Department of Health Services, Policy, and Practice, Brown University School of Public Health, Providence, Rhode Island, USA.
Department of Pharmacy Practice and Science, University of Kentucky College of Pharmacy, Lexington, Kentucky, USA.
J Am Geriatr Soc. 2023 Jun;71(6):1851-1860. doi: 10.1111/jgs.18277. Epub 2023 Mar 8.
Existing models to predict fall-related injuries (FRI) in nursing homes (NH) focus on hip fractures, yet hip fractures comprise less than half of all FRIs. We developed and validated a series of models to predict the absolute risk of FRIs in NH residents.
Retrospective cohort study of long-stay US NH residents (≥100 days in the same facility) between January 1, 2016 and December 31, 2017 (n = 733,427) using Medicare claims and Minimum Data Set v3.0 clinical assessments. Predictors of FRIs were selected through LASSO logistic regression in a 2/3 random derivation sample and tested in a 1/3 validation sample. Sub-distribution hazard ratios (HR) and 95% confidence intervals (95% CI) were estimated for 6-month and 2-year follow-up. Discrimination was evaluated via C-statistic, and calibration compared the predicted rate of FRI to the observed rate. To develop a parsimonious clinical tool, we calculated a score using the five strongest predictors in the Fine-Gray model. Model performance was repeated in the validation sample.
Mean (Q1, Q3) age was 85.0 (77.5, 90.6) years and 69.6% were women. Within 2 years of follow-up, 43,976 (6.0%) residents experienced ≥1 FRI. Seventy predictors were included in the model. The discrimination of the 2-year prediction model was good (C-index = 0.70), and the calibration was excellent. Calibration and discrimination of the 6-month model were similar (C-index = 0.71). In the clinical tool to predict 2-year risk, the five characteristics included independence in activities of daily living (ADLs) (HR 2.27; 95% CI 2.14-2.41) and a history of non-hip fracture (HR 2.02; 95% CI 1.94-2.12). Performance results were similar in the validation sample.
We developed and validated a series of risk prediction models that can identify NH residents at greatest risk for FRI. In NH, these models should help target preventive strategies.
现有的预测养老院（NH）中与跌倒相关的伤害（FRI）的模型主要集中在髋部骨折上，但髋部骨折不到所有 FRI 的一半。我们开发并验证了一系列预测 NH 居民 FRI 绝对风险的模型。
这是一项回顾性队列研究，使用医疗保险索赔和最低数据集 v3.0 临床评估，对 2016 年 1 月 1 日至 2017 年 12 月 31 日期间（n=733427）在同一机构中居住时间≥100 天的美国 NH 常住居民进行分析。通过 LASSO 逻辑回归在 2/3 的随机推导样本中选择 FRI 的预测因子，并在 1/3 的验证样本中进行测试。对 6 个月和 2 年随访的亚分布风险比（HR）和 95%置信区间（95%CI）进行估计。通过 C 统计量评估区分度，并将预测的 FRI 发生率与观察到的发生率进行比较。为了开发一个简单的临床工具，我们使用 Fine-Gray 模型中的五个最强预测因子计算了一个分数。在验证样本中重复了模型性能。
平均（Q1，Q3）年龄为 85.0（77.5，90.6）岁，69.6%为女性。在随访的 2 年内，43976（6.0%）名居民经历了≥1 次 FRI。该模型共纳入 70 个预测因子。2 年预测模型的区分度较好（C 指数=0.70），校准效果极好。6 个月模型的校准和区分度相似（C 指数=0.71）。在预测 2 年风险的临床工具中，包括日常生活活动（ADL）独立性（HR 2.27；95%CI 2.14-2.41）和非髋部骨折史（HR 2.02；95%CI 1.94-2.12）在内的五个特征。验证样本中的性能结果相似。
我们开发并验证了一系列风险预测模型，这些模型可以识别出 NH 居民中最易发生 FRI 的人群。在 NH 中，这些模型应该有助于确定预防策略的目标人群。
