Predicting critical illness on initial diagnosis of COVID-19: Development and validation of the PRIORITY model for outpatient applicability.

Martinez-Lacalzada, M.; Viteri-Noel, A.; Manzano, L.; Fabregate-Fuente, M.; Rubio-Rivas, M.; Luis Garcia, S.; Arnalich Fernandez, F.; Beato Perez, J. L.; Calvo Manuel, E.; Constanza Espino, A.; Freire Castro, S. J.; Loureiro-Amigo, J.; Pesqueira Fontan, M.; Pina, A.; Alvarez Suarez, A. M.; Silva Asiain, A.; Garcia Lopez, B.; Luque del Pino, J.; Sanz Canovas, J.; Chazarra Perez, P.; Garcia Garcia, G. M.; Millan Nunez-Cortes, J.; Casas Rojo, J. M.; Gomez Huelgas, R.

OBJECTIVE To develop and validate a prediction model, based on clinical history and examination findings on initial diagnosis of COVID-19, to identify patients at risk of critical outcomes. DESIGN National multicenter cohort study. SETTING Data from the SEMI (Sociedad Espanola de Medicina Interna) COVID-19 Registry, a nationwide cohort of consecutive COVID-19 patients presenting in 132 centers between March 23 and May 21, 2020. Model development used data from hospitals with >300 beds, and validation used those from hospitals with <300 beds. PARTICIPANTS Adults (age [≥] 18 years) presenting with COVID-19 diagnosis. MAIN OUTCOME MEASURE Composite of in-hospital death, mechanical ventilation or admission to intensive care unit. RESULTS There were 10,433 patients, 7,850 (main outcome rate 25.1%) in the model development cohort and 2,583 (main outcome rate 27.0%) in the validation cohort. The clinical variables in the final model were: age, cardiovascular disease, moderate or severe chronic kidney disease, dyspnea, tachypnea, confusion, systolic blood pressure, and SpO2 [≤] 93% or supplementary oxygen requirement at presentation. The model developed had C-statistic of 0.823 (95% confidence interval [CI] 0.813 to 0.834) and calibration slope of 0.995. The external validation had C-statistic of 0.792 (95% CI, 0.772 to 0.812) and calibration slope of 0.872. The model showed positive net benefit in terms of hospitalizations avoided for the predicted probability thresholds between 3% and 79%. CONCLUSIONS Among patients presenting with COVID-19, easily-obtained basic clinical information had good discrimination for identifying patients at risk of critical outcomes, and the model showed good generalizability. A model-based online prediction calculator provided with this paper would facilitate triage of patients during the pandemic.