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适用于所有住院患者和普通内科人群的住院时间预测工具:系统评价与荟萃分析

Hospital length of stay prediction tools for all hospital admissions and general medicine populations: systematic review and meta-analysis.

作者信息

Gokhale Swapna, Taylor David, Gill Jaskirath, Hu Yanan, Zeps Nikolajs, Lequertier Vincent, Prado Luis, Teede Helena, Enticott Joanne

机构信息

Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing, and Health Sciences, Monash University, Clayton, VIC, Australia.

Eastern Health, Box Hill, VIC, Australia.

出版信息

Front Med (Lausanne). 2023 Aug 16;10:1192969. doi: 10.3389/fmed.2023.1192969. eCollection 2023.

Abstract

BACKGROUND

Unwarranted extended length of stay (LOS) increases the risk of hospital-acquired complications, morbidity, and all-cause mortality and needs to be recognized and addressed proactively.

OBJECTIVE

This systematic review aimed to identify validated prediction variables and methods used in tools that predict the risk of prolonged LOS in all hospital admissions and specifically General Medicine (GenMed) admissions.

METHOD

LOS prediction tools published since 2010 were identified in five major research databases. The main outcomes were model performance metrics, prediction variables, and level of validation. Meta-analysis was completed for validated models. The risk of bias was assessed using the PROBAST checklist.

RESULTS

Overall, 25 all admission studies and 14 GenMed studies were identified. Statistical and machine learning methods were used almost equally in both groups. Calibration metrics were reported infrequently, with only 2 of 39 studies performing external validation. Meta-analysis of all admissions validation studies revealed a 95% prediction interval for theta of 0.596 to 0.798 for the area under the curve. Important predictor categories were co-morbidity diagnoses and illness severity risk scores, demographics, and admission characteristics. Overall study quality was deemed low due to poor data processing and analysis reporting.

CONCLUSION

To the best of our knowledge, this is the first systematic review assessing the quality of risk prediction models for hospital LOS in GenMed and all admissions groups. Notably, both machine learning and statistical modeling demonstrated good predictive performance, but models were infrequently externally validated and had poor overall study quality. Moving forward, a focus on quality methods by the adoption of existing guidelines and external validation is needed before clinical application.

SYSTEMATIC REVIEW REGISTRATION

https://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42021272198.

摘要

背景

不必要的延长住院时间(LOS)会增加医院获得性并发症、发病率和全因死亡率的风险,需要积极识别并加以解决。

目的

本系统评价旨在确定用于预测所有住院患者,特别是普通内科(GenMed)住院患者延长住院时间风险的工具中经过验证的预测变量和方法。

方法

在五个主要研究数据库中识别2010年以来发表的住院时间预测工具。主要结果是模型性能指标、预测变量和验证水平。对经过验证的模型进行荟萃分析。使用PROBAST清单评估偏倚风险。

结果

总体而言,共识别出25项所有住院患者研究和14项普通内科研究。两组中使用统计和机器学习方法的频率几乎相同。校准指标报告较少,39项研究中只有2项进行了外部验证。所有住院患者验证研究的荟萃分析显示,曲线下面积的theta的95%预测区间为0.596至0.798。重要的预测类别包括合并症诊断和疾病严重程度风险评分、人口统计学和入院特征。由于数据处理和分析报告不佳,总体研究质量被认为较低。

结论

据我们所知,这是第一项系统评价,评估普通内科和所有住院患者组中住院时间风险预测模型的质量。值得注意的是,机器学习和统计建模均显示出良好的预测性能,但模型很少进行外部验证,总体研究质量较差。展望未来,在临床应用之前,需要通过采用现有指南和外部验证来关注质量方法。

系统评价注册

https://www.crd.york.ac.uk/PROSPERO/,标识符:CRD42021272198。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be4f/10469540/69c2a9784bec/fmed-10-1192969-g0001.jpg

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