Background

Emergency departments (ED) are a portal of entry into the hospital and are uniquely positioned to influence the health care trajectories of older adults seeking medical attention. Older adults present to the ED with distinct needs and complex medical histories, which can make disposition planning more challenging. Machine Learning (ML) approaches have been previously used to inform decision-making surrounding ED disposition in the general population. However, little is known about the performance and utility of ML methods in predicting hospital admission among older ED patients. We applied a series of ML algorithms to predict ED admission among older adults and discuss their clinical and policy implications.

Materials and Methods

We analyzed the Canadian data from the interRAI multinational ED study, the largest prospective cohort study of older ED patients to date. The data included 2,274 ED patients 75 years of age and older from eight ED sites across Canada between November 2009 and April 2012. Data were extracted from the interRAI ED Contact Assessment, with predictors including a series of geriatric syndromes, functional assessments, and baseline care needs. We applied a total of five ML algorithms. Models were trained, assessed, and analyzed using 10-fold cross-validation. The performance of predictive models was measured using the area under the receiver operating characteristic curve (AUC). We also report the accuracy, sensitivity, and specificity of each model to supplement performance interpretation.

Results

Gradient boosted trees was the most accurate model to predict older ED patients who would require hospitalization (AUC = 0.80). The five most informative features include home intravenous therapy, time of ED presentation, a requirement for formal support services, independence in walking, and the presence of an unstable medical condition.

Conclusion

To the best of our knowledge, this is the first study to predict hospital admission in older ED patients using a series of geriatric syndromes and functional assessments. We were able to predict hospital admission in older ED patients with good accuracy using the items available in the interRAI ED Contact Assessment. This information can be used to inform decision-making about ED disposition and may expedite admission processes and proactive discharge planning.

中文翻译：

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预测老年急诊科患者的入院率：机器学习的见解。

背景

急诊科（ED）是进入医院的门户，并且位置独特，可以影响寻求医疗护理的老年人的医疗保健轨迹。老年人向急诊科提出独特的需求和复杂的病史，这可能会使处置计划变得更具挑战性。机器学习（ML）方法先前已被用来指导有关普通人群中ED配置的决策。然而，关于ML方法在老年ED患者中预测住院率的性能和效用知之甚少。我们应用了一系列ML算法来预测老年人的ED入院率，并讨论了其临床和政策意义。

材料和方法

我们分析了interRAI跨国ED研究的加拿大数据，这是迄今为止最大的老年ED患者前瞻性队列研究。数据包括2009年11月至2012年4月期间来自加拿大8个ED地点的2274名75岁及以上的ED患者。数据摘自interRAI ED接触评估，预测因子包括一系列老年综合征，功能评估和基线护理需要。我们总共应用了五种ML算法。使用10倍交叉验证对模型进行训练，评估和分析。预测模型的性能是使用接收器工作特性曲线（AUC）下的面积测量的。我们还报告了每种模型的准确性，敏感性和特异性，以补充对性能的解释。

结果

梯度增强树是预测需要住院的老年ED患者的最准确模型（AUC = 0.80）。五个最有用的功能包括家庭静脉注射治疗，ED的出诊时间，对正式支持服务的要求，行走的独立性以及不稳定的医疗状况。

结论

据我们所知，这是第一项使用一系列老年综合征和功能评估来预测老年ED患者入院的研究。我们能够使用interRAI ED接触评估中提供的项目，以较高的准确度预测老年ED患者的入院率。该信息可用于为有关ED处理的决策提供信息，并可加快入院流程和积极的出院计划。