World Health Organization fracture risk assessment tool in the assessment of fractures after falls in hospital
World Health Organization fracture risk assessment tool in the assessment of fractures after falls in hospital
复制标题
DOI：
10.1186/1472-6963-10-106
复制
发表时间：
2010-04-27
影响因子：
2.8
通讯作者：
Toyabe, Shin-ichi
中科院分区：
文献类型：
--
作者：
Toyabe, Shin-ichi
背景：福尔斯是医院常见的意外事故。各种风险因素和风险评估工具用于预测福尔斯。然而，这些风险评估工具并未考虑福尔斯的结局，如骨折，并且风险评估工具在日本医院环境中的性能尚不清楚。方法：这是一项回顾性单机构研究，纳入了2006年4月至2009年3月期间在三级护理大学医院住院的20，320例年龄在40至90岁之间的住院患者。福尔斯和骨折的可能风险因素包括STRATIFY评分和FRAX（TM）评分，以及从医院信息系统获得的关于福尔斯及其结局的信息。数据集被随机分为开发数据集和测试数据集。结果：发育数据集和测试数据集的跌倒者分别占3.1%和3.5%，其中发生周围骨折的分别占2.6%和2.9%。STRATIFY评分预测福尔斯跌倒的敏感性和特异性并不理想。大多数已知的福尔斯跌倒的危险因素都不能预测福尔斯跌倒后骨折。多元Logistic分析和多变量考克斯回归分析与时间相关的协变量显示，FRAX（TM）评分与骨折后福尔斯。结论：风险评估工具福尔斯是不适合预测骨折后福尔斯。FRAX（TM）可能是实现这一目的的有用工具。在日本医院环境中，STRATIFY预测福尔斯的性能与先前研究相似。
Background: Falls are very common accidents in a hospital. Various risk factors and risk assessment tools are used to predict falls. However, outcomes of falls such as bone fractures have not been considered in these risk assessment tools, and the performance of risk assessment tools in a Japanese hospital setting is not clear.Methods: This was a retrospective single-institution study of 20,320 inpatients aged from 40 to 90 years who were admitted to a tertiary-care university hospital during the period from April 2006 to March 2009. Possible risk factors for falls and fractures including STRATIFY score and FRAX (TM) score and information on falls and their outcome were obtained from the hospital information system. The datasets were divided randomly into a development dataset and a test dataset. The chi-square test, logistic regression analysis and survival analysis were used to identify risk factors for falls and fractures after falls.Results: Fallers accounted for 3.1% of the patients in the development dataset and 3.5% of the patients in the test dataset, and 2.6% and 2.9% of the fallers in those datasets suffered peripheral fractures. Sensitivity and specificity of the STRATIFY score to predict falls were not optimal. Most of the known risk factors for falls had no power to predict fractures after falls. Multiple logistic analysis and multivariate Cox's regression analysis with time-dependent covariates revealed that FRAX (TM) score was significantly associated with fractures after falls.Conclusions: Risk assessment tools for falls are not appropriate for predicting fractures after falls. FRAX (TM) might be a useful tool for that purpose. The performance of STRATIFY to predict falls in a Japanese hospital setting was similar to that in previous studies.