Prospective Validation of an EHR-based Pancreatic Cancer Risk Model

NCT ID: NCT05973331

Last Updated: 2024-11-07

Basic Information

• Recruitment Status: ACTIVE_NOT_RECRUITING
• Total Enrollment: 6134060 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2023-07-17
• Study Completion Date: 2026-09-30

Brief 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

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Study Groups

prospective general opulation cohort

Males and females age \>= 40 years, without a personal history of PDAC or current PDAC, with at least 2 clinical encounters to the HCO within the year prior to the study start date.

Pancreatic Cancer Risk Model (PRISM)

Intervention Type: OTHER

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

Interventions

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

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

• Male and females age >= 40 years from 44 US HCOs from the TriNetX platform
• at least 2 clinical encounters to the HCO, within the last year, before the study start date

Exclusion Criteria

• Personal history of PDAC or current PDAC
• Age below 40

Notes on sampling method: no sampling was performed. All eligible individuals are included in this study.

• Minimum Eligible Age: 40 Years
• Maximum Eligible Age: 100 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Massachusetts Institute of Technology

OTHER

Sponsor Role: collaborator

TriNetX, LLC

UNKNOWN

Sponsor Role: collaborator

Beth Israel Deaconess Medical Center

OTHER

Sponsor Role: lead

Responsible Party

Limor Appelbaum

Staff Scientist

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Limor Appelbaum, MD

Role: PRINCIPAL_INVESTIGATOR

Beth Israel Deaconess Medical Center

Locations

Beth Israel Deaconess Medical Center

Boston, Massachusetts, United States

Countries

United States

Other Identifiers

2023Trial

Identifier Type: -

Identifier Source: org_study_id