Arkaitz Galbete1, Ibai Tamayo2, Julián Librero2, Mónica Enguita-Germán2, Koldo Cambra3, Berta Ibáñez-Beroiz4. 1. Navarrabiomed-Hospital Universitario de Navarra (HUN)-Universidad Pública de Navarra (UPNA), Pamplona, Spain; Departamento de Estadística, Universidad Pública de Navarra (UPNA), Pamplona, Spain; Red de Investigación en Servicios Sanitarios y Enfermedades Crónicas (REDISSEC), Bilbao, Spain; Instituto de Investigación Sanitaria de Navarra (IdiSNA), IdiSNA, Pamplona, Spain. 2. Navarrabiomed-Hospital Universitario de Navarra (HUN)-Universidad Pública de Navarra (UPNA), Pamplona, Spain; Red de Investigación en Servicios Sanitarios y Enfermedades Crónicas (REDISSEC), Bilbao, Spain; Instituto de Investigación Sanitaria de Navarra (IdiSNA), IdiSNA, Pamplona, Spain. 3. Red de Investigación en Servicios Sanitarios y Enfermedades Crónicas (REDISSEC), Bilbao, Spain; Dirección de Salud Pública y Adicciones, Departamento de Sanidad, Gobierno Vasco, Vitoria, Spain. 4. Navarrabiomed-Hospital Universitario de Navarra (HUN)-Universidad Pública de Navarra (UPNA), Pamplona, Spain; Red de Investigación en Servicios Sanitarios y Enfermedades Crónicas (REDISSEC), Bilbao, Spain; Instituto de Investigación Sanitaria de Navarra (IdiSNA), IdiSNA, Pamplona, Spain; Departamento de Ciencias de la Salud, Universidad Pública de Navarra (UPNA), Pamplona, Spain. Electronic address: berta.ibanez.beroiz@navarra.es.
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.
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.
Authors: Mónica Enguita-Germán; Ibai Tamayo; Arkaitz Galbete; Julián Librero; Koldo Cambra; Berta Ibáñez-Beroiz Journal: Int J Environ Res Public Health Date: 2021-11-24 Impact factor: 3.390