Epidemiology and Data Science, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Internal Medicine, Section of Geriatric Medicine, Amsterdam UMC location University of Amsterdam, Amsterdam, the Netherlands; Amsterdam Public Health research institute, Amsterdam, the Netherlands.
Epidemiology and Data Science, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Amsterdam Public Health research institute, Amsterdam, the Netherlands.
J Am Med Dir Assoc. 2023 Dec;24(12):1996-2001. doi: 10.1016/j.jamda.2023.04.021. Epub 2023 May 30.
Before being used in clinical practice, a prediction model should be tested in patients whose data were not used in model development. Previously, we developed the ADFICE_IT models for predicting any fall and recurrent falls, referred as Any_fall and Recur_fall. In this study, we externally validated the models and compared their clinical value to a practical screening strategy where patients are screened for falls history alone.
Retrospective, combined analysis of 2 prospective cohorts.
Data were included of 1125 patients (aged ≥65 years) who visited the geriatrics department or the emergency department.
We evaluated the models' discrimination using the C-statistic. Models were updated using logistic regression if calibration intercept or slope values deviated significantly from their ideal values. Decision curve analysis was applied to compare the models' clinical value (ie, net benefit) against that of falls history for different decision thresholds.
During the 1-year follow-up, 428 participants (42.7%) endured 1 or more falls, and 224 participants (23.1%) endured a recurrent fall (≥2 falls). C-statistic values were 0.66 (95% CI 0.63-0.69) and 0.69 (95% CI 0.65-0.72) for the Any_fall and Recur_fall models, respectively. Any_fall overestimated the fall risk and we therefore updated only its intercept whereas Recur_fall showed good calibration and required no update. Compared with falls history, Any_fall and Recur_fall showed greater net benefit for decision thresholds of 35% to 60% and 15% to 45%, respectively.
The models performed similarly in this data set of geriatric outpatients as in the development sample. This suggests that fall-risk assessment tools that were developed in community-dwelling older adults may perform well in geriatric outpatients. We found that in geriatric outpatients the models have greater clinical value across a wide range of decision thresholds compared with screening for falls history alone.
在将预测模型应用于临床实践之前，应在未用于模型开发的数据的患者中对其进行测试。此前，我们开发了用于预测任何跌倒和复发性跌倒的 ADFICE_IT 模型，分别称为 Any_fall 和 Recur_fall。在这项研究中，我们对模型进行了外部验证，并将其临床价值与仅基于跌倒史进行筛查的实用策略进行了比较。
回顾性，前瞻性队列的合并分析。
纳入了 1125 名（年龄≥65 岁）就诊于老年科或急诊科的患者的数据。
我们使用 C 统计量评估了模型的区分能力。如果校准截距或斜率值与理想值有显著偏差，则使用逻辑回归更新模型。应用决策曲线分析来比较模型的临床价值（即净收益）与不同决策阈值下的跌倒史。
在 1 年的随访期间，428 名参与者（42.7%）发生了 1 次或多次跌倒，224 名参与者（23.1%）发生了复发性跌倒（≥2 次跌倒）。Any_fall 和 Recur_fall 模型的 C 统计量值分别为 0.66（95%CI 0.63-0.69）和 0.69（95%CI 0.65-0.72）。Any_fall 高估了跌倒风险，因此我们仅更新了其截距，而 Recur_fall 显示出良好的校准效果，无需更新。与跌倒史相比，在 35%至 60%和 15%至 45%的决策阈值下，Any_fall 和 Recur_fall 分别具有更大的净收益。
这些模型在该老年科门诊患者数据集的表现与开发样本相似。这表明，在社区居住的老年人中开发的跌倒风险评估工具在老年科门诊患者中可能具有良好的性能。我们发现，在老年科门诊患者中，与单独筛查跌倒史相比，在广泛的决策阈值下，这些模型具有更大的临床价值。
