Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America.
PLoS Med. 2012;9(11):e1001344. doi: 10.1371/journal.pmed.1001344. Epub 2012 Nov 20.
Chronic kidney disease (CKD) is common, and associated with increased risk of cardiovascular disease and end-stage renal disease, which are potentially preventable through early identification and treatment of individuals at risk. Although risk factors for occurrence and progression of CKD have been identified, their utility for CKD risk stratification through prediction models remains unclear. We critically assessed risk models to predict CKD and its progression, and evaluated their suitability for clinical use.
We systematically searched MEDLINE and Embase (1 January 1980 to 20 June 2012). Dual review was conducted to identify studies that reported on the development, validation, or impact assessment of a model constructed to predict the occurrence/presence of CKD or progression to advanced stages. Data were extracted on study characteristics, risk predictors, discrimination, calibration, and reclassification performance of models, as well as validation and impact analyses. We included 26 publications reporting on 30 CKD occurrence prediction risk scores and 17 CKD progression prediction risk scores. The vast majority of CKD risk models had acceptable-to-good discriminatory performance (area under the receiver operating characteristic curve>0.70) in the derivation sample. Calibration was less commonly assessed, but overall was found to be acceptable. Only eight CKD occurrence and five CKD progression risk models have been externally validated, displaying modest-to-acceptable discrimination. Whether novel biomarkers of CKD (circulatory or genetic) can improve prediction largely remains unclear, and impact studies of CKD prediction models have not yet been conducted. Limitations of risk models include the lack of ethnic diversity in derivation samples, and the scarcity of validation studies. The review is limited by the lack of an agreed-on system for rating prediction models, and the difficulty of assessing publication bias.
The development and clinical application of renal risk scores is in its infancy; however, the discriminatory performance of existing tools is acceptable. The effect of using these models in practice is still to be explored.
慢性肾脏病（CKD）较为常见，且与心血管疾病和终末期肾病的风险增加相关，而这些疾病是可以通过早期识别和治疗高危人群来预防的。虽然已经确定了 CKD 发生和进展的危险因素，但它们对于通过预测模型进行 CKD 风险分层的效用尚不清楚。我们批判性地评估了预测 CKD 及其进展的风险模型，并评估了它们在临床应用中的适用性。
我们系统地检索了 MEDLINE 和 Embase（1980 年 1 月 1 日至 2012 年 6 月 20 日）。通过双重审查来识别报告用于预测 CKD 发生/存在或进展至晚期阶段的模型的开发、验证或影响评估的研究。我们提取了研究特征、风险预测因素、模型的区分度、校准和重新分类性能，以及验证和影响分析的数据。我们纳入了 26 项报告 30 项 CKD 发生预测风险评分和 17 项 CKD 进展预测风险评分的研究。在推导样本中，绝大多数 CKD 风险模型具有可接受至良好的区分性能（接受者操作特征曲线下面积>0.70）。校准的评估较少，但总体上可接受。只有 8 项 CKD 发生和 5 项 CKD 进展风险模型经过了外部验证，显示出适度至可接受的区分度。新型 CKD （循环或遗传）生物标志物是否可以改善预测在很大程度上仍不清楚，并且尚未进行 CKD 预测模型的影响研究。风险模型的局限性包括推导样本中缺乏种族多样性，以及验证研究的稀缺性。本综述受到缺乏用于评价预测模型的公认系统以及评估发表偏倚的困难的限制。
肾脏风险评分的开发和临床应用尚处于起步阶段；然而，现有工具的区分性能是可接受的。在实践中使用这些模型的效果仍有待探索。
