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首页> 外文期刊>Nature reviews. Nephrology >Assessing risk in chronic kidney disease: A methodological review
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Assessing risk in chronic kidney disease: A methodological review

机译：慢性肾脏病风险评估：方法学综述

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Chronic kidney disease (CKD) is an increasingly common public health issue associated with substantial morbidity and mortality. Risk prediction models provide a useful clinical and research framework for forecasting the probability of adverse events and stratifying patients with CKD according to risk; however, accurate absolute risk prediction requires careful model specification. Competing events that preclude the event of interest (for example, death in studies of end-stage renal disease) must be taken into account. Functional forms of predictor variables and underlying effect modification must be accurately specified; nonlinearity and possible interactions should be evaluated. The potential effect of measurement error should also be considered. Misspecification of any of these components can dramatically affect absolute risk prediction. Evaluation of prognostic models should encompass not only traditional tests of calibration and discrimination, such as the Hosmer-Lemeshow test of 'goodness of fit' and the area under the receiver operating curve, but also newer metrics, such as risk reclassification tables and net reclassification indices. The latter two tests are particularly useful when considering the addition of novel predictors to established models. Finally, models of absolute risk prediction should be internally and externally validated as they typically generalize only to populations with similar baseline characteristics and rates of competing events.
机译：慢性肾脏病（CKD）是与大量发病率和死亡率相关的越来越常见的公共卫生问题。风险预测模型提供了有用的临床和研究框架，用于预测不良事件的可能性并根据风险对CKD患者进行分层。但是，准确的绝对风险预测需要仔细的模型说明。必须考虑排除感兴趣事件的竞争事件（例如，终末期肾脏疾病研究中的死亡）。必须准确指定预测变量的功能形式和潜在影响修改;应评估非线性和可能的​​相互作用。还应考虑测量误差的潜在影响。这些组件中任何一个组件的规格错误都会极大地影响绝对风险预测。对预后模型的评估不仅应包括传统的校准和歧视测试，例如“拟合优度”的Hosmer-Lemeshow测试和接收器工作曲线下的面积，还应包括更新的指标，例如风险重分类表和净重分类索引。当考虑将新颖的预测变量添加到已建立的模型中时，后两个测试特别有用。最后，绝对风险预测模型应在内部和外部进行验证，因为它们通常仅适用于具有相似基线特征和竞争事件发生率的人群。

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