Giunta Diego Hernán, Thomas Diego Sanchez, Bustamante Lucrecia, Ratti Maria Florencia Grande, Martinez Bernardo Julio
Internal Medicine Research Unit, Hospital Italiano de Buenos Aires, CABA, Argentina.
Research Department, Hospital Italiano de Buenos Aires, CABA, Argentina.
Intern Emerg Med. 2024 Dec 30. doi: 10.1007/s11739-024-03848-5.
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 receiveroperator 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.
预测潜在的过度拥挤是高效急诊科管理的一项重要工具。我们的目标是利用可获取的高质量信息开发并验证过度拥挤预测模型。方法：对2016年6月至2018年5月布宜诺斯艾利斯意大利医院急诊科连续的天数进行回顾性队列研究。我们估算了整个时间段内每小时的NEDOCS评分，并将结果定义为持续性急诊科严重过度拥挤（EDOC），即NEDOCS评分≥180且持续8小时或更长时间。我们生成了3个具有不同相关结果的逻辑回归预测模型：持续性急诊科严重过度拥挤的开始、结束或发生情况。我们通过内部（随机验证组和自抽样法）和外部验证（不同时间段和不同急诊科）来估计校准和区分度。结果：主要模型包括NEDOCS的开始和发生情况，包括天气变量、与NEDOCS本身相关的变量以及患者流量变量。第二个模型仅考虑持续性急诊科严重过度拥挤的开始，并包括与NEDOCS相关的变量。最后一个模型考虑持续性急诊科严重过度拥挤的结束，它包括与NEDOCS、天气、床位占用和管理相关的变量。在验证组中，主要模型的区分度在受试者工作特征曲线下的面积为0.997（95%置信区间0.994 - 1）。该模型在内部验证时校准度非常高，在外部验证时校准度也可接受。结论：持续性急诊科严重过度拥挤预测模型包含易于获取的变量，可用于在过度拥挤情况下进行有效的资源管理。
