Institute for Applied Health Research, University of Birmingham, Birmingham, UK.
National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, University of Birmingham, Birmingham, UK.
Age Ageing. 2024 Mar 1;53(3). doi: 10.1093/ageing/afae057.
Falls are common in older adults and can devastate personal independence through injury such as fracture and fear of future falls. Methods to identify people for falls prevention interventions are currently limited, with high risks of bias in published prediction models. We have developed and externally validated the eFalls prediction model using routinely collected primary care electronic health records (EHR) to predict risk of emergency department attendance/hospitalisation with fall or fracture within 1 year.
Data comprised two independent, retrospective cohorts of adults aged ≥65 years: the population of Wales, from the Secure Anonymised Information Linkage Databank (model development); the population of Bradford and Airedale, England, from Connected Bradford (external validation). Predictors included electronic frailty index components, supplemented with variables informed by literature reviews and clinical expertise. Fall/fracture risk was modelled using multivariable logistic regression with a Least Absolute Shrinkage and Selection Operator penalty. Predictive performance was assessed through calibration, discrimination and clinical utility. Apparent, internal-external cross-validation and external validation performance were assessed across general practices and in clinically relevant subgroups.
The model's discrimination performance (c-statistic) was 0.72 (95% confidence interval, CI: 0.68 to 0.76) on internal-external cross-validation and 0.82 (95% CI: 0.80 to 0.83) on external validation. Calibration was variable across practices, with some over-prediction in the validation population (calibration-in-the-large, -0.87; 95% CI: -0.96 to -0.78). Clinical utility on external validation was improved after recalibration.
The eFalls prediction model shows good performance and could support proactive stratification for falls prevention services if appropriately embedded into primary care EHR systems.
老年人经常跌倒，受伤（如骨折）和对未来跌倒的恐惧会严重影响个人的独立性。目前，用于识别跌倒预防干预对象的方法有限，发表的预测模型存在很高的偏倚风险。我们使用常规收集的初级保健电子健康记录（EHR）开发并外部验证了 eFalls 预测模型，以预测在 1 年内因跌倒或骨折而到急诊科就诊/住院的风险。
数据来自两个独立的、回顾性的 65 岁以上成年人队列：威尔士人群，来自 Secure Anonymised Information Linkage Databank（模型开发）；英格兰布拉德福德和艾尔代尔人群，来自 Connected Bradford（外部验证）。预测因子包括电子虚弱指数成分，并补充了文献综述和临床专业知识得出的变量。使用多变量逻辑回归和最小绝对收缩和选择算子惩罚来构建跌倒/骨折风险模型。通过校准、区分和临床实用性评估预测性能。在整个实践中以及在临床相关亚组中评估了明显的、内部-外部交叉验证和外部验证性能。
模型的判别性能（c 统计量）在内部-外部交叉验证中为 0.72（95%置信区间：0.68 至 0.76），在外部验证中为 0.82（95%置信区间：0.80 至 0.83）。在实践中校准情况各不相同，验证人群中存在一定的过度预测（大校准，-0.87；95%置信区间：-0.96 至 -0.78）。在外部验证中重新校准后提高了临床实用性。
eFalls 预测模型性能良好，如果适当嵌入到初级保健 EHR 系统中，可能会支持积极的跌倒预防服务分层。
