OBJECTIVES/GOALS: Diabetic ketoacidosis (DKA) is a life-threatening complication of diabetes. Though largely preventable, DKA is one of the most common acute complications of diabetes. In the US, rates of DKA hospitalization and associated costs have been increasing over the past two decades. METHODS/STUDY POPULATION: In this study, we used the Kentucky Statewide Inpatient Database (2010-2019) and the Nationwide Readmission Database (2010-2018) to explore variation in rates of DKA hospitalization across key sociodemographic subgroups (age, sex, race/ethnicity, rural/urban, insurance coverage, and county-level poverty index) and identify clinical predictors of DKA hospitalization. The primary outcome was hospitalization with a first diagnosis of DKA identified using ICD-9 and -10 codes. Crude rates were calculated using state- and county-level population estimates obtained from the US Census Bureau and are presented as the total number of events per 10,000 people. Regression models will be used to examine the associations between DKA hospitalization and clinical predictors. RESULTS/ANTICIPATED RESULTS: In Kentucky, from 2010-2019, rates of DKA hospitalization increased by 45% (from 65.5 to 94.8 per 100,000). The largest variation was observed by age, race/ethnicity, and insurance. In those aged 15-44, rates of DKA hospitalization were three times higher than rates in the youngest (<15) and oldest (>75) groups (>130 vs <45 per 100,000). Non-Hispanic Blacks experienced rates of DKA hospitalization that were 2x higher than rates observed in non-Hispanic Whites (183.9 vs 92.6 per 100,000). Those covered by Medicaid had the highest rates of DKA hospitalization (171.3 vs 32.4 per 100,000 in commercially insured). Small, but consistent, disparities were observed in rural vs urban counties and higher poverty rates. Predictors of DKA hospitalization are being examined in the Nationwide Readmission Database. DISCUSSION/SIGNIFICANCE: Our findings underscore significant variation in DKA risk across key sociodemographic subgroups and will examine and confirm previously identified clinical predictors of DKA. Because DKA is largely preventable, identifying individuals at higher risk and targeting interventions and services to these individuals may help reduce DKA rates.