sustained levels of SARS-CoV-2 antibodies in vaccinated individuals. Given the inconsistent supply of COVID-19 vaccines in many countries due to inequities in global distribution, our results suggest that even greater efforts should be made to vaccinate more people, especially individuals without previous COVID-19 infection.

The Impact of Strict Public Health Measures on COVID-19 Transmission in Developing Countries: The Case of Kuwait.

Background: Many countries have succeeded in curbing the initial outbreak of COVID-19 by imposing strict public health control measures. However, little is known about the effectiveness of such control measures in curbing the outbreak in developing countries. In this study, we seek to assess the impact of various outbreak control measures in Kuwait to gain more insight into the outbreak progression and the associated healthcare burden. Methods: We use a SEIR mathematical model to simulate the first wave of the epidemic outbreak of COVID-19 in Kuwait with additional testing and hospitalization compartments. We calibrate our model by using a NBD observational framework for confirmed case and death counts. We simulate trajectories of model forecasts and assess the effectiveness of public health interventions by using maximum likelihood to estimate both the basic and effective reproduction numbers. Results: Our results indicate that the early strict control measures had the effect of delaying the intensity of the outbreak but were unsuccessful in reducing the effective reproduction number below 1. Forecasted model trajectories suggest a need to expand the healthcare system capacity to cope with the associated epidemic burden of such ineffectiveness. Conclusion: Strict public health interventions may not always lead to the same desired outcomes, particularly when population and demographic factors are not accounted for as in the case in some developing countries. Real-time dynamic modeling can provide an early assessment of the impact of such control measures as well as a forecasting tool to support outbreak surveillance and the associated healthcare expansion planning.

A Review on Potential Antimutagenic Plants of Saudi Arabia

Mutagenic complications can cause disease in both present as well as future generations. The disorders are caused by exogenous and endogenous agents that damage DNA beyond the normal repair mechanism. Rapid industrialization and the population explosion have contributed immensely to changes in the environment, leading to unavoidable exposure to mutagens in our daily life. As it is impossible to prevent exposure, one of the better approaches is to increase the intake of anti-mutagenic substances derived from natural resources. This review summarizes some of the important plants in Saudi Arabia that might have the potential to exhibit anti-mutagenic activity. The data for the review were retrieved from Google scholar, NCBI, PUBMED, EMBASE and the Web of Science. The information in the study has importance since one of the major reasons for mutation is viral infection. Considering the pandemic situation due to novel coronavirus and its aftermath, the native plants of Saudi Arabia could become an important source for reducing mutagenic complications associated with exogenous agents, including viruses.

The effects of non-pharmaceutical interventions on SARS-CoV-2 transmission in different socioeconomic populations in Kuwait: a modeling study.

BACKGROUND: Aggressive non-pharmaceutical interventions (NPIs) may reduce transmission of SARS-CoV-2. The extent to which these interventions are successful in stopping the spread have not been characterized in countries with distinct socioeconomic groups. We compared the effects of a partial lockdown on disease transmission among Kuwaitis (P1) and non-Kuwaitis (P2) living in Kuwait. METHODS: We fit a modified metapopulation SEIR transmission model to reported cases stratified by two groups to estimate the impact of a partial lockdown on the effective reproduction number ([Formula: see text]). We estimated the basic reproduction number ([Formula: see text]) for the transmission in each group and simulated the potential trajectories of an outbreak from the first recorded case of community transmission until 12 days after the partial lockdown. We estimated [Formula: see text] values of both groups before and after the partial curfew, simulated the effect of these values on the epidemic curves and explored a range of cross-transmission scenarios. RESULTS: We estimate [Formula: see text] at 1·08 (95% CI: 1·00-1·26) for P1 and 2·36 (2·03-2·71) for P2. On March 22nd, [Formula: see text] for P1 and P2 are estimated at 1·19 (1·04-1·34) and 1·75 (1·26-2·11) respectively. After the partial curfew had taken effect, [Formula: see text] for P1 dropped modestly to 1·05 (0·82-1·26) but almost doubled for P2 to 2·89 (2·30-3·70). Our simulated epidemic trajectories show that the partial curfew measure greatly reduced and delayed the height of the peak in P1, yet significantly elevated and hastened the peak in P2. Modest cross-transmission between P1 and P2 greatly elevated the height of the peak in P1 and brought it forward in time closer to the peak of P2. CONCLUSION: Our results indicate and quantify how the same lockdown intervention can accentuate disease transmission in some subpopulations while potentially controlling it in others. Any such control may further become compromised in the presence of cross-transmission between subpopulations. Future interventions and policies need to be sensitive to socioeconomic and health disparities.

Clinical characteristics and outcomes of COVID-19 patients with diabetes mellitus in Kuwait.

BACKGROUND: COVID-19 has a highly variable clinical presentation, ranging from asymptomatic to severe respiratory symptoms and death. Diabetes seems to be one of the main comorbidities contributing to a worse COVID-19 outcome. OBJECTIVE: In here we analyze the clinical characteristics and outcomes of diabetic COVID-19 patients Kuwait. METHODS: In this single-center, retrospective study of 417 consecutive COVID-19 patients, we analyze and compare disease severity, outcome, associated complications, and clinical laboratory findings between diabetic and non-diabetic COVID-19 patients. RESULTS: COVID-19 patients with diabetes had more ICU admission than non-diabetic COVID-19 patients (20.1% vs. 16.8%, p < 0.001). Diabetic COVID-19 patients also recorded higher mortality in comparison to non-diabetic COVID-19 patients (16.7% vs. 12.1%, p < 0.001). Diabetic COVID-19 patients had significantly higher prevalence of comorbidities, such as hypertension. Laboratory investigations also highlighted notably higher levels of C-reactive protein in diabetic COVID019 patients and lower estimated glomerular filtration rate. They also showed a higher incidence of complications. logistic regression analysis showed that every 1 mmol/L increase in fasting blood glucose in COVID-19 patients is associated with 1.52 (95% CI: 1.34-1.72, p < 0.001) times the odds of dying from COVID-19. CONCLUSION: Diabetes is a major contributor to worsening outcomes in COVID-19 patients. Understanding the pathophysiology underlining these findings could provide insight into better management and improved outcome of such cases.

Outcomes of COVID-19: Disparities by ethnicity.

OBJECTIVES: To investigate the role of ethnicity in COVID-19 outcome disparities in a cohort in Kuwait. METHODS: This is a retrospective analysis of 405 individuals infected with SARS-CoV-2 in Kuwait. Outcomes such as symptoms severity and mortality were considered. Multivariate logistic regression models were used to report the odds ratios (OR) for ICU admission and dying from COVID-19. RESULTS: The cohort included 290 Arabs and 115 South Asians. South Asians recorded significantly higher COVID-19 death rates compared to Arabs (33% vs. 7.6%, P value<0.001). When compared to Arabs, South Asians also had higher odds of being admitted to the ICU (OR = 6.28, 95% CI: 3.34-11.80, p < 0.001). South Asian patients showed 7.62 (95% CI: 3.62-16.02, p < 0.001) times the odds of dying from COVID-19. CONCLUSION: COVID-19 patients with South Asians ethnicity in Kuwait are more likely to have worse prognosis and outcome when compared to patients with Arab ethnicity. This suggest a possible role for ethnicity in COVID-19 outcome disparities and this role is likely to be multifactorial.

Fasting Blood Glucose and COVID-19 Severity: Nonlinearity Matters.

OBJECTIVE: Fasting blood glucose (FBG) could be an independent predictor for coronavirus disease 2019 (COVID-19) morbidity and mortality. However, when included as a predictor in a model, it is conventionally modeled linearly, dichotomously, or categorically. We comprehensively examined different ways of modeling FBG to assess the risk of being admitted to the intensive care unit (ICU). RESEARCH DESIGN AND METHODS: Utilizing COVID-19 data from Kuwait, we fitted conventional approaches to modeling FBG as well as a nonlinear estimation using penalized splines. RESULTS: For 417 patients, the conventional linear, dichotomous, and categorical approaches to modeling FBG missed key trends in the exposure-response relationship. A nonlinear estimation showed a steep slope until about 10 mmol/L before flattening. CONCLUSIONS: Our results argue for strict glucose management on admission. Even a small incremental increase within the normal range of FBG was associated with a substantial increase in risk of ICU admission for COVID-19 patients.