Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam UMC-Location AMC, University of Amsterdam, Amsterdam, The Netherlands.
Department of Epidemiology and Biostatistics, Amsterdam UMC-Location VU, VU University Medical Center, Amsterdam, The Netherlands.
J Gerontol A Biol Sci Med Sci. 2022 Jul 5;77(7):1438-1445. doi: 10.1093/gerona/glab311.
Currently used prediction tools have limited ability to identify community-dwelling older people at high risk for falls. Prediction models utilizing electronic health records (EHRs) provide opportunities but up to now showed limited clinical value as risk stratification tool, because of among others the underestimation of falls prevalence. The aim of this study was to develop a fall prediction model for community-dwelling older people using a combination of structured data and free text of primary care EHRs and to internally validate its predictive performance.
We used EHR data of individuals aged 65 or older. Age, sex, history of falls, medications, and medical conditions were included as potential predictors. Falls were ascertained from the free text. We employed the Bootstrap-enhanced penalized logistic regression with the least absolute shrinkage and selection operator to develop the prediction model. We used 10-fold cross-validation to internally validate the prediction strategy. Model performance was assessed in terms of discrimination and calibration.
Data of 36 470 eligible participants were extracted from the data set. The number of participants who fell at least once was 4 778 (13.1%). The final prediction model included age, sex, history of falls, 2 medications, and 5 medical conditions. The model had a median area under the receiver operating curve of 0.705 (interquartile range 0.700-0.714).
Our prediction model to identify older people at high risk for falls achieved fair discrimination and had reasonable calibration. It can be applied in clinical practice as it relies on routinely collected variables and does not require mobility assessment tests.
目前使用的预测工具在识别社区居住的高风险跌倒老年人方面能力有限。利用电子健康记录（EHR）的预测模型提供了机会，但迄今为止，由于跌倒发生率的低估等原因，作为风险分层工具，其临床价值有限。本研究旨在使用初级保健 EHR 的结构化数据和自由文本组合为社区居住的老年人开发跌倒预测模型，并对其预测性能进行内部验证。
我们使用了年龄在 65 岁或以上的个体的 EHR 数据。年龄、性别、跌倒史、药物和医疗状况被纳入潜在的预测因素。从自由文本中确定跌倒事件。我们采用了带有 Bootstrap 增强的最小绝对收缩和选择算子的惩罚逻辑回归来开发预测模型。我们使用 10 折交叉验证对内部分类器的预测性能进行验证。模型性能通过判别能力和校准度来评估。
从数据集提取了 36470 名符合条件的参与者的数据。至少跌倒一次的参与者人数为 4778 人（13.1%）。最终的预测模型包括年龄、性别、跌倒史、2 种药物和 5 种医疗状况。模型的接收器工作特征曲线下面积中位数为 0.705（四分位距 0.700-0.714）。
我们用于识别高风险跌倒的老年人的预测模型具有较好的判别能力和合理的校准度。它可以应用于临床实践，因为它依赖于常规收集的变量，并且不需要进行移动性评估测试。
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