NCT06140823

Prospective Validation of Liver Cancer Risk Computation (LIRIC) Models

Prospective Validation of Liver Cancer Risk Computation (LIRIC) Models on Multicenter EHR Data

• Status: Active Not Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 6,000,000 (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 previously developed Hepatocellular Carcinoma (HCC) risk prediction algorithms, the Liver Risk Computation (LIRIC) models, which are based on electronic health records. The main questions it aims to answer are: * Will our retrospectively developed general population LIRIC models, developed on routine EHR data, perform similarly when prospectively validated, and reliably and accurately predict HCC in real-time? * What is the average time from model deployment and risk prediction, to the date of HCC development and what is the stage of HCC 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

The investigators will conduct a prospective observational cohort study, separately deploying three separate LIRIC models (the general population, cirrhosis, and no\_cirrhosis models) on retrospective de-identified EHR data of 44 HCOs in the USA, using the TriNetX federated network platform. LIRIC will generate a risk score for each individual. All risk-stratified individuals will be prospectively, electronically followed for up to 3-years to assess the primary end-point of HCC development. At the end of this period, model discrimination will be assessed, using the following metrics: AUROC, sensitivity, specificity, PPV/NPV. Risk scores generated by the model will be divided into quantiles. For each quantile, the investigators will evaluate the following: number of individuals in each quantile, number of HCC cases, PPV, NNS, SIR. Model calibration will be used for assessing the accuracy of estimates, based on the estimated to observed number of events. The model will dynamically re-evaluate all individual data every 6 months, re-classifying individuals (as needed).

Conditions

• Predictive Cancer Model

Interventions

Type	Name	Description
OTHER	Liver Risk Computation Model (LIRIC)	A neural network model (LIRIC-NN) and a logistic regression model (LIRIC-LR) that use routinely collected EHR data to stratify individuals into HCC risk groups for the general population
OTHER	Liver Risk Computation Model (LIRIC)_cirrhosis	A neural network model (LIRIC-NN) and a logistic regression model (LIRIC-LR) that use routinely collected EHR data to stratify individuals into HCC risk groups for the population with liver cirrhosis
OTHER	Liver Risk Computation Model (LIRIC)_no_cirrhosis	neural network model (LIRIC-NN) and a logistic regression model (LIRIC-LR) that use routinely collected EHR data to stratify individuals into HCC risk groups for the population without liver cirrhosis

Timeline

• Start date: 2023-04-01
• Primary completion: 2026-03-31
• Completion: 2027-03-31
• First posted: 2023-11-20
• Last updated: 2025-05-25

Locations

1 site across 1 country: United States