Background. COVID-19 pandemic poses a burden on hospital resources and intensive care unit (ICU) occupation. This study aimed to provide a scoring system that, assessed upon first-contact evaluation at the emergency department, predicts the need for ICU admission. Methods. We prospectively assessed patients admitted to a COVID-19 reference center in Mexico City between March 16th and May 21st, and split them into development and validation cohorts. Patients were segregated into a group that required admission to ICU, and a group that never required ICU admission and was discharged from hospitalization. By logistic regression, we constructed predictive models for ICU admission, including clinical, laboratory, and imaging findings from the emergency department evaluation. The ABC-GOALS score was created by assigning values to the weighted odd ratios. The score was compared to other COVID-19 and pneumonia scores through the area under the curve (AUC). Results. We included 569 patients divided into development (n=329) and validation (n=240) cohorts. One-hundred-fifteen patients from each cohort required admission to ICU. The clinical model (ABC-GOALSc) included sex, obesity, the Charlson comorbidity index, dyspnea, arterial pressure, and respiratory rate at triage evaluation. The clinical plus laboratory model (ABC-GOALScl) added serum albumin, glucose, lactate dehydrogenase, and S/F ratio to the clinical model. The model that included imaging (ABC-GOALSclx) added the CT scan finding of >50% lung involvement. The model AUC were 0.79 (95%CI 0.74-0.83) and 0.77 (95%CI 0.71-0.83), 0.86 (95%CI 0.82-0.90) and 0.87 (95%CI 0.83-0.92), 0.88 (95%CI 0.84-0.92) and 0.86 (95%CI 0.81-0.90) for the clinical, laboratory and imaging models in the development and validation cohorts, respectively. The ABC-GOALScl and ABC-GOALSclx scores outperformed other COVID-19 and pneumonia-specific scores. Conclusion. The ABC-GOALS score is a tool to evaluate patients with COVID-19 at admission to the emergency department, which allows to timely predict their risk of admission to an ICU.