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对比剂肾病的风险预测模型:系统评价

Risk prediction models for contrast induced nephropathy: systematic review.

作者信息

Silver Samuel A, Shah Prakesh M, Chertow Glenn M, Harel Shai, Wald Ron, Harel Ziv

机构信息

Division of Nephrology, St Michael's Hospital, University of Toronto, Toronto, Canada.

Department of Paediatrics, Mount Sinai Hospital, University of Toronto, Toronto, Canada.

出版信息

BMJ. 2015 Aug 27;351:h4395. doi: 10.1136/bmj.h4395.

Abstract

OBJECTIVES

To look at the available literature on validated prediction models for contrast induced nephropathy and describe their characteristics.

DESIGN

Systematic review.

DATA SOURCES

Medline, Embase, and CINAHL (cumulative index to nursing and allied health literature) databases.

REVIEW METHODS

Databases searched from inception to 2015, and the retrieved reference lists hand searched. Dual reviews were conducted to identify studies published in the English language of prediction models tested with patients that included derivation and validation cohorts. Data were extracted on baseline patient characteristics, procedural characteristics, modelling methods, metrics of model performance, risk of bias, and clinical usefulness. Eligible studies evaluated characteristics of predictive models that identified patients at risk of contrast induced nephropathy among adults undergoing a diagnostic or interventional procedure using conventional radiocontrast media (media used for computed tomography or angiography, and not gadolinium based contrast).

RESULTS

16 studies were identified, describing 12 prediction models. Substantial interstudy heterogeneity was identified, as a result of different clinical settings, cointerventions, and the timing of creatinine measurement to define contrast induced nephropathy. Ten models were validated internally and six were validated externally. Discrimination varied in studies that were validated internally (C statistic 0.61-0.95) and externally (0.57-0.86). Only one study presented reclassification indices. The majority of higher performing models included measures of pre-existing chronic kidney disease, age, diabetes, heart failure or impaired ejection fraction, and hypotension or shock. No prediction model evaluated its effect on clinical decision making or patient outcomes.

CONCLUSIONS

Most predictive models for contrast induced nephropathy in clinical use have modest ability, and are only relevant to patients receiving contrast for coronary angiography. Further research is needed to develop models that can better inform patient centred decision making, as well as improve the use of prevention strategies for contrast induced nephropathy.

摘要

目的

查阅关于对比剂肾病有效预测模型的现有文献,并描述其特征。

设计

系统评价。

数据来源

Medline、Embase和CINAHL(护理及相关健康文献累积索引)数据库。

综述方法

检索自数据库建库至2015年的文献,并手工检索所获参考文献列表。进行双人评审以识别用患者进行测试的预测模型的英文发表研究,这些研究包括推导队列和验证队列。提取关于患者基线特征、操作特征、建模方法、模型性能指标、偏倚风险和临床实用性的数据。符合条件的研究评估了预测模型的特征,这些模型在使用传统放射性对比剂(用于计算机断层扫描或血管造影的对比剂,而非钆基对比剂)进行诊断或介入操作的成人中识别出有对比剂肾病风险的患者。

结果

识别出16项研究,描述了12种预测模型。由于临床环境、联合干预措施以及定义对比剂肾病的肌酐测量时间不同,研究间存在显著异质性。10种模型进行了内部验证,6种进行了外部验证。内部验证的研究中辨别力有所不同(C统计量为0.61 - 0.95),外部验证的研究中辨别力也不同(0.57 - 0.86)。只有一项研究给出了重新分类指数。大多数性能较高模型纳入了既往慢性肾病、年龄、糖尿病、心力衰竭或射血分数降低以及低血压或休克的测量指标。没有预测模型评估其对临床决策或患者结局的影响。

结论

临床应用的大多数对比剂肾病预测模型能力有限,且仅与接受冠状动脉造影对比剂的患者相关。需要进一步研究以开发能够更好地为以患者为中心的决策提供信息的模型,并改善对比剂肾病预防策略的应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d5a8/4784870/517a506ec114/sils027294.f1_default.jpg

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