NCT05973331

Prospective Validation of an EHR-based Pancreatic Cancer Risk Model

Prospective Validation of an EHR-based Model to Predict Pancreatic Cancer Risk Using Multicenter US Data

• Status: Active Not Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 6,134,060 (actual)
• Sponsor: Beth Israel Deaconess Medical Center · Academic / Other
• Sex: All
• Age: 40 Years – 100 Years
• Healthy volunteers: Accepted

Summary

The goal of this prospective observational cohort study is to validate a previously developed pancreatic cancer risk prediction algorith (the PRISM model) using electronic health records from the general population. The main questions it aims to answer are: * Will a pancreatic cancer risk model, developed on routine EHR data, reliably and accurately predict pancreatic cancer in real-time? * What is the average time from model deployment and risk prediction, to the date of pancreatic cancer development and what is the stage of pancreatic cancer at diagnosis? The risk model will be deployed on data from individuals eligible for the study. Each individual will be assigned a risk score and tracked over time to assess the model's discriminatory performance and calibration.

Detailed description

To prospectively validate, implement in real-time, and assess performance of an EHR- based PDAC risk-prediction model. To test the hypothesis that our model will reliably predict PDAC in a real-time clinical setting, we will conduct a multi-center prospective cohort study, deploying the PDAC risk model within the TriNetX federated network database, and will take the following steps: i) generate a risk prediction score for each individual under the care of 44 health care organizations (HCOs) in the USA ii) follow all individuals for up to 3 years to assess the primary end-point of PDAC development. The following metrics will be used to test the discriminative performance and calibration of the EHR-based PDAC risk model in predicting incident PDAC, at the end of the 3-year period: 1. AUROC, sensitivity, specificity, PPV/NPV for assessing discrimination 2. Calibration: for assessing the accuracy of estimates, based on the estimated to observed number of events

Conditions

• Pancreatic Adenocarcinoma
• Predictive Cancer Model

Interventions

Type	Name	Description
OTHER	Pancreatic Cancer Risk Model (PRISM)	A neural network model (PrismNN) and a logistic regression model (PrismLR) that use routinely collected EHR data to stratify individuals from the general population into PDAC risk groups

Timeline

• Start date: 2023-04-21
• Primary completion: 2026-07-01
• Completion: 2026-09-01
• First posted: 2023-08-02
• Last updated: 2026-02-09

Locations

1 site across 1 country: United States