Department of Health Management, Hangzhou Normal University, Hangzhou, China.
Department of Cardiothoracic Surgery, Stanford University, Stanford, CA, United States; Clinical and Translational Research Program, Betty Irene Moore Children's Heart Center, Lucile Packard Children's Hospital, Palo Alto, CA, United States.
Int J Med Inform. 2020 May;137:104105. doi: 10.1016/j.ijmedinf.2020.104105. Epub 2020 Mar 3.
Predicting the risk of falls in advance can benefit the quality of care and potentially reduce mortality and morbidity in the older population. The aim of this study was to construct and validate an electronic health record-based fall risk predictive tool to identify elders at a higher risk of falls.
The one-year fall prediction model was developed using the machine-learning-based algorithm, XGBoost, and tested on an independent validation cohort. The data were collected from electronic health records (EHR) of Maine from 2016 to 2018, comprising 265,225 older patients (≥65 years of age).
This model attained a validated C-statistic of 0.807, where 50 % of the identified high-risk true positives were confirmed to fall during the first 94 days of next year. The model also captured in advance 58.01 % and 54.93 % of falls that happened within the first 30 and 30-60 days of next year. The identified high-risk patients of fall showed conditions of severe disease comorbidities, an enrichment of fall-increasing cardiovascular and mental medication prescriptions and increased historical clinical utilization, revealing the complexity of the underlying fall etiology. The XGBoost algorithm captured 157 impactful predictors into the final predictive model, where cognitive disorders, abnormalities of gait and balance, Parkinson's disease, fall history and osteoporosis were identified as the top-5 strongest predictors of the future fall event.
By using the EHR data, this risk assessment tool attained an improved discriminative ability and can be immediately deployed in the health system to provide automatic early warnings to older adults with increased fall risk and identify their personalized risk factors to facilitate customized fall interventions.
提前预测跌倒风险有助于提高老年人的护理质量，并可能降低死亡率和发病率。本研究旨在构建和验证一种基于电子健康记录的跌倒风险预测工具，以识别跌倒风险较高的老年人。
使用基于机器学习的算法 XGBoost 开发了为期一年的跌倒预测模型，并在独立验证队列中进行了测试。该数据来自 2016 年至 2018 年缅因州的电子健康记录（EHR），包含 265225 名年龄在 65 岁以上的老年人。
该模型的验证 C 统计量为 0.807，其中 50%的识别出的高风险真阳性病例在次年的前 94 天内被证实跌倒。该模型还提前捕捉到了 58.01%和 54.93%的次年 30 天内和 30-60 天内发生的跌倒事件。识别出的高跌倒风险患者存在严重疾病合并症、增加跌倒风险的心血管和精神药物处方以及增加的历史临床利用情况，揭示了潜在跌倒病因的复杂性。XGBoost 算法将 157 个有影响的预测因子纳入最终预测模型，其中认知障碍、步态和平衡异常、帕金森病、跌倒史和骨质疏松症被确定为未来跌倒事件的前 5 个最强预测因子。
通过使用 EHR 数据，该风险评估工具提高了区分能力，可以立即在医疗系统中部署，为跌倒风险增加的老年人提供自动预警，并识别他们的个性化风险因素，以促进定制化的跌倒干预措施。
