Tamura Shuntaro, Kobayashi Makoto, Saito Yasuyuki, Asakura Tomoyuki, Usuda Shigeru
Department of Rehabilitation, Fujioka General Hospital: 813-1 Nakakurisu, Fujioka, Gunma 375-8503, Japan.
Department of Basic Rehabilitation, Gunma University School of Health Sciences, Japan.
J Phys Ther Sci. 2020 Nov;32(11):722-728. doi: 10.1589/jpts.32.722. Epub 2020 Nov 11.
[Purpose] To present an accurate and straight-forward system of fall prediction by performing decision tree analysis using both the fall assessment sheet and Berg balance scale (BBS). [Participants and Methods] The participants in this retrospective study were inpatients from acute care units. We extracted the risk factors for falls from the fall assessment and performed a decision tree analysis using the extracted fall risk factors and BBS score. [Results] "History of more than one fall in the last 1 year", "Muscle weakness", "Use of a walking aid or wheelchair", "Requires assistance for transfer", "Use of Narcotics", "Dangerous behavior", and "High degree of self-reliance" were fall risk factors. The decision tree analysis extracted five fall risk factors, with an area under the curve of 0.7919. Patients with no history of falls and who did not require assistance for transfer or those with a BBS score ≥51 did not fall. [Conclusion] Decision tree-based fall prediction was useful and straightforward and revealed that patients with no history of falling and those who did not require assistance for transfer or had a BBS score ≥51 had a low risk of falling.
[目的] 通过使用跌倒评估表和伯格平衡量表(BBS)进行决策树分析,提出一种准确且直接的跌倒预测系统。[参与者与方法] 本回顾性研究的参与者为急性护理病房的住院患者。我们从跌倒评估中提取跌倒风险因素,并使用提取的跌倒风险因素和BBS评分进行决策树分析。[结果] “过去1年中跌倒次数超过1次”、“肌肉无力”、“使用助行器或轮椅”、“转移需要协助”、“使用麻醉药品”、“危险行为”以及“高度自理”是跌倒风险因素。决策树分析提取了5个跌倒风险因素,曲线下面积为0.7919。无跌倒史且转移不需要协助的患者或BBS评分≥51的患者未发生跌倒。[结论] 基于决策树的跌倒预测实用且直接,表明无跌倒史、转移不需要协助或BBS评分≥51的患者跌倒风险较低。