RESUMO
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.
RESUMO
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.
