住院老年患者跌倒风险预测模型：系统评价与Meta分析
Risk prediction models for falls in hospitalized older patients: a systematic review and meta-analysis.
作者信息
Mao Anli, Su Jie, Ren Mingzhu, Chen Shuying, Zhang Huafang
机构信息
Department of Nursing, the Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, 322000, China.
出版信息
BMC Geriatr. 2025 Jan 14;25(1):29. doi: 10.1186/s12877-025-05688-0.
BACKGROUND
Existing fall risk assessment tools in clinical settings often lack accuracy. Although an increasing number of fall risk prediction models have been developed for hospitalized older patients in recent years, it remains unclear how useful these models are for clinical practice and future research.
OBJECTIVES
To systematically review published studies of fall risk prediction models for hospitalized older adults.
METHODS
A search was performed of the Web of Science, PubMed, Cochrane Library, CINAHL, MEDLINE, and Embase databases: to retrieve studies of predictive models related to falls in hospitalized older adults from their inception until January 11, 2024. Extraction of data from included studies, including study design, data sources, sample size, predictors, model development and performance, etc. Risk of bias and applicability were assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST) checklist.
RESULTS
A total of 8086 studies were retrieved, and after screening, 13 prediction models from 13 studies were included. Four models were externally validated. Eight models reported discrimination metrics and two models reported calibration metrics. The most common predictors of falls were mobility, fall history, medications, and psychiatric disorders. All studies indicated a high risk of bias, primarily due to inadequate study design and methodological flaws. The AUC values of 8 models ranged from 0.630 to 0.851.
CONCLUSIONS
In the present study, all included studies had a high risk of bias, primarily due to the lack of prospective study design, inappropriate data analysis, and the absence of robust external validation. Future studies should prioritize the use of rigorous methodologies for the external validation of fall risk prediction models in hospitalized older adults.
TRIAL REGISTRATION
The study was registered in the International Database of Prospectively Registered Systematic Reviews (PROSPERO) CRD42024503718.
背景
临床环境中现有的跌倒风险评估工具往往缺乏准确性。尽管近年来针对住院老年患者开发了越来越多的跌倒风险预测模型，但这些模型对临床实践和未来研究的有用性仍不明确。
目的
系统评价已发表的关于住院老年成人跌倒风险预测模型的研究。
方法
检索了Web of Science、PubMed、Cochrane图书馆、CINAHL、MEDLINE和Embase数据库：检索从模型创立至2024年1月11日期间与住院老年成人跌倒相关的预测模型研究。从纳入研究中提取数据，包括研究设计、数据来源、样本量、预测因素、模型开发和性能等。使用预测模型偏倚风险评估工具（PROBAST）清单评估偏倚风险和适用性。
结果
共检索到8086项研究，经筛选后，纳入了13项研究中的13个预测模型。4个模型进行了外部验证。8个模型报告了区分度指标，2个模型报告了校准指标。跌倒最常见的预测因素是活动能力、跌倒史、药物和精神障碍。所有研究均表明存在较高的偏倚风险，主要原因是研究设计不充分和方法学缺陷。8个模型的AUC值范围为0.630至0.851。
结论
在本研究中，所有纳入研究均存在较高的偏倚风险，主要原因是缺乏前瞻性研究设计、数据分析不当以及缺乏有力的外部验证。未来的研究应优先采用严谨的方法对住院老年成人跌倒风险预测模型进行外部验证。
试验注册
该研究已在国际前瞻性注册系统评价数据库（PROSPERO）中注册，注册号为CRD42024503718。
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