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OBJECTIVE: To develop a score to predict the need for intensive care unit (ICU) admission in Covid-19. MATERIALS AND METHODS: We assessed patients admitted to a Covid-19 center in Mexico. Patients were segregated into a group that required ICU admission, and a group that never required ICU admission. By logistic regression, we derived predictive models including clinical, laboratory, and imaging findings. The ABC-GOALS was constructed and compared to other scores. RESULTS: We included 329 and 240 patients in the development and validation cohorts, respectively. Onehundred- fifteen patients from each cohort required ICU admission. The clinical (ABC-GOALSc), clinical+laboratory (ABC-GOALScl), clinical+laboratory+image (ABC-GOALSclx) models area under the curve 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), in the development and validation cohorts, respectively. The ABC-GOALScl and ABC-GOALSclxoutperformed other Covid-19 and pneumonia predictive scores. CONCLUSIONS: ABC-GOALS is a tool to timely predict the need for admission to ICU in Covid-19.

salud publica de mexico

Puntaje de riesgo para predecir el ingreso a una unidad de cuidados intensivos en pacientes con Covid-19: puntaje ABC-GOALS./ A risk score to predict admission to the intensive care unit in patients with Covid-19: the ABC-GOALS score.