M. Martínez-Lacalzada, A. Viteri-Noel, L. Manzano, M. Fabregate-Fuente, M. Rubio-Rivas, S. Luis García, F. Arnalich Fernández, J. L. Beato Pérez, E. Calvo Manuel, A. Constanza Espino, S. J. Freire Castro, J. Loureiro-Amigo, M. Pesqueira Fontan, A. Piña, A. M. Álvarez Suárez, A. Silva Asiain, B. García López, J. Luque del Pino, J. Sanz Canovas, P. Chazarra Perez, G. M. García García, J. Millán Núñez-Cortés, J. M. Casas Rojo, R. Gómez Huelgas
medRxiv, 2020
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.
Cited by 3 publications.
Field of study: Medicine