Background Currently used prediction tools have limited ability to identify community-dwelling older people at high risk for falls. Prediction models utilizing Electronic Heath Records (EHR) 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 EHR and to internally validate its predictive performance. Methods EHR data of individuals aged 65 or over. 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. Results Data of 36,470 eligible participants were extracted from the dataset. The number of participants who fell at least once was 4,778 (13.1%). The final prediction model included age, sex, history of falls, two medications and five medical conditions. The model had a median area under the receiver operating curve of 0.705 (IQR 0.700-0.714) . Conclusions 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.

中文翻译：

---

使用初级保健电子健康记录的老年人跌倒风险预测模型的开发和内部验证

背景 目前使用的预测工具识别社区老年人跌倒风险的能力有限。利用电子健康记录 (EHR) 的预测模型提供了机会，但到目前为止，作为风险分层工具的临床价值有限；因为其中包括低估跌倒发生率。本研究的目的是结合结构化数据和初级保健 EHR 的自由文本，为社区居住的老年人开发跌倒预测模型，并在内部验证其预测性能。方法 65 岁或以上个体的 EHR 数据。年龄、性别、跌倒史、药物和医疗状况被列为潜在预测因素。瀑布是从自由文本中确定的。我们采用具有最小绝对收缩和选择算子的 Bootstrap 增强惩罚逻辑回归来开发预测模型。我们使用 10 折交叉验证来内部验证预测策略。模型性能是根据辨别和校准来评估的。结果 从数据集中提取了 36,470 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 4,778 (13.1%)。最终的预测模型包括年龄、性别、跌倒史、两种药物和五种医疗状况。该模型的受试者工作曲线下面积中位数为 0.705 (IQR 0.700-0.714)。结论 我们用于识别高跌倒风险老年人的预测模型实现了公平区分，并具有合理的校准。