Prediction of Pancreatic Cancer in Diabetes Patients with Worsening Glycemic Control

Background: Worsening glycemic control indicates elevated risk of pancreatic ductal adenocarcinoma (PDAC). We developed pre-diction models for PDAC among those with worsening glycemic control after diabetes diagnosis. Methods: In 2000-2016 records within the Veterans Affairs Health System (VA), we identified three cohorts with progression of diabetes: (i) insulin initiation (n = 449,685), (ii) initiation of combination oral hypoglycemic medication (n = 414,460), and (iii) hemoglobin A1c (HbA1c) >= 8% with >= D1% within 15 months (n = 593,401). We computed 12-, 36-, and 60-month incidence of PDAC and developed prediction models separately for males and females, with consideration of >30 demographic, behavioral, clin-ical, and laboratory variables. Models were selected to optimize Akaike's Information Criterion, and performance for predicting 12-, 36-, and 60-month incident PDAC was evaluated by bootstrap. Results: Incidence of PDAC was highest for insulin initiators and greater in males than in females. Optimism-corrected c-indices of the models for predicting 36-month incidence of PDAC in the male population were: (i) 0.72, (ii) 0.70, and (iii) 0.71, respectively. Models performed better for predicting 12-month incident PDAC [c-index (i) 0.78, (ii) 0.73, (iii) 0.76 for males], and worse for predicting 60-month incident PDAC [c-index (i) 0.69, (ii) 0.67, (iii) 0.68 for males]. Model performance was lower among females. For subjects whose model-predicted 36-month PDAC risks were >= 1%, the observed incidences were (i) 1.9%, (ii) 2.2%, and (iii) 1.8%. Conclusions: Sex-specific models for PDAC can estimate risk of PDAC at the time of progression of diabetes. Impact: Our models can identify diabetes patients who would benefit from PDAC screening.

Automated summary

This study developed prediction models for pancreatic ductal adenocarcinoma (PDAC) among diabetes patients with worsening glycemic control. The models, which considered various demographic, behavioral, clinical, and laboratory variables, can estimate the risk of PDAC at different time points and guide screening efforts.