Studies that examine racial disparities in health outcomes often include analyses that account or adjust for baseline differences in co-morbid conditions. Often, these conditions are defined as dichotomous (Yes/No) variables, and few analyses include clinical and/or laboratory data that could allow for more nuanced estimates of disease severity. However, disease severity – not just prevalence – can differ substantially by race and is an underappreciated mechanism for health disparities. Thus, relying on dichotomous disease indicators may not fully describe health disparities. This study explores the effect of substituting continuous clinical and/or laboratory data for dichotomous disease indicators on racial disparities, using data from the Queen’s Medical Center’s (QMC) cardiac surgery database (a subset of the national Society of Thoracic Surgeon’s cardiothoracic surgery database) as an example case. Two logistic regression models predicting in-hospital mortality were constructed: (I) a baseline model including race and dichotomous (Yes/No) indicators of disease (diabetes, heart failure, liver disease, kidney disease), and (II) a more detailed model with continuous laboratory values in place of the dichotomous indicators (eg, including Hemoglobin A1c level rather than just diabetes yes/no). When only dichotomous disease indicators were used in the model, Native Hawaiian and other Pacific Islander (NHPI) race was significantly associated with in-hospital mortality (OR: 1.57[1.29,2.47], P=.04). Yet when the more specific laboratory values were included, NHPI race was no longer associated with in-hospital mortality (OR: 1.67[0.92,2.28], P=.28). Thus, researchers should be thoughtful in their choice of independent variables and understand the potential impact of how clinical measures are operationalized in their research.