Epidemiological characteristics of COVID-19 cases among Indians residing in Kuwait

Background: The coronavirus disease 2019 (COVID-19) pandemic has rapidly spread to most countries around the world. Disproportionate spread of COVID-19 among the Indian community in Kuwait prompted heightened surveillance in this community. Aims: To study the epidemiological characteristics of COVID-19 patients and their contacts among the Indian community in Kuwait. Methods: Data collection was done as a part of contact tracing efforts undertaken by the Kuwaiti Ministry of Health. Results: We analysed contact-tracing data for the initial 1348 laboratory-confirmed Indian patients and 6357 contacts (5681 close and 676 casual). The mean (standard deviation) age of the patients was 39.43 (10.5) years and 76.5% of the cases were asymptomatic or had only mild symptoms. Asymptomatic patients were significantly older [40.05 (10.42) years] than patients with severe symptoms [37.54 (10.54) years] (P = 0.024). About 70% of the patients were living in shared accommodation. Most of the close contacts were living in the same household, as compared with casual contacts, who were primarily workplace contacts (P < 0.001). Among the different occupations, healthcare workers had the highest proportion of cases (18.4%). Among the 216 pairs of cases with a clear relationship between the index and secondary cases, the mean serial interval was estimated to be 3.89 (3.69) days, with a median of 3 and interquartile range of 1–5 days. Conclusion: An early increase in the number of COVID-19 cases among the Indian community could be primarily attributed to crowded living conditions and the high proportion of healthcare workers in this community.

Validation of the Kuwait Progression Indicator Score for predicting progression of severity in COVID19

IntroductionIdentifying patients with COVID-19, at risk of having a severe clinical course during their hospitalization is important for appropriate allocation of clinical resources. We recently described the Kuwait Progression Indicator based on laboratory findings, in an initial training cohort derived from the first series of 1096 consecutive patients admitted to Jaber Al-Ahmad Al-Sabah Hospital in Kuwait. The aim of this study was to validate the KPI scoring system in an independent cohort of patients with COVID-19. MethodologyData was collected prospectively for consecutive patients admitted to Jaber Al-Ahmad Al-Sabah Hospital in Kuwait between 24th February - 28th April 2020. Patients were grouped according to the severity of their clinical course as their main outcome, based on clinical and radiological parameters, with ICU admission and death as secondary outcomes. Model discrimination was assessed through the area under the receiver operating characteristic curve (AUC) while model calibration was assessed through a calibration plot and measures of slope and calibration in the large (CITL). ResultsOf 752 patients not used in model development previously, 414 met the criteria for inclusion in this validation study. The baseline characteristics for these 752 patients were similar to the patients that were included in our validation cohort. The area under the curve was equal to 0.904 (95% CI, 0.867-0.942), indicating good model discrimination. The calibration plot and CITL confirmed reasonably good model calibration. Sensitivity and specificity were above 90% for the low and high risk levels respectively. ConclusionsWe were able to validate our previously described laboratory based prognostic scoring system for COVID-19 patients, to predict which patients progressed to a severe clinical course.

Clinical Characteristics, Risk Factors and Outcomes Among the First Consecutive 1,096 Patients Diagnosed with COVID-19: The Kuwait Experience

BackgroundIn Kuwait, prior to the first case of COVID-19 being reported in the country, mass screening of incoming travelers from countries with known outbreaks was performed and resulted in the first identified cases in the country. All COVID-19 cases at the time and subsequently after, were transferred to a single center, Jaber Al-Ahmad Al-Sabah Hospital, where the patients received standardized investigations and treatments. The objective of this study was to characterize the demographics, clinical manifestations and outcomes in this unique patient population. MethodsThis retrospective cohort study was conducted between 24th February 2020 and 20th April 2020. All consecutive patients in the entire State of Kuwait diagnosed with COVID-19 according to WHO guidelines and admitted to Jaber Al-Ahmad Al-Sabah Hospital were recruited. Patients received standardized investigations and treatments. Multivariable analysis was used to determine the associations between risk factors and outcomes. FindingsOf 1096 patients, the median age was 41 years and 81% of patients were male. Most patients were asymptomatic on admission (49.5%), 69.4% had no signs of infection and 94.6% were afebrile. Only 3.6% of patients required an ICU admission and 1.7% were dead at the studys cutoff date. On multivariate analysis, the risk factors found to be significantly associated with admission to intensive care were age above 50 years old, a qSOFA score above 0, smoking, elevated CRP and elevated procalcitonin levels. Asthma, smoking and elevated procalcitonin levels correlated significantly with mortality in our cohort.To our knowledge, this is the first large retrospective cohort study observing the characteristics of the initial consecutive COVID-19 patients of an entire country. Further, large proportion of asymptomatic patients provides novel insights into the clinical features of patients with milder disease. FundingResearch Grant Awarded by the Kuwait Foundation for the Advancement of Science.

Predicting Disease Progression in COVID19: A Score Based On Lab Tests At Time Of Diagnosis

BackgroundCOVID19 is worldwide pandemic that is mild in the majority of patients but can result in a pneumonia like illness with progression to acute respiratory distress syndrome and death. Predicting the disease severity at time of diagnosis can be helpful in prioritizing hospital admission and resources. MethodsWe prospectively recruited 1096 consecutive patients with COVID19 from the Jaber Hospital, a COVID19 facility in Kuwait, between 24 February and 20 April 2020. The primary endpoint of interest was disease severity defined algorithmically. Predefined risk variables were collected at the time of PCR based diagnosis of the infection. Prognostic model development used 5-fold cross-validated regularized logit regression. The cohort was divided into a training and validation cohort and all model development proceeded on the training cohort. ResultsThere were 643 patients with clinical course data of whom 94 developed severe COVID19. In the final model, age, CRP, procalcitonin, lymphocyte and monocyte percentages and serum albumin were independent predictors of a more severe illness course. The final prognostic model demonstrated good discrimination, calibration and internal validity. ConclusionWe developed and validated a simple score calculated at time of diagnosis that can predict patients with severe COVID19 disease.