Cardiovascular risk in patients with type 2 diabetes: A systematic review of prediction models

•There is overabundance of CVD risk prediction models for Type 2 diabetes patients.•Many CVD risk prediction models have high risk of bias and methodological problems.•Many CVD risk prediction models have not been externally validated.•Validation of CVD risk prediction models is needed in Type 2 dia...

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Bibliographic Details
Published in:Diabetes research and clinical practice 2022-02, Vol.184, p.109089-109089, Article 109089
Main Authors: Galbete, Arkaitz, Tamayo, Ibai, Librero, Julián, Enguita-Germán, Mónica, Cambra, Koldo, Ibáñez-Beroiz, Berta
Format: Article
Language:English
Subjects:
Cardiovascular Diseases - epidemiology
Cardiovascular Diseases - etiology
Cardiovascular risk factors
Diabetes Mellitus, Type 2 - complications
Diabetes Mellitus, Type 2 - epidemiology
Heart Disease Risk Factors
Humans
Prediction models
Prognosis
Risk Factors
Type 2 diabetes
Validation
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Summary:•There is overabundance of CVD risk prediction models for Type 2 diabetes patients.•Many CVD risk prediction models have high risk of bias and methodological problems.•Many CVD risk prediction models have not been externally validated.•Validation of CVD risk prediction models is needed in Type 2 diabetes patients. To identify all cardiovascular disease risk prediction models developed in patients with type 2 diabetes or in the general population with diabetes as a covariate updating previous studies, describing model performance and analysing both their risk of bias and their applicability A systematic search for predictive models of cardiovascular risk was performed in PubMed. The CHARMS and PROBAST guidelines for data extraction and for the assessment of risk of bias and applicability were followed. Google Scholar citations of the selected articles were reviewed to identify studies that conducted external validations. The titles of 10,556 references were extracted to ultimately identify 19 studies with models developed in a population with diabetes and 46 studies in the general population. Within models developed in a population with diabetes, only six were classified as having a low risk of bias, 17 had a favourable assessment of applicability, 11 reported complete model information, and also 11 were externally validated. There exists an overabundance of cardiovascular risk prediction models applicable to patients with diabetes, but many have a high risk of bias due to methodological shortcomings and independent validations are scarce. We recommend following the existing guidelines to facilitate their applicability.
ISSN:0168-8227
1872-8227
DOI:10.1016/j.diabres.2021.109089