A Clinical Prediction Model to Assess Risk for Pancreatic Cancer Among Patients With Acute Pancreatitis

Abstract

Objectives: We aimed to develop and validate a prediction model as the first step in a sequential screening strategy to identify acute pancreatitis (AP) individuals at risk for pancreatic cancer (PC).

Materials and methods: We performed a population-based retrospective cohort study among individuals 40 years or older with a hospitalization for AP in the US Veterans Health Administration. For variable selection, we used least absolute shrinkage and selection operator regression with 10-fold cross-validation to identify a parsimonious logistic regression model for predicting the outcome, PC diagnosed within 2 years after AP. We evaluated model discrimination and calibration.

Results: Among 51,613 eligible study patients with AP, 801 individuals were diagnosed with PC within 2 years. The final model (area under the receiver operating curve, 0.70; 95% confidence interval, 0.67-0.73) included histories of gallstones, pancreatic cyst, alcohol use, smoking, and levels of bilirubin, triglycerides, alkaline phosphatase, aspartate aminotransferase, alanine aminotransferase, and albumin. If the predicted risk threshold was set at 2% over 2 years, 20.3% of the AP population would undergo definitive screening, identifying nearly 50% of PC associated with AP.

Conclusions: We developed a prediction model using widely available clinical factors to identify high-risk patients with PC-associated AP, the first step in a sequential screening strategy.