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Research Article Open Access
Volume 5 | Issue 2 | DOI: https://doi.org/10.33696/cardiology.5.057

External Validation of Four Cardiovascular Risk Prediction Models

  • 1iHealthScreen Inc., Richmond Hill, NY, USA
  • 2Icahn School of Medicine at Mount Sinai, New York, NY, USA
+ Affiliations - Affiliations

Corresponding Author

Alauddin Bhuiyan, alauddin.bhuiyan@gmail.com

Received Date: July 04, 2024

Accepted Date: July 19, 2024

Abstract

Background and Purpose: Cardiovascular disease (CVD) is a leading cause of death and disability in the world. Many CVD risk prediction models have been created, but those most widely used in clinical settings have not been externally validated, a significant gap addressed herein.

Methods: Using the Multi-Ethnic Study of Atherosclerosis (MESA), we have externally validated the Framingham Risk Score, ASSIGN (Assessing the cardiovascular disease risk using SIGN) risk score, Atherosclerotic Cardiovascular Disease (ASCVD) risk score, and the European SCORE model, which were selected based on popularity among clinicians and frequency of clinical use. The models were implemented in a computer program based on published algorithms, and 100 incident CVD and 100 non-incident CVD subjects from MESA were selected for testing the repeatability. The outcome in both models achieved 100% correlation. The individual model accuracy was tested by computing their sensitivity, specificity, accuracy, and C-statistics.

Results: For discrimination, ASCVD showed the highest C-statistic (0.717) and SCORE the lowest (0.677). The Framingham score provided the most consistent results among the models, with a sensitivity of 69% and a specificity of 62%, but overestimated the risks as shown by the calibration results. SCORE was relatively inconsistent, with a sensitivity of 34% and specificity of 85%, for a risk threshold set at the top 20%.

Conclusions: Calibration experiments showed that the transportability of the risk was generally poor in all models, with Framingham and SCORE particularly overestimating the risks for the American population There is clearly a need for better models to predict CVD risk in the multi-ethnic American population.

Keywords

Cardiac biomarkers, Cardiovascular risk reduction, Congestive heart failure

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