ABSTRACT
BACKGROUND: The clinical epidemiology of hospitalized COVID-19 patients has never been described before in Lebanon. Moreover, the hospital admission and PCR positivity rates have not been assessed and compared yet. OBJECTIVES: To describe the characteristics and outcomes of hospitalized patients with coronavirus induced disease 2019 (COVID-19) in Lebanon and identify risk factors for severe disease or death. STUDY DESIGN: This is a retrospective mono-center cohort study in which we used patients' files to extract and analyse data on demographic and clinical characteristics, as well as mortality. Moreover, we tracked the pandemic by recording the daily total and ICU inpatient census and the PCR positivity rate for admitted and outpatients. RESULTS: Although the total admission rate increased from September to April, the ICU census switched this trend in December to stabilize at an average of around 10 patients/day until April. The case fatality rate was 19% for the 902 hospitalized patients, of which the majority (80%) had severe COVID-19. The severity odds ratio is significantly decreased in immunosuppressed cases (OR, 0.18; CI, 0.05-0.67; p=0.011). Additionally, the odds of COVID-19 related death are significantly greater if consolidations are found in the chest computed tomography (CT) scan (OR, 12; CI, 2.63-55.08; p=0.0013). CONCLUSION: Consolidations in the lungs significantly increase the COVID-19 death risk. Risk factors identification is important to improve patients' management and vaccination strategies. In addition, hospital statistics are good indicators of a pandemic's track.
ABSTRACT
OBJECTIVE: To develop predictive models for in-hospital mortality and length of stay (LOS) for coronavirus disease 2019 (COVID-19)-positive patients. PATIENTS AND METHODS: We performed a multicenter retrospective cohort study of hospitalized COVID-19-positive patients. A total of 764 patients admitted to 14 different hospitals within the Cleveland Clinic from March 9, 2020, to May 20, 2020, who had reverse transcriptase-polymerase chain reaction-proven coronavirus infection were included. We used LightGBM, a machine learning algorithm, to predict in-hospital mortality at different time points (after 7, 14, and 30 days of hospitalization) and in-hospital LOS. Our final cohort was composed of 764 patients admitted to 14 different hospitals within our system. RESULTS: The median LOS was 5 (range, 1-44) days for patients admitted to the regular nursing floor and 10 (range, 1-38) days for patients admitted to the intensive care unit. Patients who died during hospitalization were older, initially admitted to the intensive care unit, and more likely to be white and have worse organ dysfunction compared with patients who survived their hospitalization. Using the 10 most important variables only, the final model's area under the receiver operating characteristics curve was 0.86 for 7-day, 0.88 for 14-day, and 0.85 for 30-day mortality in the validation cohort. CONCLUSION: We developed a decision tool that can provide explainable and patient-specific prediction of in-hospital mortality and LOS for COVID-19-positive patients. The model can aid health care systems in bed allocation and distribution of vital resources.
ABSTRACT
Currently, there are no approved specific antiviral agents for novel coronavirus disease 2019 (COVID-19). Convalescent plasma has not yet been approved for use in patients with COVID-19 infection; however, it is regulated as an investigational product. This is a case report of a 55-year-old male, with COVID-19 pneumonia who has received convalescent plasma as part of a treatment plan which showed significant radiological and clinical improvement post-treatment.