PDF(Pubmed)
Abstract：
BACKGROUND: Prediction models can identify fall-prone individuals. Prediction models can be based on either data from research cohorts (cohort-based) or routinely collected data (RCD-based). We review and compare cohort-based and RCD-based studies describing the development and/or validation of fall prediction models for community-dwelling older adults.
METHODS: Medline and Embase were searched via Ovid until January 2023. We included studies describing the development or validation of multivariable prediction models of falls in older adults (60+). Both risk of bias and reporting quality were assessed using the PROBAST and TRIPOD, respectively.
RESULTS: We included and reviewed 28 relevant studies, describing 30 prediction models (23 cohort-based and 7 RCD-based), and external validation of two existing models (one cohort-based and one RCD-based). The median sample sizes for cohort-based and RCD-based studies were 1365 [interquartile range (IQR) 426-2766] versus 90 441 (IQR 56 442-128 157), and the ranges of fall rates were 5.4% to 60.4% versus 1.6% to 13.1%, respectively. Discrimination performance was comparable between cohort-based and RCD-based models, with the respective area under the receiver operating characteristic curves ranging from 0.65 to 0.88 versus 0.71 to 0.81. The median number of predictors in cohort-based final models was 6 (IQR 5-11); for RCD-based models, it was 16 (IQR 11-26). All but one cohort-based model had high bias risks, primarily due to deficiencies in statistical analysis and outcome determination.
CONCLUSIONS: Cohort-based models to predict falls in older adults in the community are plentiful. RCD-based models are yet in their infancy but provide comparable predictive performance with no additional data collection efforts. Future studies should focus on methodological and reporting quality.
METHODS: Medline and Embase were searched via Ovid until January 2023. We included studies describing the development or validation of multivariable prediction models of falls in older adults (60+). Both risk of bias and reporting quality were assessed using the PROBAST and TRIPOD, respectively.
RESULTS: We included and reviewed 28 relevant studies, describing 30 prediction models (23 cohort-based and 7 RCD-based), and external validation of two existing models (one cohort-based and one RCD-based). The median sample sizes for cohort-based and RCD-based studies were 1365 [interquartile range (IQR) 426-2766] versus 90 441 (IQR 56 442-128 157), and the ranges of fall rates were 5.4% to 60.4% versus 1.6% to 13.1%, respectively. Discrimination performance was comparable between cohort-based and RCD-based models, with the respective area under the receiver operating characteristic curves ranging from 0.65 to 0.88 versus 0.71 to 0.81. The median number of predictors in cohort-based final models was 6 (IQR 5-11); for RCD-based models, it was 16 (IQR 11-26). All but one cohort-based model had high bias risks, primarily due to deficiencies in statistical analysis and outcome determination.
CONCLUSIONS: Cohort-based models to predict falls in older adults in the community are plentiful. RCD-based models are yet in their infancy but provide comparable predictive performance with no additional data collection efforts. Future studies should focus on methodological and reporting quality.
摘要：
背景：预测模型可以识别容易跌倒的个体。预测模型可以基于来自研究队列的数据(基于队列)或常规收集的数据(基于RCD)。我们回顾并比较了基于队列和基于RCD的研究，这些研究描述了社区居住的老年人的跌倒预测模型的开发和/或验证。
方法：通过Ovid搜索Medline和Embase，直到2023年1月。我们纳入了描述老年人(60+)跌倒多变量预测模型发展或验证的研究。使用PROBAST和TRIPOD评估偏倚风险和报告质量，分别。
结果：我们纳入并回顾了28项相关研究，描述30个预测模型(23个基于队列的和7个基于RCD的)，以及两个现有模型(一个基于队列和一个基于RCD)的外部验证。基于队列和基于RCD的研究的中位数样本量为1365[四分位距（IQR）426-2766]与90.441（IQR56.442-128.157），下降幅度为5.4%至60.4%，而下降幅度为1.6%至13.1%，分别。基于队列和基于刚果民盟的模型之间的歧视表现是可比的，接收器工作特性曲线下的相应面积范围为0.65至0.88，而非0.71至0.81。基于队列的最终模型中预测因子的中位数为6(IQR5-11)；对于基于RCD的模型，它是16（IQR11-26）。除了一个基于队列的模型外，所有模型都有很高的偏倚风险，主要是由于统计分析和结果确定方面的不足。
结论：基于队列的预测社区老年人跌倒的模型很多。基于RCD的模型还处于起步阶段，但在没有额外数据收集工作的情况下提供了可比的预测性能。未来的研究应侧重于方法学和报告质量。
方法：通过Ovid搜索Medline和Embase，直到2023年1月。我们纳入了描述老年人(60+)跌倒多变量预测模型发展或验证的研究。使用PROBAST和TRIPOD评估偏倚风险和报告质量，分别。
结果：我们纳入并回顾了28项相关研究，描述30个预测模型(23个基于队列的和7个基于RCD的)，以及两个现有模型(一个基于队列和一个基于RCD)的外部验证。基于队列和基于RCD的研究的中位数样本量为1365[四分位距（IQR）426-2766]与90.441（IQR56.442-128.157），下降幅度为5.4%至60.4%，而下降幅度为1.6%至13.1%，分别。基于队列和基于刚果民盟的模型之间的歧视表现是可比的，接收器工作特性曲线下的相应面积范围为0.65至0.88，而非0.71至0.81。基于队列的最终模型中预测因子的中位数为6(IQR5-11)；对于基于RCD的模型，它是16（IQR11-26）。除了一个基于队列的模型外，所有模型都有很高的偏倚风险，主要是由于统计分析和结果确定方面的不足。
结论：基于队列的预测社区老年人跌倒的模型很多。基于RCD的模型还处于起步阶段，但在没有额外数据收集工作的情况下提供了可比的预测性能。未来的研究应侧重于方法学和报告质量。
