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应用净重新分类指数于非嵌套和基于点的风险预测模型:综述。

Application of net reclassification index to non-nested and point-based risk prediction models: a review.

机构信息

Department of Biostatistics and Bioinformatics, Duke University School of Medicine, 2424 Erwin Road, Suite 1102, Durham, NC, USA.

Duke Clinical Research Institute, Duke University School of Medicine, 2400 Pratt St, 7021 North Pavilion, Durham, NC, USA.

出版信息

Eur Heart J. 2019 Jun 14;40(23):1880-1887. doi: 10.1093/eurheartj/ehy345.

Abstract

Much of medical risk prediction involves externally derived prediction equations, nomograms, and point-based risk scores. These settings are vulnerable to misleading findings of incremental value based on versions of the net reclassification index (NRI) in common use. By applying non-nested models and point-based risk scores in the setting of stroke risk prediction in patients with atrial fibrillation (AF), we demonstrate current recommendations for presentation and interpretation of the NRI. We emphasize pitfalls that are likely to occur with point-based risk scores that are easy to neglect when statistical methodology is focused on continuous models. In order to make appropriate decisions about risk prediction and personalized medicine, physicians, researchers, and policy makers need to understand the strengths and limitations of the NRI.

摘要

许多医学风险预测都涉及外部推导的预测方程、诺模图和基于点的风险评分。这些设置容易受到基于常用净重新分类指数 (NRI) 版本的增量价值的误导性发现的影响。通过在心房颤动 (AF) 患者的中风风险预测中应用非嵌套模型和基于点的风险评分,我们展示了目前推荐的 NRI 的呈现和解释方法。我们强调了基于点的风险评分容易忽略的陷阱,而当统计方法侧重于连续模型时,这些陷阱很容易被忽略。为了对风险预测和个性化医疗做出适当的决策,医生、研究人员和政策制定者需要了解 NRI 的优缺点。

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