Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands
Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands.
Emerg Med J. 2022 Mar;39(3):191-198. doi: 10.1136/emermed-2020-210902. Epub 2021 Oct 28.
ED crowding has potential detrimental consequences for both patient care and staff. Advancing disposition can reduce crowding. This may be achieved by using prediction models for admission. This systematic review aims to present an overview of prediction models for admission at the ED. Furthermore, we aimed to identify the best prediction tool based on its performance, validation, calibration and clinical usability.
We included observational studies published in Embase.com, Medline Ovid, Cochrane CENTRAL, Web of Science Core Collection or Google scholar, in which admission models were developed or validated in a general medical population in European EDs including the UK. We used the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) checklist to assess quality of model development. Model performance was presented as discrimination and calibration. The search was performed on 11 October 2020.
In total, 18 539 articles were identified. We included 11 studies, describing 16 different models, comprising the development of 9 models and 12 external validations of 11 models. The risk of bias of the development studies was considered low to medium. Discrimination, as represented by the area under the curve ranged from 0.630 to 0.878. Calibration was assessed in seven models and was strong. The best performing models are the models of Lucke and Cameron . These models combine clinical applicability, by inclusion of readily available parameters, and appropriate discrimination, calibration and validation.
None of the models are yet implemented in EDs. Further research is needed to assess the applicability and implementation of the best performing models in the ED.
PROSPERO CRD42017057975.
急诊拥挤对患者护理和医护人员都有潜在的不利影响。提前安排可以减少拥挤。这可以通过使用入院预测模型来实现。本系统评价旨在概述急诊科入院预测模型。此外，我们旨在根据其性能、验证、校准和临床可用性确定最佳预测工具。
我们纳入了在 Embase.com、Medline Ovid、Cochrane 中心、Web of Science 核心合集或 Google Scholar 发表的观察性研究，这些研究在包括英国在内的欧洲急诊科的一般医疗人群中开发或验证了入院模型。我们使用了预测模型研究的批判性评估和数据提取系统综述（CHARMS）清单来评估模型开发的质量。模型性能表现为区分度和校准度。搜索于 2020 年 10 月 11 日进行。
共确定了 18539 篇文章。我们纳入了 11 项研究，描述了 16 个不同的模型，其中包括 9 个模型的开发和 11 个模型的 12 个外部验证。发展研究的偏倚风险被认为是低到中等。以曲线下面积表示的区分度范围为 0.630 至 0.878。七个模型评估了校准度，结果很强。表现最好的模型是 Lucke 模型和 Cameron 模型。这些模型通过纳入易于获得的参数并具有适当的区分度、校准度和验证度，结合了临床适用性。
目前尚无模型在急诊科实施。需要进一步研究以评估最佳表现模型在急诊科的适用性和实施情况。
PROSPERO CRD42017057975。
