Abstract Background Identification of patients with novel coronavirus disease 2019 (COVID-19) requiring hospital admission or at high-risk of in-hospital mortality is essential to guide patient triage and to provide timely treatment for higher risk hospitalized patients. Methods A retrospective multi-centre (8 hospital) cohort at Beaumont Health, Michigan, USA, reporting on COVID-19 patients diagnosed between 1 March and 1 April 2020 was used for score validation. The COVID-19 Risk of Complications Score was automatically computed by the EHR. Multivariate logistic regression models were built to predict hospital admission and in-hospital mortality using individual variables constituting the score. Validation was performed using both discrimination and calibration. Results Compared to Green scores, Yellow Scores (OR: 5.72) and Red Scores (OR: 19.1) had significantly higher odds of admission (both p < .0001). Similarly, Yellow Scores (OR: 4.73) and Red Scores (OR: 13.3) had significantly higher odds of in-hospital mortality than Green Scores (both p < .0001). The cross-validated C-Statistics for the external validation cohort showed good discrimination for both hospital admission ( C = 0.79 (95% CI: 0.77–0.81)) and in-hospital mortality ( C = 0.75 (95% CI: 0.71–0.78)). Conclusions The COVID-19 Risk of Complications Score predicts the need for hospital admission and in-hospital mortality patients with COVID-19. Key points: Can an electronic health record generated risk score predict the risk of hospital admission and in-hospital mortality in patients diagnosed with coronavirus disease 2019 (COVID-19)? In both validation cohorts of 2,025 and 1,290 COVID-19, the cross-validated C-Statistics showed good discrimination for both hospital admission (C = 0.79 (95% CI: 0.77–0.81)) and in-hospital mortality (C = 0.75 (95% CI: 0.71–0.78)), respectively. The COVID-19 Risk of Complications Score may help predict the need for hospital admission if a patient contracts SARS-CoV-2 infection and in-hospital mortality for a hospitalized patient with COVID-19.
【저자키워드】 COVID-19, Corona virus, Mortality, Hospitalization, Triage, emergency medicine, Admission, internal medicine, 【초록키워드】 Treatment, coronavirus disease, Hospitalized, SARS-COV-2 infection, hospital, risk, hospitalized patients, novel coronavirus disease, Cohort, Health, validation, Patient, Complication, Hospital admission, USA, predict, score, in-hospital mortality, retrospective, COVID-19 patient, external validation, EHR, identification, higher risk, logistic regression model, patient triage, help, multivariate, validation cohort, green, yellow, variable, patient with COVID-19, Result, was used, diagnosed, was performed, significantly higher, Can, automatically, patients with COVID-19, Red, 【제목키워드】 Hospital admission, predict, in-hospital mortality, COVID-19 patient, clinical risk, External,