Predicting inhospital admission at the emergency department: a systematic review.

Autor:	Brink A; Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands a.brink@erasmusmc.nl., Alsma J; Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands., van Attekum LA; Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands., Bramer WM; Medical Library, Erasmus MC, Rotterdam, The Netherlands., Zietse R; Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands., Lingsma H; Public Health, Erasmus MC, Rotterdam, The Netherlands., Schuit SC; Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands.
Jazyk:	angličtina
Zdroj:	Emergency medicine journal : EMJ [Emerg Med J] 2022 Mar; Vol. 39 (3), pp. 191-198. Date of Electronic Publication: 2021 Oct 28.
DOI:	10.1136/emermed-2020-210902
Abstrakt:	Background: 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. Methods: 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. Results: 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 et al and Cameron et al . These models combine clinical applicability, by inclusion of readily available parameters, and appropriate discrimination, calibration and validation. Conclusion: 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. Systematic Review Registration Number: PROSPERO CRD42017057975. Competing Interests: Competing interests: None declared. (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.)
Databáze:	MEDLINE
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