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Models to predict length of stay in the emergency department: a systematic literature review and appraisal.
Farimani, Raheleh Mahboub; Karim, Hesam; Atashi, Alireza; Tohidinezhad, Fariba; Bahaadini, Kambiz; Abu-Hanna, Ameen; Eslami, Saeid.
Afiliação
  • Farimani RM; Department of Medical Informatics, Kerman University of Medical Sciences, Kerman, Iran.
  • Karim H; Department of Health Information Management, Faculty of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.
  • Atashi A; E-Health Department, Virtual School, Tehran University of Medical Sciences, Tehran, Iran.
  • Tohidinezhad F; Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
  • Bahaadini K; Department of Medical Informatics, Kerman University of Medical Sciences, Kerman, Iran.
  • Abu-Hanna A; Medical Informatics, UMC Location University of Amsterdam, Meibergdreef, Amsterdam, The Netherlands.
  • Eslami S; Amsterdam Public Health, Amsterdam, The Netherlands.
BMC Emerg Med ; 24(1): 54, 2024 Apr 04.
Article em En | MEDLINE | ID: mdl-38575857
ABSTRACT

INTRODUCTION:

Prolonged Length of Stay (LOS) in ED (Emergency Department) has been associated with poor clinical outcomes. Prediction of ED LOS may help optimize resource utilization, clinical management, and benchmarking. This study aims to systematically review models for predicting ED LOS and to assess the reporting and methodological quality about these models.

METHODS:

The online database PubMed, Scopus, and Web of Science (10 Sep 2023) was searched for English language articles that reported prediction models of LOS in ED. Identified titles and abstracts were independently screened by two reviewers. All original papers describing either development (with or without internal validation) or external validation of a prediction model for LOS in ED were included.

RESULTS:

Of 12,193 uniquely identified articles, 34 studies were included (29 describe the development of new models and five describe the validation of existing models). Different statistical and machine learning methods were applied to the papers. On the 39-point reporting score and 11-point methodological quality score, the highest reporting scores for development and validation studies were 39 and 8, respectively.

CONCLUSION:

Various studies on prediction models for ED LOS were published but they are fairly heterogeneous and suffer from methodological and reporting issues. Model development studies were associated with a poor to a fair level of methodological quality in terms of the predictor selection approach, the sample size, reproducibility of the results, missing imputation technique, and avoiding dichotomizing continuous variables. Moreover, it is recommended that future investigators use the confirmed checklist to improve the quality of reporting.
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Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Serviço Hospitalar de Emergência / Tempo de Internação Limite: Humans Idioma: En Revista: BMC Emerg Med Assunto da revista: MEDICINA DE EMERGENCIA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Irã

Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Serviço Hospitalar de Emergência / Tempo de Internação Limite: Humans Idioma: En Revista: BMC Emerg Med Assunto da revista: MEDICINA DE EMERGENCIA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Irã