Objective

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.

Methods

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).

Results

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.

Conclusions

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.

中文翻译：

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利用全州范围的电子健康记录和机器学习算法来识别高跌倒风险的老年人。

目的

提前预测跌倒的风险可以提高护理质量，并有可能降低老年人口的死亡率和发病率。这项研究的目的是构建和验证基于电子健康记录的跌倒风险预测工具，以识别高跌倒风险的老年人。

方法

使用基于机器学习的算法XGBoost开发的一年跌倒预测模型，并在独立的验证队列中进行了测试。数据收集自2016年至2018年缅因州的电子健康记录（EHR），包括265,225名年龄较大的患者（≥65岁）。

结果

该模型的验证C统计量为0.807，其中确认的50％的高风险真实阳性病例在明年的前94天下降。该模型还预先捕获了明年前30天内和30-60天内发生的跌幅的58.01％和54.93％。确定的跌倒高危患者表现出严重的疾病合并症，增加了增加跌倒的心血管和精神药物处方，并增加了历史临床使用率，从而揭示了跌倒病因的复杂性。XGBoost算法将157个有影响力的预测因素捕获到了最终的预测模型中，其中认知障碍，步态和平衡异常，帕金森氏病，跌倒史和骨质疏松症被确定为未来跌倒事件的前5强预测因素。

结论

通过使用EHR数据，该风险评估工具具有更高的判别能力，可以立即部署到卫生系统中，以向坠落风险增加的老年人提供自动预警，并识别其个性化的危险因素，以促进定制的坠落干预措施。