Saito Chihiro, Nakatani Eiji, Sasaki Hatoko, E Katsuki Naoko, Tago Masaki, Harada Kiyoshi
Department of Nursing, Shizuoka General Hospital, Shizuoka, Japan.
Graduate School of Public Health, Shizuoka Graduate University of Public Health, 4-27-2, Kita-ando, Aoi-ku, Shizuoka, 420-0881, Japan, 81 54-295-5400, 81 54-248-3520.
JMIR Hum Factors. 2025 Jan 13;12:e58073. doi: 10.2196/58073.
Falls in hospitalized patients are a serious problem, resulting in physical injury, secondary complications, impaired activities of daily living, prolonged hospital stays, and increased medical costs. Establishing a fall prediction scoring system to identify patients most likely to fall can help prevent falls among hospitalized patients.
This study aimed to identify predictive factors of falls in acute care hospital patients, develop a scoring system, and evaluate its validity.
This single-center, retrospective cohort study involved patients aged 20 years or older admitted to Shizuoka General Hospital between April 2019 and September 2020. Demographic data, candidate predictors at admission, and fall occurrence reports were collected from medical records. The outcome was the time from admission to a fall requiring medical resources. Two-thirds of cases were randomly selected as the training set for analysis, and univariable and multivariable Cox regression analyses were used to identify factors affecting fall risk. We scored the fall risk based on the estimated hazard ratios (HRs) and constructed a fall prediction scoring system. The remaining one-third of cases was used as the test set to evaluate the predictive performance of the new scoring system.
A total of 13,725 individuals were included. During the study period, 2.4% (326/13,725) of patients experienced a fall. In the training dataset (n=9150), Cox regression analysis identified sex (male: HR 1.60, 95% CI 1.21-2.13), age (65 to <80 years: HR 2.26, 95% CI 1.48-3.44; ≥80 years: HR 2.50, 95% CI 1.60-3.92 vs 20-<65 years), BMI (18.5 to <25 kg/m²: HR 1.36, 95% CI 0.94-1.97; <18.5 kg/m²: HR 1.57, 95% CI 1.01-2.44 vs ≥25 kg/m²), independence degree of daily living for older adults with disabilities (bedriddenness rank A: HR 1.81, 95% CI 1.26-2.60; rank B: HR 2.03, 95% CI 1.31-3.14; rank C: HR 1.23, 95% CI 0.83-1.83 vs rank J), department (internal medicine: HR 1.23, 95% CI 0.92-1.64; emergency department: HR 1.81, 95% CI 1.26-2.60 vs department of surgery), and history of falls within 1 year (yes: HR 1.66, 95% CI 1.21-2.27) as predictors of falls. Using these factors, we developed a fall prediction scoring system categorizing patients into 3 risk groups: low risk (0-4 points), intermediate risk (5-9 points), and high risk (10-15 points). The c-index indicating predictive performance in the test set (n=4575) was 0.733 (95% CI 0.684-0.782).
We developed a new fall prediction scoring system for patients admitted to acute care hospitals by identifying predictors of falls in Japan. This system may be useful for preventive interventions in patient populations with a high likelihood of falling in acute care settings.
住院患者跌倒问题严重,会导致身体损伤、继发并发症、日常生活活动受限、住院时间延长及医疗费用增加。建立跌倒预测评分系统以识别最易跌倒的患者,有助于预防住院患者跌倒。
本研究旨在确定急性护理医院患者跌倒的预测因素,开发评分系统并评估其有效性。
本单中心回顾性队列研究纳入了2019年4月至2020年9月期间入住静冈县综合医院的20岁及以上患者。从病历中收集人口统计学数据、入院时的候选预测因素及跌倒发生报告。结局指标为从入院到需要医疗资源处理的跌倒发生时间。随机选取三分之二的病例作为分析的训练集,采用单变量和多变量Cox回归分析确定影响跌倒风险的因素。我们根据估计的风险比(HR)对跌倒风险进行评分,并构建了跌倒预测评分系统。其余三分之一的病例用作测试集,以评估新评分系统的预测性能。
共纳入13725例个体。研究期间,2.4%(326/13725)的患者发生了跌倒。在训练数据集(n = 9150)中,Cox回归分析确定性别(男性:HR 1.60,95%CI 1.21 - 2.13)、年龄(65至<80岁:HR 2.26,95%CI 1.48 - 3.44;≥80岁:HR 2.50,95%CI 1.60 - 3.92 vs 20 - <65岁)、体重指数(BMI)(18.5至<25 kg/m²:HR 1.36,95%CI 0.94 - 1.97;<18.5 kg/m²:HR 1.57,95%CI 1.01 - 2.44 vs ≥25 kg/m²)、残疾老年人的日常生活独立程度(卧床等级A:HR 1.81,95%CI 1.26 - 2.60;等级B:HR 2.03,95%CI 1.31 - 3.14;等级C:HR 1.23,95%CI 0.83 - 1.83 vs 等级J)、科室(内科:HR 1.23,95%CI 0.92 - 1.64;急诊科:HR 1.81,95%CI 1.26 - 2.60 vs 外科)以及1年内跌倒史(是:HR 1.66,95%CI 1.21 - 2.27)为跌倒的预测因素。利用这些因素,我们开发了一个跌倒预测评分系统,将患者分为3个风险组:低风险(0 - 4分)、中度风险(5 - 9分)和高风险(10 - 15分)。测试集(n = 4575)中表示预测性能的c指数为0.733(95%CI 0.684 - 0.782)。
我们通过识别日本急性护理医院患者跌倒的预测因素,开发了一种新的跌倒预测评分系统。该系统可能有助于对急性护理环境中跌倒可能性高的患者群体进行预防性干预。