87例住院肿瘤患者跌倒时间特征性分析

Time Distribution Characteristic of Falling in Hospitalized Cancer Patients

目的探讨住院肿瘤患者跌倒事件的特征，为针对性预防和减少跌倒事件，保障患者安全提供依据。方法利用圆形分布法．对87例住院肿瘤患者的跌倒时间进行回顾性分析，把跌倒发生时间转换为角度，通过计算平均角并进行显著性检验．分析跌倒事件时间分布的集中趋势和离散程度。结果跌倒事件的日期相对集中发生于8月3日；跌倒事件在一日中的时点分布集中在3：45—9：50，围如厕期相关跌倒事件发生的平均角为37．5°，相当于2：32，95％CI在23：12—5：48（P=0．056），非如厕期相关跌倒事件发生51例，平均角为137．2°，相当于9：09，95％Cl在6：56—11：20（P=0．007）；伤害Ⅱ级～Ⅲ级平均角为126．6°，相当于8：28（P=0．005）。集中趋势有统计学意义。结论圆形分布法可用于跌倒事件昼时性资料的分析，夜间及围如厕期是住院肿瘤患者跌倒的高危时段，应针对患者的年龄、性别、病情及活动规律和特点，加强住院肿瘤患者的安全管理。

Objective To explore the time distribution characteristic of falling in hospitalized cancer patients and to prevent and reduce falling. Methods The data of 87 cases of hospitalized cancer patients were retrospectively analyzed by circular distribution method. The falling time was converted into angle then significance test was carried out by calculating the average angle and analysis of central tendency and discrete degree. Results August 3rd witnessed more falling, which showed no statistical significance by Rayleigh＇s test and in the daytime failing majorly occurred from 3：45 am to 9：50 am. The average angle of central tendency had statistical significance by Rayleigh＇s test. The average angular correlation of falling in peri toilet period was 37.5, relative to the 2：32 am and 95% of the confidence interval ranged from 23：12 pm to 5：48 am （/）=0.056）. Fifty-one cases of failing occurred in non peri toilet period and the average angle was 137.2, relative to the 9：09 am , 6：56 to 11：20 am in the 95% of the confidence interval （P= 0.007）; average angle of level II - III hurt was 126.6, relative to the 8：28am, and concentration trend indicated statistical significance （P=0.005）. Conclusion The circular distribution method can be used for the analysis of cancer patients＇ diurnal data of the falling. Male, aged and patients from internal medicine are high risk population of falling during hospitalization and night period and peri toilet period are high-risk period of falling. Safety management should be strengthened based on patients＇ features.