Implementation of risk prediction models using electronic health data for early detection of pancreatic cancer: a prospective pilot feasibility study
- 1Southern California Permanente Medical Group
Abstract
Background & AimsWe 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.
MethodsThis 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.
ResultsOf 1,251 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.
ConclusionsReal-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.
Related
- Early Local Tumour Control and Feasibility of Intratumoral P-32 Microparticle Implantation in Patients with Locally Advanced Pancreatic Cancer: A Prospective Single-Center Observational Cohort Study
- Implementation feasibility and early outcome trends of a nursing-led multimodal recovery protocol after urgent abdominal surgery: a prospective single-group evaluation study
- Prospective field deployment of Swaasa® AI for community-based screening of tuberculosis and other respiratory diseases in Andhra Pradesh: a feasibility and implementation study
- Implementation, feasibility and optimization of a web- and mobile-supported semi-automated shoulder rehabilitation system (WMSSR): a prospective consecutive interventional cohort study
- Beyond Manufacturing Feasibility: From Point-of-Care Manufacturing to Point-of-Care Implementation
BibTeX
@misc{wu2026implementation,
title = {{Implementation of risk prediction models using electronic health data for early detection of pancreatic cancer: a prospective pilot feasibility study}},
author = {B. U. Wu and T. Q. Luong and E. Lustigova and C. Jeon and E. J. Puttock and R. H. Moon and M. A. Munis and W. Chen},
howpublished = {medRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.22.26363704},
url = {https://www.medrxiv.org/content/10.64898/2026.09.22.26363704v1},
note = {CC BY-ND},
}
RIS
TY - UNPB
TI - Implementation of risk prediction models using electronic health data for early detection of pancreatic cancer: a prospective pilot feasibility study
AU - B. U. Wu
AU - T. Q. Luong
AU - E. Lustigova
AU - C. Jeon
AU - E. J. Puttock
AU - R. H. Moon
AU - M. A. Munis
AU - W. Chen
PY - 2026
DA - 2026/09/22
PB - medRxiv
DO - 10.64898/2026.09.22.26363704
UR - https://www.medrxiv.org/content/10.64898/2026.09.22.26363704v1
AB - Background & AimsWe 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. MethodsThis 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. ResultsOf 1,251 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. ConclusionsReal-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.
ER -