Emergency admissions (EA), where a patient requires urgent in-hospital care, are a major challenge for healthcare systems. The development of risk prediction models can partly alleviate this problem by supporting primary care interventions and public health planning. Here, we introduce SPARRAv4, a predictive score for EA risk that will be deployed nationwide in Scotland. SPARRAv4 was derived using supervised and unsupervised machine-learning methods applied to routinely collected electronic health records from approximately 4.8M Scottish residents (2013-18). We demonstrate improvements in discrimination and calibration with respect to previous scores deployed in Scotland, as well as stability over a 3-year timeframe. Our analysis also provides insights about the epidemiology of EA risk in Scotland, by studying predictive performance across different population sub-groups and reasons for admission, as well as by quantifying the effect of individual input features. Finally, we discuss broader challenges including reproducibility and how to safely update risk prediction models that are already deployed at population level.

急诊入院 （EA） 是指患者需要紧急住院护理，是医疗保健系统面临的主要挑战。风险预测模型的开发可以通过支持初级保健干预和公共卫生规划来部分缓解这个问题。在这里，我们介绍了 SPARRAv4，这是一种 EA 风险的预测评分，将在苏格兰全国范围内部署。SPARRAv4 是使用有监督和无监督的机器学习方法得出的，该方法应用于从大约 4.8M 苏格兰居民 （2013-18） 的常规收集电子健康记录。我们证明了与苏格兰之前部署的分数相比，鉴别和校准方面的改进，以及 3 年时间范围内的稳定性。我们的分析还通过研究不同人群亚组和入院原因的预测表现，以及量化个体输入特征的影响，提供了有关苏格兰 EA 风险流行病学的见解。最后，我们讨论了更广泛的挑战，包括可重复性以及如何安全地更新已经在人群层面部署的风险预测模型。