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Cited by 32Diabetes, Cardiovascular Risks, and LipoproteinsHealth Promotion and Cardiovascular PreventionDiabetes Treatment and Management

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

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Galbete, Arkaitz, Tamayo, Ibai, Librero, Julián, Enguita-Germán, Mónica, Cambra, Koldo, Ibáñez-Beroiz, Berta·2022·Diabetes Research and Clinical Practice
DOI 10.1016/j.diabres.2021.109089
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Abstract

AIMS: 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 METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

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  • Traditional risk assessment tools such as the ACC/AHA Pooled Cohort Equations and the Framingham Risk Score suffer from notable limitations in patients with diabetes.

    “Traditional risk assessment tools, such as the ACC/AHA Pooled Cohort Equations (PCE) and the Framingham Risk Score, remain widely used in clinical practice but suffer from notable limitations in patients with diabetes (3).”

    From AI-driven cardiovascular risk prediction in patients with diabetes: bridging algorithmic innovation to equitable clinical application (2026) · cites this paper as [3]

    The Introduction (p. not available) says CVD risk models are included in clinical guidelines and that many fail key quality criteria. The Discussion and §3.1.4 (p. not available) report modest discrimination and poor calibration for some models studied in patients with type 2 diabetes. Our copy does not establish those limitations specifically for the ACC/AHA Pooled Cohort Equations or Framingham Risk Score, or that those two tools remain widely used in practice. (LLM, conf=0.86)

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Research works printed but not linked: 39

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Authors
Galbete, A., Tamayo, I., Librero, J., Enguita-Germán, M., Cambra, K., & Ibáñez-Beroiz, B.
Affiliations
  • Universidad Pública de Navarra (UPNA) (ES)
  • Basque Government (ES)
ORCID
Venue
Diabetes Research and Clinical Practice
Publisher
Elsevier BV
Published
2022-02
Volume
184
Pages
109089
ISSN
0168-8227 (ISSN-L)
DOI
10.1016/j.diabres.2021.109089
OpenAlex
W3207683296
Cited by
32
References
49
Citations by year
2026: 6 · 2025: 5 · 2024: 5 · 2023: 9 · 2022: 5 · 2021: 2
FWCI
0.81
Type
journal article
Language
en
Open access
Licence
Locations
  • Diabetes Research and Clinical Practice (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • Academica-e (Universidad Pública de Navarra) (submittedVersion, cc-by-nc-nd)
Funders
  • Instituto de Salud Carlos III (PI15/02196, PI19/00056, PI19/00154, PI19/00306, PI19/00381, PI20/00783, RD16/0001/0014)
  • European Regional Development Fund
Topics
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Health Promotion and Cardiovascular Prevention
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Treatment and Management
Keywords
cardiovascular risk prediction models, type 2 diabetes, cardiovascular risk, risk of bias, applicability, external validation, model performance
MeSH terms
Heart Disease Risk Factors, Cardiovascular Diseases, Diabetes Mellitus, Type 2, Humans, Prognosis, Risk Factors

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