265 Connecting Electronic Medical Record Sub-phenotypes of Obstructive Sleep Apnea to Adverse Outcome Risks

OBJECTIVES/GOALS: Prior work has established subtypes of OSA and linked them to risks of future adverse events but rarely with the longitudinality and richness of data available in the EMR. Our goal is to leverage EMR data identify clinically meaningful sub-phenotypes of OSA and better study how they affect risks of adverse outcomes. METHODS/STUDY POPULATION: Vanderbilt’s EMR database has over 61,000 adult patients with a literature-validated EMR definition of OSA with a median EMR follow-up period of 4 years after OSA diagnosis. Of these patients, 12,516 have fully recorded sleep study data in addition to EMR variables such as age at study and most recent BMI. We applied several clustering methods including to identify natural sub-phenotypes of OSA and assessed cluster quality. We also applied techniques which allow a single patient to belong to multiple clusters in various degrees. After selecting final clusters, we plan to analyze the associations between OSA sub-phenotypes and risks using statistical tools like logistic regression and Cox proportional hazards regression, with and without adjusting for factors such as age, gender, and certain medications. RESULTS/ANTICIPATED RESULTS: Preliminary clustering with primarily sleep study data has shown overlap with literature-described patient clusters, including a severe, high non-REM stage 1 sleep, high BMI cluster and a high nocturnal limb movement cluster. As we incorporate more EMR variables, we will select a final set of OSA sub-types. We anticipate patients in different clusters to have different risks of various adverse OSA-associated outcomes that are tracked in our EMR data. Notable outcomes with sufficient incidence rates (>3%) after OSA diagnosis include essential hypertension (43.4%), hyperlipidemia (28.8%), type 2 diabetes (21.9%), anxiety disorder (19.2%), coronary atherosclerosis (14.9%), cerebrovascular disease (7.7%), and pulmonary heart disease (5.9%). DISCUSSION/SIGNIFICANCE: If our results match anticipations, we will show how EMR data can be used to define OSA sub-phenotypes and predict patient risks of various OSA-associated outcomes. This analysis enables work in personalized risk and treatment predictions for OSA patients. By better understanding these risks, providers can better tailor treatments to patients.