Implementation of Risk Prediction Models Using Electronic Health Data for Early Detection of Pancreatic Cancer: A Prospective Pilot Feasibility Study.
Authors
Affiliations (4)
Affiliations (4)
- Department of Research and Evaluation, Southern California Permanente Medical Group, Pasadena, California, USA.
- Department of Gastroenterology, Kaiser Permanente Los Angeles Medical Center, Los Angeles, California, USA.
- Tulane Center for Clinical Research, Tulane University School of Medicine, New Orleans, Louisiana, USA.
- Department of Epidemiology, UCLA Fielding School of Public Health, Los Angeles, California, USA.
Abstract
We previously developed multiple machine-learning and regression-based risk prediction models using retrospective electronic health records (EHR) to estimate near-term risk of pancreatic cancer (PC). This pilot study aimed to assess the feasibility of an early detection strategy combining initial EHR-based risk prediction with downstream imaging and blood-based testing. This prospective study included adults aged 50-84 years with an estimated ≥ 1% 18-month risk of PC, identified between January 2021 and March 2023 using four previously validated models. Patients with a history or active suspicion of PC were excluded. Participants completed cross-sectional imaging and CA19-9 Labs at baseline and again at 18 months. Outcomes included enrollment rates, test completion rate, and burden of incidental findings. Of 1251 eligible patients, 102 (8.2%) enrolled (median age 69 years [IQR 62-75]; 58% women; 48% Hispanic, 34% White, 11% Black, and 7% Asian). At baseline, 89 (87%) completed imaging and 98 (96%) completed CA19-9. At 18 months, 79% completed both imaging and CA19-9. Ten (10%) participants underwent additional clinical evaluations triggered by imaging findings. Evaluations included an endoscopic ultrasound, renal imaging, and FibroScan. CA19-9 results were normal in most participants, though 18% had elevated levels at baseline. Real-time EHR-based ML algorithms can be combined with blood- and imaging-based surveillance for early detection of PC with acceptable burden of incidental findings. However, improved patient engagement is a key aspect that will need to be addressed in order to successfully scale an AI-guided strategy for early detection.