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Developing Non-Laboratory Cardiovascular Risk Assessment Charts and Validating Laboratory and Non-Laboratory-Based Models

BACKGROUND: Developing simplified risk assessment model based on non-laboratory risk factors that could determine cardiovascular risk as accurately as laboratory-based one can be valuable, particularly in developing countries where there are limited resources. OBJECTIVE: To develop a simplified non-...

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Detalles Bibliográficos
Autores principales: Hassannejad, Razieh, Mansourian, Marjan, Marateb, Hamidreza, Mohebian, Mohammad Reza, Gaziano, Thomas Andrew, Jackson, Rodney T., Angelantonio, Emanuele Di, Sarrafzadegan, Nizal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Ubiquity Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8428313/
https://www.ncbi.nlm.nih.gov/pubmed/34692382
http://dx.doi.org/10.5334/gh.890
Descripción
Sumario:BACKGROUND: Developing simplified risk assessment model based on non-laboratory risk factors that could determine cardiovascular risk as accurately as laboratory-based one can be valuable, particularly in developing countries where there are limited resources. OBJECTIVE: To develop a simplified non-laboratory cardiovascular disease risk assessment chart based on previously reported laboratory-based chart and evaluate internal and external validation, and recalibration of both risk models to assess the performance of risk scoring tools in other population. METHODS: A 10-year non-laboratory-based risk prediction chart was developed for fatal and non-fatal CVD using Cox Proportional Hazard regression. Data from the Isfahan Cohort Study (ICS), a population-based study among 6504 adults aged ≥ 35 years, followed-up for at least ten years was used for the non-laboratory-based model derivation. Participants were followed up until the occurrence of CVD events. Tehran Lipid and Glucose Study (TLGS) data was used to evaluate the external validity of both non-laboratory and laboratory risk assessment models in other populations rather than one used in the model derivation. RESULTS: The discrimination and calibration analysis of the non-laboratory model showed the following values of Harrell’s C: 0.73 (95% CI 0.71–0.74), and Nam-D’Agostino χ(2):11.01 (p = 0.27), respectively. The non-laboratory model was in agreement and classified high risk and low risk patients as accurately as the laboratory one. Both non-laboratory and laboratory risk prediction models showed good discrimination in the external validation, with Harrell’s C of 0.77 (95% CI 0.75–0.78) and 0.78 (95% CI 0.76–0.79), respectively. CONCLUSIONS: Our simplified risk assessment model based on non-laboratory risk factors could determine cardiovascular risk as accurately as laboratory-based one. This approach can provide simple risk assessment tool where laboratory testing is unavailable, inconvenient, and costly.