Department of Medical Informatics, Amsterdam UMC location University of Amsterdam, Amsterdam, the Netherlands; Amsterdam Public Health Research Institute, Amsterdam, the Netherlands.
Department of Medical Informatics, Amsterdam UMC location University of Amsterdam, Amsterdam, the Netherlands; Amsterdam Public Health Research Institute, Amsterdam, the Netherlands.
J Am Med Dir Assoc. 2023 Jul;24(7):964-970.e5. doi: 10.1016/j.jamda.2023.03.006. Epub 2023 Apr 12.
Fall prevention is important in many hospitals. Current fall-risk-screening tools have limited predictive accuracy specifically for older inpatients. Their administration can be time-consuming. A reliable and easy-to-administer tool is desirable to identify older inpatients at higher fall risk. We aimed to develop and internally validate a prognostic prediction model for inpatient falls for older patients.
Retrospective analysis of a large cohort drawn from hospital electronic health record data.
Older patients (≥70 years) admitted to a university medical center (2016 until 2021).
The outcome was an inpatient fall (≥24 hours of admission). Two prediction models were developed using regularized logistic regression in 5 imputed data sets: one model without predictors indicating missing values (Model-without) and one model with these additional predictors indicating missing values (Model-with). We internally validated our whole model development strategy using 10-fold stratified cross-validation. The models were evaluated using discrimination (area under the receiver operating characteristic curve) and calibration (plot assessment). We determined whether the areas under the receiver operating characteristic curves (AUCs) of the models were significantly different using DeLong test.
Our data set included 21,286 admissions. In total, 470 (2.2%) had a fall after 24 hours of admission. The Model-without had 12 predictors and Model-with 13, of which 4 were indicators of missing values. The AUCs of the Model-without and Model-with were 0.676 (95% CI 0.646-0.707) and 0.695 (95% CI 0.667-0.724). The AUCs between both models were significantly different (P = .013). Calibration was good for both models.
Both the Model-with and Model-without indicators of missing values showed good calibration and fair discrimination, where the Model-with performed better. Our models showed competitive performance to well-established fall-risk-screening tools, and they have the advantage of being based on routinely collected data. This may substantially reduce the burden on nurses, compared with nonautomatic fall-risk-screening tools.
预防跌倒在许多医院都很重要。目前，专门针对老年住院患者的跌倒风险筛查工具的预测准确性有限。它们的管理可能很耗时。需要一种可靠且易于管理的工具来识别高跌倒风险的老年住院患者。我们旨在为老年住院患者开发并内部验证一种用于住院患者跌倒的预后预测模型。
从医院电子健康记录数据中提取的大型队列的回顾性分析。
年龄在 70 岁及以上的入住大学医疗中心的患者（2016 年至 2021 年）。
结果是住院患者跌倒（入院后 24 小时以上）。使用正则逻辑回归在 5 个插补数据集中开发了两个预测模型：一个没有表示缺失值的预测因子的模型（无模型）和一个具有这些额外表示缺失值的预测因子的模型（有模型）。我们使用 10 折分层交叉验证对整个模型开发策略进行了内部验证。使用判别分析（接收者操作特征曲线下的面积）和校准（图评估）评估模型。我们使用 DeLong 检验确定模型的接收者操作特征曲线下面积（AUC）是否存在显著差异。
我们的数据集中包含 21286 例住院患者。共有 470 例（2.2%）在入院 24 小时后发生跌倒。无模型有 12 个预测因子，有模型有 13 个，其中 4 个是缺失值的指标。无模型和有模型的 AUC 分别为 0.676（95% CI 0.646-0.707）和 0.695（95% CI 0.667-0.724）。两个模型之间的 AUC 有显著差异（P =.013）。两个模型的校准都很好。
无模型和有模型的缺失值指标都表现出良好的校准和适度的判别能力，其中有模型表现更好。我们的模型与成熟的跌倒风险筛查工具相比表现出有竞争力的性能，并且它们具有基于常规收集数据的优势。与非自动跌倒风险筛查工具相比，这可能会大大减轻护士的负担。
