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

Abstract Background Predicting potential overcrowding is a significant tool in efficient emergency department (ED) management. Our aim was to develop and validate overcrowding predictive models using accessible and high quality information. Methods Retrospective cohort study of consecutive days in the Hospital Italiano de Buenos Aires ED from june 2016 to may 2018. We estimated hourly NEDOCS score for 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. We generated 3 logistic regression predictive models with different related outcomes: beginning, ending or occurrence of Sustained Critical EDOC. We estimated calibration and discrimination as internal (random validation group and bootstrapping) and external validation (different period and different ED). Results The main model included both the beginning and occurrence of NEDOCS, including weather variables, variables related to NEDOCS itself and patient flow variables. The second model considered only the beginning of Sustained Critical EDOC and included variables related to NEDOCS. The last model considered the end of Sustained Critical EDOC and it included variables related to NEDOCS, weather, bed occupancy and management. Discrimination for the main model had an area under the receiver-operator curve 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 are easily obtained and can be used for effective resource management in situations of overcrowding.