Department of Medical Informatics, Kerman University of Medical Sciences, Kerman, Iran.
Department of Health Information Management, Faculty of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.
BMC Emerg Med. 2024 Apr 4;24(1):54. doi: 10.1186/s12873-024-00965-4.
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.
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.
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.
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.
急诊科（ED）住院时间延长（LOS）与不良临床结局有关。ED LOS 的预测可能有助于优化资源利用、临床管理和基准测试。本研究旨在系统回顾用于预测 ED LOS 的模型，并评估这些模型的报告和方法学质量。
在线数据库 PubMed、Scopus 和 Web of Science（2023 年 9 月 10 日）搜索了报告 ED LOS 预测模型的英文文章。两名评审员独立筛选了确定的标题和摘要。所有描述 ED LOS 预测模型的开发（有或没有内部验证）或外部验证的原始论文均被纳入。
在 12,193 篇独特识别的文章中，有 34 项研究被纳入（29 项描述了新模型的开发，5 项描述了现有模型的验证）。不同的统计和机器学习方法被应用于这些论文。在 39 分的报告评分和 11 分的方法学质量评分中，开发和验证研究的最高报告评分分别为 39 分和 8 分。
已经发表了许多关于 ED LOS 预测模型的研究，但它们相当混杂，存在方法学和报告问题。在预测因子选择方法、样本量、结果的可重复性、缺失值处理技术和避免将连续变量二值化等方面，模型开发研究与较差到公平的方法学质量相关。此外，建议未来的研究者使用确认清单来提高报告质量。
