Anli Mao, Jie Su, Mingzhu Ren, Shuying Chen, Huafang Zhang
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引用次数: 0
摘要
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。尽管近年来为住院老年患者开发了越来越多的跌倒风险预测模型,但尚不清楚这些模型对临床实践和未来研究的有用程度。目的:系统回顾已发表的关于住院老年人跌倒风险预测模型的研究。方法:检索Web of Science、PubMed、Cochrane Library、CINAHL、MEDLINE和Embase数据库,检索从建立到2024年1月11日住院老年人跌倒相关预测模型的研究。从纳入研究中提取数据,包括研究设计、数据来源、样本量、预测因子、模型开发和性能等。使用预测模型偏倚风险评估工具(PROBAST)检查表评估偏倚风险和适用性。结果:共检索到8086项研究,经筛选,纳入13项研究的13个预测模型。四个模型进行了外部验证。8个模型报告了歧视指标,2个模型报告了校准指标。最常见的跌倒预测因素是活动能力、跌倒史、药物和精神疾病。所有的研究都显示出高偏倚风险,主要是由于研究设计不充分和方法上的缺陷。8个模型的AUC值在0.630 ~ 0.851之间。结论:在本研究中,所有纳入的研究均存在高偏倚风险,主要原因是缺乏前瞻性研究设计、不适当的数据分析以及缺乏可靠的外部验证。未来的研究应优先考虑使用严格的方法对住院老年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)注册,编号为CRD42024503718。
Risk prediction models for falls in hospitalized older patients: a systematic review and meta-analysis.
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.
期刊介绍:
BMC Geriatrics is an open access journal publishing original peer-reviewed research articles in all aspects of the health and healthcare of older people, including the effects of healthcare systems and policies. The journal also welcomes research focused on the aging process, including cellular, genetic, and physiological processes and cognitive modifications.