10544 Background: Screening for pancreatic ductal adenocarcinoma (PDAC) has focused on individuals with a hereditary component, accounting for only 10% of PDAC events. Using the electronic health record, we developed a PDAC risk prediction model in a general population in the Veterans Health Administration (VA), the largest integrated health system in the US. Methods: We included all individuals from 2002- 2021 with ≥1 inpatient or outpatient VA encounter and recorded race, starting follow-up 1.5 years after first VA encounter. We excluded those who developed PDAC before start of follow-up. We used the multivariable Cox proportional hazards model in the derivation dataset (random 70% of cohort) to estimate the coefficients associated with each predictor for our outcome, time to PDAC (3 and 10 years), censoring at loss to follow-up or 12/31/2021. Candidate predictors included age, gender, race/ethnicity, sex, BMI, smoking status, alcohol use (alcohol use/related disorders and AUDIT-C), diabetes, pancreatitis, Hepatitis C, HIV, liver disease, cholecystectomy, pancreatic cysts, Charlson comorbidity index and common laboratory values. Selection of predictors was based on clinical reasoning, extent of missing values and model fit (Akaike Information Criterion and Harrell’s c–statistic). We evaluated model discrimination using c – statistic and model calibration comparing observed Kaplan – Meier estimated incidence vs. predicted risk score. Results: Among 9.4 million individuals, we observed 19,839 cases of PDAC within 10 years (6,022 cases within 3 years). Median baseline age was 58.5 years, 75.0% were White, and 16.3% were Black. Final predictors included age, gender, smoking, alcohol use (alcohol use/related disorders and AUDIT-C), BMI, diabetes, pancreatitis, pancreatic cyst, and history of cancer (Table, c-statistic in the derivation cohort 3 years: 0.79; 10 years: 0.77). Ten-year risk ranged maximally from 0-5.5%. Preliminary studies suggest labs do not clinically improve model performance. Conclusions: We developed a novel prediction model using available clinical data in a general population that identifies PDAC with high accuracy. Future work will try to identify additional predictors to improve the range of risk predicted. We will validate our findings in the testing set and in external cohorts. [Table: see text]
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