School of Nursing, Fudan University, Shanghai, People's Republic of China.
Department of Nursing, Huashan Hospital, Fudan University, Shanghai, People's Republic of China.
Clin Interv Aging. 2020 Sep 24;15:1767-1778. doi: 10.2147/CIA.S258171. eCollection 2020.
Injurious falls seriously threaten the safety of elderly patients. Identifying risk factors for predicting the probability of injurious falls is an important issue that still needs to be solved urgently. We aimed to identify predictors and develop a nomogram for distinguishing populations at high risk of injurious falls from older adults in acute settings.
A retrospective case-control study was conducted at three hospitals in Shanghai, China. Elderly patients with injurious falls from January 2014 to December 2018 were taken as cases, and control patients who did not have falls were randomly matched based on the admission date and the department. The data were collected through a medical record review and adverse events system. The original data set was randomly divided into a training set and a validation set at a 7:3 ratio. A nomogram was established based on the results of the univariate analysis and multivariate logistic regression analysis, and its discrimination and calibration were verified to confirm the accuracy of the prediction. The cut-off value of risk stratification was determined to help medical staff identify the high-risk groups.
A total of 115 elderly patients with injurious falls and 230 controls were identified. History of fractures, orthostatic hypotension, functional status, sedative-hypnotics and level of serum albumin were independent risk factors for injurious falls in elderly patients. The C-indexes of the training and validation sets were 0.874 (95% CI: 0.784-0.964) and 0.847 (95% CI: 0.771-0.924), respectively. Calibration curves were drawn and showed acceptable predictive performance. The cut-off values of the training and validation sets were 146.3 points (sensitivity: 73.7%; specificity: 87.5%) and 157.2 points (sensitivity: 69.2%; specificity: 85.5%), respectively.
The established nomogram facilitates the identification of high-risk populations among elderly patients, providing a new assessment tool to forecast the individual risk of injurious falls.
伤害性跌倒严重威胁老年患者的安全。确定预测伤害性跌倒概率的风险因素是一个亟待解决的重要问题。我们旨在确定预测指标，并为区分急性环境中高伤害性跌倒风险人群和老年患者开发一个列线图。
这是一项在中国上海的三家医院进行的回顾性病例对照研究。将 2014 年 1 月至 2018 年 12 月期间发生伤害性跌倒的老年患者作为病例，根据入院日期和科室随机匹配未发生跌倒的对照患者。通过病历回顾和不良事件系统收集数据。原始数据集按 7:3 的比例随机分为训练集和验证集。基于单变量分析和多变量逻辑回归分析的结果建立了一个列线图，并验证了其区分度和校准度，以确认预测的准确性。确定风险分层的临界值有助于医务人员识别高危人群。
共确定了 115 例发生伤害性跌倒的老年患者和 230 例对照。骨折史、直立性低血压、功能状态、镇静催眠药和血清白蛋白水平是老年患者发生伤害性跌倒的独立危险因素。训练集和验证集的 C 指数分别为 0.874（95%CI：0.784-0.964）和 0.847（95%CI：0.771-0.924）。绘制了校准曲线，显示出可接受的预测性能。训练集和验证集的截断值分别为 146.3 分（敏感性：73.7%；特异性：87.5%）和 157.2 分（敏感性：69.2%；特异性：85.5%）。
所建立的列线图有助于识别老年患者中的高危人群，为预测伤害性跌倒的个体风险提供了一种新的评估工具。
