OBJECTIVES: To look at the available literature on validated prediction models for contrast induced @entity8 and describe their characteristics. DESIGN: Systematic review. DATA SOURCES: Medline, Embase, and @entity7184 ( @entity7184 ) 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 @entity1 that included derivation and validation cohorts. Data were extracted on baseline @entity1 characteristics, procedural characteristics, modelling methods, metrics of model performance, risk of bias, and clinical usefulness. Eligible studies evaluated characteristics of predictive models that identified @entity1 at risk of contrast induced @entity8 among adults undergoing a diagnostic or interventional procedure using conventional radiocontrast media (media used for computed tomography or angiography, and not @entity2287 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 @entity1011 measurement to define contrast induced @entity8 . 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 @entity740 , age, @entity6 , @entity712 or impaired ejection fraction, and @entity551 or shock. No prediction model evaluated its effect on clinical decision making or @entity1 outcomes. CONCLUSIONS: Most predictive models for contrast induced @entity8 in clinical use have modest ability, and are only relevant to @entity1 receiving contrast for coronary angiography. Further research is needed to develop models that can better inform @entity1 centred decision making, as well as improve the use of prevention strategies for contrast induced @entity8 .

Risk prediction models for contrast induced XXXX : systematic review.

@entity1 :: ('9606', 'Species') :: ['patients', 'patient'] @entity8 :: ('MESH:D007674', 'Disease') :: ['nephropathy'] @entity712 :: ('MESH:D006333', 'Disease') :: ['heart failure'] @entity7184 :: ('MESH:D012090', 'Disease') :: ['CINAHL', 'cumulative index to nursing and allied health literature'] @entity6 :: ('MESH:D003920', 'Disease') :: ['diabetes'] @entity740 :: ('MESH:D051436', 'Disease') :: ['chronic kidney disease'] @entity551 :: ('MESH:D007022', 'Disease') :: ['hypotension'] @entity1011 :: ('MESH:D003404', 'Chemical') :: ['creatinine'] @entity2287 :: ('MESH:D005682', 'Chemical') :: ['gadolinium']
@entity8 :: (MESH:D007674,Disease) :: ['nephropathy']
