The following are five points to remember about using risk models to understand gender disparities in care:

1. This cardiovascular perspective examines the role of risk scores in understanding gender differences related to cardiovascular care.
2. Gender differences in the treatment of patients for primary prevention and diagnosis of coronary artery disease have been described in prior studies, both observational and randomized trials. The authors hypothesize that residual bias occurs in research since sex may be an important determinant of outcomes and related treatment benefits.
3. Clinical prediction models estimate the risk for coronary artery disease for a female lower than for a comparable male. When sex-specific effects on risk are not included in assessment of future risk, understanding the degree of gender-based disparity is difficult or impossible to assess.
4. Using the National Health and Nutrition Examination Survey (NHANES) cohort, the authors modeled the association of gender and the likelihood of receiving statin therapy for a hypothetical scenario. An estimated 40% of men and 34% of women using this scenario would be recommended to receive statins. However, after adjusting for 10-year risk of atherosclerotic cardiovascular disease, gender disparity in receipt of statins was no longer observed.
5. The authors suggest that controlling for baseline risk factors alone does not explain gender differences, given that gender in itself is associated with difference in outcomes. The authors concluded that research on differences in treatment and outcomes related to gender (and also likely race) should account for known differences in outcome risk or therapy response related to gender and/or race.

Keywords: Coronary Artery Disease, Coronary Disease, Female, Hydroxymethylglutaryl-CoA Reductase Inhibitors, Male, Nutrition Surveys, Primary Prevention, Risk, Risk Factors

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