长期护理机构入住后骨折风险。

Registry of Senior Australians, South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia; UniSA Clinical & Health Sciences, University of South Australia, Adelaide, South Australia, Australia; UniSA Allied Health and Human Performance, University of South Australia, Adelaide, South Australia, Australia.

Registry of Senior Australians, South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia; UniSA Allied Health and Human Performance, University of South Australia, Adelaide, South Australia, Australia; Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia.

Bone. 2024 Mar;180:116995. doi: 10.1016/j.bone.2023.116995. Epub 2023 Dec 23.

BACKGROUND

Stratifying residents at increased risk for fractures in long-term care facilities (LTCFs) can potentially improve awareness and facilitate the delivery of targeted interventions to reduce risk. Although several fracture risk assessment tools exist, most are not suitable for individuals entering LTCF. Moreover, existing tools do not examine risk profiles of individuals at key periods in their aged care journey, specifically at entry into LTCFs.

PURPOSE

Our objectives were to identify fracture predictors, develop a fracture risk prognostic model for new LTCF residents and compare its performance to the Fracture Risk Assessment in Long term care (FRAiL) model using the Registry of Senior Australians (ROSA) Historical National Cohort, which contains integrated health and aged care information for individuals receiving long term care services.

METHODS

Individuals aged ≥65 years old who entered 2079 facilities in three Australian states between 01/01/2009 and 31/12/2016 were examined. Fractures (any) within 365 days of LTCF entry were the outcome of interest. Individual, medication, health care, facility and system-related factors were examined as predictors. A fracture prognostic model was developed using elastic nets penalised regression and Fine-Gray models. Model discrimination was examined using area under the receiver operating characteristics curve (AUC) from the 20 % testing dataset. Model performance was compared to an existing risk model (i.e., FRAiL model).

RESULTS

Of the 238,782 individuals studied, 62.3 % (N = 148,838) were women, 49.7 % (N = 118,598) had dementia and the median age was 84 (interquartile range 79-89). Within 365 days of LTCF entry, 7.2 % (N = 17,110) of individuals experienced a fracture. The strongest fracture predictors included: complex health care rating (no vs high care needs, sub-distribution hazard ratio (sHR) = 1.52, 95 % confidence interval (CI) 1.39-1.67), nutrition rating (moderate vs worst, sHR = 1.48, 95%CI 1.38-1.59), prior fractures (sHR ranging from 1.24 to 1.41 depending on fracture site/type), one year history of general practitioner attendances (≥16 attendances vs none, sHR = 1.35, 95%CI 1.18-1.54), use of dopa and dopa derivative antiparkinsonian medications (sHR = 1.28, 95%CI 1.19-1.38), history of osteoporosis (sHR = 1.22, 95%CI 1.16-1.27), dementia (sHR = 1.22, 95%CI 1.17-1.28) and falls (sHR = 1.21, 95%CI 1.17-1.25). The model AUC in the testing cohort was 0.62 (95%CI 0.61-0.63) and performed similar to the FRAiL model (AUC = 0.61, 95%CI 0.60-0.62).

CONCLUSIONS

Critical information captured during transition into LTCF can be effectively leveraged to inform fracture risk profiling. New fracture predictors including complex health care needs, recent emergency department encounters, general practitioner and consultant physician attendances, were identified.

背景

在长期护理机构（LTCF）中对骨折风险较高的居民进行分层，有可能提高意识，并有助于提供有针对性的干预措施来降低风险。虽然有几种骨折风险评估工具，但大多数都不适合进入 LTCF 的个人。此外，现有的工具没有检查个人在其老年护理旅程的关键时期的风险概况，特别是在进入 LTCF 时。

目的

我们的目的是确定骨折预测因素，为新进入 LTCF 的居民开发骨折风险预测模型，并使用包含接受长期护理服务的个人的综合健康和老年护理信息的澳大利亚老年人登记处（ROSA）历史国家队列，比较其与 FRAiL 模型的性能，该模型用于长期护理（FRAiL）。

方法

检查了 2009 年 1 月 1 日至 2016 年 12 月 31 日期间进入澳大利亚三个州的 2079 个设施的年龄在 65 岁及以上的个人。在 LTCF 进入后的 365 天内发生的任何骨折是研究的结果。个体、药物、医疗保健、设施和系统相关因素被检查为预测因素。使用弹性网络惩罚回归和 Fine-Gray 模型开发骨折预测模型。使用来自 20%测试数据集的接收器工作特征曲线（ROC）下面积（AUC）检查模型的区分度。将模型性能与现有风险模型（即 FRAiL 模型）进行比较。

结果

在所研究的 238782 个人中，62.3%（N=148838）为女性，49.7%（N=118598）患有痴呆症，中位年龄为 84 岁（四分位距为 79-89）。在进入 LTCF 后的 365 天内，7.2%（N=17110）的个人发生骨折。最强的骨折预测因素包括：复杂的医疗保健评级（无 vs 高护理需求，亚分布危险比（sHR）=1.52，95%置信区间（CI）1.39-1.67），营养评级（中度 vs 最差，sHR=1.48，95%CI 1.38-1.59），既往骨折（取决于骨折部位/类型，sHR 从 1.24 到 1.41 不等），一年来全科医生就诊次数（≥16 次就诊 vs 无就诊，sHR=1.35，95%CI 1.18-1.54），使用多巴和多巴衍生物抗帕金森病药物（sHR=1.28，95%CI 1.19-1.38），骨质疏松史（sHR=1.22，95%CI 1.16-1.27），痴呆症（sHR=1.22，95%CI 1.17-1.28）和跌倒（sHR=1.21，95%CI 1.17-1.25）。测试队列中的模型 AUC 为 0.62（95%CI 0.61-0.63），与 FRAiL 模型（AUC=0.61，95%CI 0.60-0.62）性能相当。