Papaioannou Alexandra, Parkinson William, Cook Richard, Ferko Nicole, Coker Esther, Adachi Jonathan D
Department of Medicine, McMaster University, Hamilton, Ontario, Canada.
BMC Med. 2004 Jan 21;2:1. doi: 10.1186/1741-7015-2-1.
The British STRATIFY tool was previously developed to predict falls in hospital. Although the tool has several strengths, certain limitations exist which may not allow generalizability to a Canadian setting. Thus, we tested the STRATIFY tool with some modification and re-weighting of items in Canadian hospitals.
This was a prospective validation cohort study in four acute care medical units of two teaching hospitals in Hamilton, Ontario. In total, 620 patients over the age of 65 years admitted during a 6-month period. Five patient characteristics found to be risk factors for falls in the British STRATIFY study were tested for predictive validity. The characteristics included history of falls, mental impairment, visual impairment, toileting, and dependency in transfers and mobility. Multivariate logistic regression was used to obtain optimal weights for the construction of a risk score. A receiver-operating characteristic curve was generated to show sensitivities and specificities for predicting falls based on different threshold scores for considering patients at high risk.
Inter-rater reliability for the weighted risk score indicated very good agreement (inter-class correlation coefficient = 0.78). History of falls, mental impairment, toileting difficulties, and dependency in transfer / mobility significantly predicted fallers. In the multivariate model, mental status was a significant predictor (P < 0.001) while history of falls and transfer / mobility difficulties approached significance (P = 0.089 and P = 0.077 respectively). The logistic regression model led to weights for a risk score on a 30-point scale. A risk score of 9 or more gave a sensitivity of 91% and specificity of 60% for predicting who would fall.
Good predictive validity for identifying fallers was achieved in a Canadian setting using a simple-to-obtain risk score that can easily be incorporated into practice.
英国的STRATIFY工具先前已开发用于预测医院内的跌倒情况。尽管该工具具有若干优点，但也存在某些局限性，可能无法推广至加拿大的情况。因此，我们在加拿大医院对STRATIFY工具进行了一些修改并重新权衡了各项因素后进行了测试。
这是一项在安大略省汉密尔顿市两家教学医院的四个急性医疗科室进行的前瞻性验证队列研究。在6个月期间，共纳入了620名65岁以上的患者。对英国STRATIFY研究中发现的五个跌倒风险因素患者特征进行了预测有效性测试。这些特征包括跌倒史、精神障碍、视力障碍、如厕情况以及转移和活动时的依赖性。使用多变量逻辑回归来获得构建风险评分的最佳权重。生成了一条受试者工作特征曲线，以显示基于不同阈值分数预测跌倒的敏感性和特异性，这些阈值分数用于确定高危患者。
加权风险评分的评分者间信度显示出非常好的一致性（组内相关系数 = 0.78）。跌倒史、精神障碍、如厕困难以及转移/活动时的依赖性显著预测了跌倒者。在多变量模型中，精神状态是一个显著的预测因素（P < 0.001），而跌倒史和转移/活动困难接近显著水平（分别为P = 0.089和P = 0.077）。逻辑回归模型得出了一个30分制风险评分的权重。风险评分为9分或更高时，预测谁会跌倒的敏感性为91%，特异性为60%。
在加拿大环境中，使用一个易于获取且可轻松纳入实践的风险评分，在识别跌倒者方面取得了良好的预测有效性。
