Risk prediction models are statistical models that estimate the probability of individuals having a certain disease or clinical outcome based on a range of characteristics, and they can be used in clinical practice to stratify disease severity and characterize the risk of disease or disease prognosis. With technological advancements and the proliferation of clinical and biological data, prediction models are increasingly being developed in many areas of nephrology practice. This article guides the reader through the process of creating a prediction model, including (i) defining the clinical question and type of model, (ii) data collection and data cleaning, (iii) model building and variable selection, (iv) model performance, (v) model validation, (vi) model presentation and reporting, and (vii) impact evaluation. An example of developing a prediction model to predict mortality after intensive care unit admission for patients with end-stage kidney disease is also provided to illustrate the model development process.

中文翻译：

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预测建模-第1部分：回归建模。

风险预测模型是统计模型，可以基于一系列特征来估计个体患有某种疾病或临床结果的可能性，并且可以在临床实践中用于对疾病严重程度进行分层并表征疾病或疾病预后的风险。随着技术的进步以及临床和生物学数据的激增，在肾脏病实践的许多领域中，预测模型的开发越来越多。本文将指导读者完成创建预测模型的过程，包括（i）定义临床问题和模型类型，（ii）数据收集和数据清理，（iii）模型构建和变量选择，（iv）模型性能，（v）模型验证，（vi）模型展示和报告以及（vii）影响评估。