Development and validation of a multivariable predictive model for Emergency Department Overcrowding based on the National Emergency Department Overcrowding Study (NEDOCS) score

Background Predicting potential overcrowding is a significant tool in efficientemergency department (ED) management. Our aim was to develop and validateovercrowding predictive models using accessible and high quality information.Methods Retrospective cohort study of consecutive days in the Hospital Italiano deBuenos Aires ED from june 2016 to may 2018. We estimated hourly NEDOCS scorefor the entire period, and defined the outcome as Sustained Critical ED Overcrowding(EDOC) equal to occurrence of 8 or more hours with a NEDOCS score ≥ 180. Wegenerated 3 logistic regression predictive models with different related outcomes:beginning, ending or occurrence of Sustained Critical EDOC. We estimated calibrationand discrimination as internal (random validation group and bootstrapping) andexternal validation (different period and different ED). Results The main modelincluded both the beginning and occurrence of NEDOCS, including weather variables,variables related to NEDOCS itself and patient flow variables. The second modelconsidered only the beginning of Sustained Critical EDOC and included variablesrelated to NEDOCS. The last model considered the end of Sustained Critical EDOCand it included variables related to NEDOCS, weather, bed occupancy andmanagement. Discrimination for the main model had an area under the receiveroperatorcurve of 0.997 (95% CI 0.994 - 1) in the validation group. Calibration for the model was very high on internal validation and acceptable on external validation.Conclusion The Sustained Critical EDOC predictive model includes variables that areeasily obtained and can be used for effective resource management in situations ofovercrowding.