AbstractImportanceThe coronavirus disease 2019 (COVID-19) pandemic has placed unprecedented stress on health systems across the world, and reliable estimates of risk for adverse hospital outcomes are needed.ObjectiveTo quantify admission laboratory and comorbidity features associated with critical illness and death and mortality risk across 6 Eastern Massachusetts hospitals.DesignRetrospective cohort study using hospital course, prior diagnoses, and laboratory values through June 5, 2020.SettingEmergency department and inpatient settings from 2 academic medical centers and 4 community hospitals.ParticipantsAll individuals with hospital admission and positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing across these 6 hospitals.Main Outcome or Measuresevere illness defined by ICU admission, mechanical ventilation, or death.ResultsAmong 2,511 hospitalized individuals who tested positive for SARS-CoV-2, 215 (8.6%) were eventually admitted to the ICU, 164 (6.5%) required mechanical ventilation, and 292 (11.6%) died. L1-regression models developed in 3 of these hospitals yielded area under ROC curve (AUC) of 0.823 for severe illness and 0.847 for mortality in the 3 held-out hospitals. In total, 78% of deaths occurred in the highest-risk mortality quintile.Conclusions and RelevanceSpecific admission laboratory studies in concert with sociodemographic features and prior diagnosis facilitate risk stratification among individuals hospitalized for COVID-19.Funding1R56MH115187-01Trial RegistrationNoneKey PointsQuestionHow well can sociodemographic features, laboratory values, and comorbiditeis of individuals hospitalized with coronavirus disease 2019 (COVID-19) in Eastern Massachusetts through June 5, 2020 predict severe illness course?FindingsAmong 2,511 hospitalized individuals who tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and were admitted to one of six hospitals, 215 (8.6%) were eventually admitted to the ICU, 164 (6.5%) required mechanical ventilation, and 292 (11.6%) died. In a risk prediction model, 78% of deaths occurred in the top mortality-risk quintile.MeaningSimple prediction models may assist in risk stratification among hospitalized COVID-19 patients.