Woo Jean, Leung Jason, Wong Samuel, Kwok Timothy, Lee Jenny, Lynn Henry
Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China.
J Clin Nurs. 2009 Apr;18(7):1038-48. doi: 10.1111/j.1365-2702.2008.02591.x. Epub 2009 Jan 8.
We documented the number of falls and falls risk profile over two years to derive a falls risks prediction score.
Simple falls risk assessment tools not requiring equipment or trained personnel may be used as a first step in the primary care setting to identify older people at risk who may be referred for further falls risk assessment in special clinics.
Survey.
Men (n = 1941) and 1949 women aged 65 years and over living in the community were followed up for two years to document the number of falls. Information was collected regarding demography, socioeconomic status, medical history, functional limitations, lifestyle factors and psychosocial functioning. Measurements include body mass index, grip strength and stride length. Logistic regression was used to determine significant predictions of falls and to calculate predictive scores.
Twelve factors in men and nine factors in women were used to construct a risk score. The AUC of the receiver operating characteristic curve was >0.70 for both men and women and a cut off score of >or=8 gave sensitivity and specificity values between 60-78%. The factors included chronic disease, drugs, functional limitation, lifestyle, education and psychosocial factors. When applied to future predictions, only low energy level and clumsiness in both hands in men and feeling downhearted in women, were significant factors.
A risk assessment tool with a cut off score of >or=8 developed from a two-year prospective study of falls may be used in the community setting as an initial first step for screening out those at low risk of falls.
A simple tool may be used in the community to screen out those at risk for falls, concentrating trained healthcare professionals' time on detailed falls assessment and intervention for those classified as being at risk.
我们记录了两年内的跌倒次数及跌倒风险概况，以得出跌倒风险预测评分。
在初级保健环境中，可将无需设备或专业人员的简单跌倒风险评估工具作为第一步，用于识别有风险的老年人，这些老年人可被转介至专科诊所进行进一步的跌倒风险评估。
调查。
对社区中1941名65岁及以上男性和1949名65岁及以上女性进行了为期两年的随访，记录跌倒次数。收集了有关人口统计学、社会经济状况、病史、功能受限、生活方式因素和心理社会功能的信息。测量包括体重指数、握力和步长。采用逻辑回归确定跌倒的显著预测因素并计算预测评分。
男性的12个因素和女性的9个因素被用于构建风险评分。男性和女性的受试者工作特征曲线下面积均>0.70，截断分数≥8时，灵敏度和特异度值在60%-78%之间。这些因素包括慢性病、药物、功能受限、生活方式、教育和心理社会因素。在应用于未来预测时，男性的低能量水平和双手笨拙以及女性的情绪低落是显著因素。
通过对跌倒进行为期两年的前瞻性研究开发的截断分数≥8的风险评估工具，可在社区环境中用作初步筛选低跌倒风险人群的第一步。
社区中可使用一种简单工具筛选出跌倒风险人群，使训练有素的医疗保健专业人员能够将时间集中于对被归类为有风险人群的详细跌倒评估和干预。
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