264138 Quantifying change in cardiometabolic risk

Tuesday, October 30, 2012

Miguel Marino, PhD , Department of Biostatistics, Harvard School of Public Health, Boston, MA
Orfeu Buxton, PhD , Department of Medicine, Harvard Medical School/Brigham and Women's Hospital, Boston, MA
Yi Li, PhD , Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI
The probability of future cardiovascular disease can be predicted using the thoroughly validated Framingham risk score (FRS). The current FRS assigns weights to major CVD risk factors including age, sex, cholesterol, smoking, blood pressure and diabetes. Whereas the FRS includes non-modifiable risk factors such as age and gender, this study aims to evaluate a cumulative cardiometabolic risk score that is optimized on factors that can be modified (e.g. through longitudinal intervention studies) such as blood pressure cholesterol, smoking, body mass index, glycated hemoglobin and C-reactive protein to detect intervention or experimental effects. We describe methodology on how to optimally weight these factors to predict change in cardiometabolic risk. Because it is rare for any prediction model to maintain its predictive ability over time, we propose a dynamic prediction model that is continuously updated with new waves of Framingham data. The proposed prediction model will provide a similar Framingham risk score measure of cardiometabolic risk that is sensitive to modifiable known risk factors, while controlling for key non-modifiable factors of cardiometabolic risk through the final model where the risk score is the outcome. We use a gender-stratified cox proportional hazards model on the Framingham cohort data and use cross-validation for proper assessment of predictive ability of the proposed model.

Learning Areas:
Biostatistics, economics
Implementation of health education strategies, interventions and programs
Public health or related research

Learning Objectives:
1. Evaluate a cumulative cardiometabolic risk score that is optimized on factors that can be modified throughout the course of a study. 2. Design prediction models that are optimized on modifiable risk factors. 3. Describe dynamic prediction models that can be continuously updated with new waves of data (e.g. longitudinal studies).

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: This work is based on a Robert Wood Foundation seed grant where I am the principal investigator. The aim of this grant is to predict change in cardiometabolic risk using modifiable risk factors. My PhD degree is in Biostatistics from Harvard University where I focused on model selection and prediction. My interests are in statistical applications that aid prediction of cardiometabolic risk and designing of interventions to be powerful to detect changes in risk.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.

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