CONTEXT: A recent overview of all CVD models applicable to diabetes patients is not available. OBJECTIVE: To review the primary prevention studies that focused on the development, validation and impact assessment of a cardiovascular risk model, scores or rules that can be applied to patients with type 2 diabetes. DESIGN: Systematic review. DATA SOURCES: Medline was searched from 1966 to 1 April 2011. STUDY SELECTION: A study was eligible when it described the development, validation or impact assessment of a model that was constructed to predict the occurrence of cardiovascular disease in people with type 2 diabetes, or when the model was designed for use in the general population but included diabetes as a predictor. DATA EXTRACTION: A standardized form was sued to extract all data of the CVD models. RESULTS: 45 prediction models were identified, of which 12 were specifically developed for patients with type 2 diabetes. Only 31% of the risk scores has been externally validated in a diabetes population, with an area under the curve ranging from 0.61 to 0.86 and 0.59 to 0.80 for models developed in a diabetes population and in the general population, respectively. Only one risk score has been studied for its effect on patient management and outcomes. 10% of the risk scores are advocated in national diabetes guidelines. CONCLUSION: Many cardiovascular risk scores are available that can be applied to patients with type 2 diabetes. A minority of these risk scores has been validated and tested for its predictive accuracy, with only a few showing a discriminative value of ≥0.80. The impact of applying these risk scores in clinical practice is almost completely unknown, but their use is recommended in various national guidelines.
CONTEXT: A recent overview of all CVD models applicable to diabetespatients is not available. OBJECTIVE: To review the primary prevention studies that focused on the development, validation and impact assessment of a cardiovascular risk model, scores or rules that can be applied to patients with type 2 diabetes. DESIGN: Systematic review. DATA SOURCES: Medline was searched from 1966 to 1 April 2011. STUDY SELECTION: A study was eligible when it described the development, validation or impact assessment of a model that was constructed to predict the occurrence of cardiovascular disease in people with type 2 diabetes, or when the model was designed for use in the general population but included diabetes as a predictor. DATA EXTRACTION: A standardized form was sued to extract all data of the CVD models. RESULTS: 45 prediction models were identified, of which 12 were specifically developed for patients with type 2 diabetes. Only 31% of the risk scores has been externally validated in a diabetes population, with an area under the curve ranging from 0.61 to 0.86 and 0.59 to 0.80 for models developed in a diabetes population and in the general population, respectively. Only one risk score has been studied for its effect on patient management and outcomes. 10% of the risk scores are advocated in national diabetes guidelines. CONCLUSION: Many cardiovascular risk scores are available that can be applied to patients with type 2 diabetes. A minority of these risk scores has been validated and tested for its predictive accuracy, with only a few showing a discriminative value of ≥0.80. The impact of applying these risk scores in clinical practice is almost completely unknown, but their use is recommended in various national guidelines.
Authors: Joseph Yeboah; Raimund Erbel; Joseph Chris Delaney; Robin Nance; Mengye Guo; Alain G Bertoni; Matthew Budoff; Susanne Moebus; Karl-Heinz Jöckel; Gregory L Burke; Nathan D Wong; Nils Lehmann; David M Herrington; Stefan Möhlenkamp; Philip Greenland Journal: Atherosclerosis Date: 2014-08-14 Impact factor: 5.162
Authors: Gijs F N Berkelmans; Soffia Gudbjörnsdottir; Frank L J Visseren; Sarah H Wild; Stefan Franzen; John Chalmers; Barry R Davis; Neil R Poulter; Annemieke M Spijkerman; Mark Woodward; Sara L Pressel; Ajay K Gupta; Yvonne T van der Schouw; Ann-Marie Svensson; Yolanda van der Graaf; Stephanie H Read; Bjorn Eliasson; Jannick A N Dorresteijn Journal: Eur Heart J Date: 2019-09-07 Impact factor: 29.983
Authors: Ying Shan; Gerard Tromp; Helena Kuivaniemi; Diane T Smelser; Shefali S Verma; Marylyn D Ritchie; James R Elmore; David J Carey; Yvette P Conley; Michael B Gorin; Daniel E Weeks Journal: Genet Epidemiol Date: 2017-02-15 Impact factor: 2.135
Authors: Helen C Looker; Marco Colombo; Felix Agakov; Tanja Zeller; Leif Groop; Barbara Thorand; Colin N Palmer; Anders Hamsten; Ulf de Faire; Everson Nogoceke; Shona J Livingstone; Veikko Salomaa; Karin Leander; Nicola Barbarini; Riccardo Bellazzi; Natalie van Zuydam; Paul M McKeigue; Helen M Colhoun Journal: Diabetologia Date: 2015-03-05 Impact factor: 10.122
Authors: Albert Shieh; Shinya Ishii; Gail A Greendale; Jane A Cauley; Joan C Lo; Arun S Karlamangla Journal: J Bone Miner Res Date: 2016-10-21 Impact factor: 6.741
Authors: Stephen P Juraschek; Mara McAdams-Demarco; Edgar R Miller; Allan C Gelber; Janet W Maynard; James S Pankow; Hunter Young; Josef Coresh; Elizabeth Selvin Journal: Am J Epidemiol Date: 2014-01-12 Impact factor: 4.897