Risk Prediction Models for Contrast Induced Nephropathy
Risk Prediction Models for Contrast Induced Nephropathy
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.
Every year, over 80 million iodinated contrast studies are performed worldwide, ordered by a wide range of medical specialties. With the increasing trend towards minimally invasive diagnostic and interventional procedures that often need intravenous or intra-arterial contrast, there has been a concomitant rise in the incidence of acute kidney injury after exposure to radiocontrast, often termed contrast induced nephropathy. In fact, contrast induced nephropathy could be as high as the third most common cause of acute kidney injury in patients admitted to hospital, after ischaemic and drug induced injury.
Studies have shown a strong association between contrast induced nephropathy and adverse clinical outcomes, including cardiovascular complications, provision of dialysis, and death. Therefore, the use of prediction models for contrast induced nephropathy could have several benefits. Firstly, they may help identify patients at high risk for the disorder, who might benefit from peri-procedural strategies that protect the kidney. Secondly, patients identified as high risk would also be an ideal population to study novel therapies for the prevention and treatment of the disorder. Finally, prediction models for contrast induced nephropathy could improve preintervention counselling to facilitate informed patient centred decision making.
Despite these potential benefits, clinical prediction rules have several weaknesses that limit their application in daily practice. These include differences in derivation, inconsistent external validation, and complexity. Most importantly, the downstream effects of adopting clinical prediction rules to guide decision making and improve patient outcomes are often not evaluated. These factors make it challenging for a clinician to select the ideal model to use in practice. To address this knowledge gap, we did a systematic review to look at the available literature on validated prediction models for contrast induced nephropathy, and to describe their performance and clinical usefulness.
Abstract and Introduction
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.
Introduction
Every year, over 80 million iodinated contrast studies are performed worldwide, ordered by a wide range of medical specialties. With the increasing trend towards minimally invasive diagnostic and interventional procedures that often need intravenous or intra-arterial contrast, there has been a concomitant rise in the incidence of acute kidney injury after exposure to radiocontrast, often termed contrast induced nephropathy. In fact, contrast induced nephropathy could be as high as the third most common cause of acute kidney injury in patients admitted to hospital, after ischaemic and drug induced injury.
Studies have shown a strong association between contrast induced nephropathy and adverse clinical outcomes, including cardiovascular complications, provision of dialysis, and death. Therefore, the use of prediction models for contrast induced nephropathy could have several benefits. Firstly, they may help identify patients at high risk for the disorder, who might benefit from peri-procedural strategies that protect the kidney. Secondly, patients identified as high risk would also be an ideal population to study novel therapies for the prevention and treatment of the disorder. Finally, prediction models for contrast induced nephropathy could improve preintervention counselling to facilitate informed patient centred decision making.
Despite these potential benefits, clinical prediction rules have several weaknesses that limit their application in daily practice. These include differences in derivation, inconsistent external validation, and complexity. Most importantly, the downstream effects of adopting clinical prediction rules to guide decision making and improve patient outcomes are often not evaluated. These factors make it challenging for a clinician to select the ideal model to use in practice. To address this knowledge gap, we did a systematic review to look at the available literature on validated prediction models for contrast induced nephropathy, and to describe their performance and clinical usefulness.
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