Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium.
BMC Geriatr. 2022 May 30;22(1):465. doi: 10.1186/s12877-022-03156-7.
Disability poses a burden for older persons, and is associated with poor outcomes and high societal costs. Prediction models could potentially identify persons who are at risk for disability. An up to date review of such models is missing.
To identify models developed for the prediction of functional status in community dwelling older persons.
A systematic review was performed including studies of older persons that developed and/or validated prediction models for the outcome functional status. Medline and EMBASE were searched, and reference lists and prospective citations were screened for additional references. Risk of bias was assessed using the PROBAST-tool. The performance of models was described and summarized, and the use of predictors was collated using the bag-of-words text mining procedure.
Forty-three studies were included and reported 167 evaluations of prediction models. The median c-statistic values for the multivariable development models ranged between 0.65 and 0.76 (minimum = 0.58, maximum = 0.90), and were consistently higher than the values of the validation models for which median c-statistic values ranged between 0.6 and 0.68 (minimum = 0.50, maximum = 0.81). A total of 559 predictors were used in the models. The five predictors most frequently used were gait speed (n = 47), age (n = 38), cognition (n = 27), frailty (n = 24), and gender (n = 22).
No model can be recommended for implementation in practice. However, frailty models appear to be the most promising, because frailty components (e.g. gait speed) and frailty indexes demonstrated good to excellent predictive performance. However, the risk of study bias was high. Substantial improvements can be made in the methodology.
残疾给老年人带来负担,与不良结局和高社会成本有关。预测模型可能有助于识别有残疾风险的人。目前缺乏对这些模型的综述。
确定用于预测社区居住老年人功能状态的模型。
系统综述纳入了开发和/或验证功能状态结局预测模型的老年人研究。检索了 Medline 和 EMBASE,并对参考文献列表和前瞻性引用进行筛选,以获取更多参考文献。使用 PROBAST 工具评估偏倚风险。描述和总结模型的性能,并使用词汇袋文本挖掘程序整理预测因子的使用情况。
纳入 43 项研究,报道了 167 项预测模型评估。多变量开发模型的中位数 c 统计值范围为 0.65 至 0.76(最小值=0.58,最大值=0.90),始终高于验证模型的中位数 c 统计值范围为 0.6 至 0.68(最小值=0.50,最大值=0.81)。模型共使用了 559 个预测因子。使用最频繁的五个预测因子是步态速度(n=47)、年龄(n=38)、认知功能(n=27)、虚弱(n=24)和性别(n=22)。
没有模型可以推荐用于实际应用。然而,虚弱模型似乎最有前途,因为虚弱成分(例如步态速度)和虚弱指数表现出良好到优秀的预测性能。然而,研究偏倚风险较高。在方法学上可以进行实质性改进。