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Risk prediction models for contrast induced nephropathy: systematic review
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 D...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group Ltd.
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4784870/ https://www.ncbi.nlm.nih.gov/pubmed/26316642 http://dx.doi.org/10.1136/bmj.h4395 |
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author | Silver, Samuel A Shah, Prakesh M Chertow, Glenn M Harel, Shai Wald, Ron Harel, Ziv |
author_facet | Silver, Samuel A Shah, Prakesh M Chertow, Glenn M Harel, Shai Wald, Ron Harel, Ziv |
author_sort | Silver, Samuel A |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4784870 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BMJ Publishing Group Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-47848702016-03-29 Risk prediction models for contrast induced nephropathy: systematic review Silver, Samuel A Shah, Prakesh M Chertow, Glenn M Harel, Shai Wald, Ron Harel, Ziv BMJ Research 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. BMJ Publishing Group Ltd. 2015-08-27 /pmc/articles/PMC4784870/ /pubmed/26316642 http://dx.doi.org/10.1136/bmj.h4395 Text en © Silver et al 2015 http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/. |
spellingShingle | Research Silver, Samuel A Shah, Prakesh M Chertow, Glenn M Harel, Shai Wald, Ron Harel, Ziv Risk prediction models for contrast induced nephropathy: systematic review |
title | Risk prediction models for contrast induced nephropathy: systematic review |
title_full | Risk prediction models for contrast induced nephropathy: systematic review |
title_fullStr | Risk prediction models for contrast induced nephropathy: systematic review |
title_full_unstemmed | Risk prediction models for contrast induced nephropathy: systematic review |
title_short | Risk prediction models for contrast induced nephropathy: systematic review |
title_sort | risk prediction models for contrast induced nephropathy: systematic review |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4784870/ https://www.ncbi.nlm.nih.gov/pubmed/26316642 http://dx.doi.org/10.1136/bmj.h4395 |
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