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Validity of Cardiovascular Risk Prediction Models in Kidney Transplant Recipients

Background. Predicting cardiovascular risk is of great interest in renal transplant recipients since cardiovascular disease is the leading cause of mortality. Objective. To conduct a systematic review to assess the validity of cardiovascular risk prediction models in this population. Methods. Five d...

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Detalles Bibliográficos
Autores principales: Mansell, Holly, Stewart, Samuel Alan, Shoker, Ahmed
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3996891/
https://www.ncbi.nlm.nih.gov/pubmed/24977223
http://dx.doi.org/10.1155/2014/750579
Descripción
Sumario:Background. Predicting cardiovascular risk is of great interest in renal transplant recipients since cardiovascular disease is the leading cause of mortality. Objective. To conduct a systematic review to assess the validity of cardiovascular risk prediction models in this population. Methods. Five databases were searched (MEDLINE, EMBASE, SCOPUS, CINAHL, and Web of Science) and cohort studies with at least one year of follow-up were included. Variables that described population characteristics, study design, and prognostic performance were extracted. The Quality in Prognostic Studies (QUIPS) tool was used to evaluate bias. Results. Seven studies met the criteria for inclusion, of which, five investigated the Framingham risk score and three used a transplant-specific model. Sample sizes ranged from 344 to 23,575, and three studies lacked sufficient event rates to confidently reach conclusion. Four studies reported discrimination (as measured by c-statistic), which ranged from 0.701 to 0.75, while only one risk model was both internally and externally validated. Conclusion. The Framingham has underestimated cardiovascular events in renal transplant recipients, but these studies have not been robust. A risk prediction model has been externally validated at least on one occasion, but comprehensive validation in multiple cohorts and impact analysis are recommended before widespread clinical application is advocated.