Over the past several years, multivariate approaches have been developed that address the problem of disease diagnosis. Here, we report an integrated approach to the problem of prognosis that uses protein microarrays to measure a focused set of molecular markers and non-parametric methods to reveal non-linear relationships among these markers, clinical variables, and patient outcome. As proof-of-concept, we applied our approach to the prediction of early mortality in patients initiating kidney dialysis. We found that molecular markers are not uniformly prognostic, but instead vary in their value depending on a combination of clinical variables. This may explain why reports in this area aiming to identify prognostic markers, without taking into account clinical variables, are either conflicting or show that markers have marginal prognostic value. Just as treatments are now being tailored to specific subsets of patients, our results show that prognosis can also benefit from a 'personalized' approach.

译文

在过去的几年中,已经开发了解决疾病诊断问题的多变量方法。在这里,我们报告了一种针对预后问题的综合方法,该方法使用蛋白质微阵列来测量一组集中的分子标记物,以及非参数方法来揭示这些标记物,临床变量和患者预后之间的非线性关系。作为概念验证,我们将我们的方法应用于开始肾透析的患者的早期死亡率的预测。我们发现,分子标志物的预后并不一致,而是根据临床变量的组合而变化。这可以解释为什么在不考虑临床变量的情况下,旨在识别预后标志物的这一领域的报告存在冲突或表明标志物具有边际预后价值。就像现在针对特定的患者子集进行治疗一样,我们的结果表明,“个性化” 方法也可以使预后受益。

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