The risk prediction model for kidney failure and death in people with chronic kidney disease (CKD) presented in the linked study is a super learner. A super learner is an algorithm that repeatedly splits the data into training and test sets and then chooses the best performing model from a list of candidate prediction models. This article describes why and how the super learner was implemented in the linked study. ### What is a medical risk prediction model? A medical risk prediction model reads the data of a patient and returns their predicted risks.12 Generally speaking, the model makes predictions by referring to what happened to similar patients in the past, as recorded in a learning dataset. For example, a model could predict for a new patient with CKD that within two years from now their risk of kidney failure is 8% and their risk of death is 13%. These predictions are interpretable as follows: out of 100 people who today are all like this patient, eight are expected to develop kidney failure and 13 are expected to die within the next two years. Notice that patients who first develop kidney failure and then die contribute to both outcomes. Whenever competing events prevent the outcome of interest, in our case death, medical decision making needs to account for the predicted risks of all events. ### Risk prediction framework Creating a medical risk prediction model based on electronic health records is challenging. A sound framework includes the definition of a clinically meaningful time zero, which is called prediction time origin, and one or multiple prediction time horizons.2 Subsequently, the availability of predictor information at the time origin (patient age, sex, albuminuria, etc) should be verified. Inclusion of predictor variables that are accessible in a timely fashion and without great additional costs enhances model usability. For example, in the linked study …

中文翻译：

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预测患有中度至重度慢性肾脏病的成人的肾衰竭和死亡风险

关联研究中提出的慢性肾病 (CKD) 患者肾衰竭和死亡的风险预测模型是一个超级学习器。超级学习器是一种算法，它反复将数据分为训练集和测试集，然后从候选预测模型列表中选择性能最佳的模型。本文描述了为什么以及如何在链接研究中实现超级学习器。 ### 什么是医疗风险预测模型？医疗风险预测模型读取患者的数据并返回其预测风险。12 一般来说，该模型通过参考学习数据集中记录的过去类似患者发生的情况来进行预测。例如，模型可以预测新的 CKD 患者在两年内发生肾衰竭的风险为 8%，死亡风险为 13%。这些预测可以解释如下：在今天的 100 名患者中，预计有 8 人会出现肾衰竭，13 人预计会在未来两年内死亡。请注意，首先出现肾衰竭然后死亡的患者会导致这两种结果。每当竞争事件阻止了感兴趣的结果（在我们的死亡案例中）时，医疗决策就需要考虑所有事件的预测风险。 ### 风险预测框架 创建基于电子健康记录的医疗风险预测模型具有挑战性。健全的框架包括定义具有临床意义的零时间（称为预测时间原点）和一个或多个预测时间范围。2 随后，时间原点处预测信息的可用性（患者年龄、性别、蛋白尿等）应予以核实。包含可及时访问且无需大量额外成本的预测变量可以增强模型的可用性。例如，在相关研究中……