Prospective Validation of Liver Cancer Risk Computation (LIRIC) Models

Predictive Cancer Model Clinical Trial

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

Status Active, not recruiting

Clinical Trial 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.

Clinical Trial 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). ;

Related Conditions & MeSH terms

• Liver Neoplasms
• Predictive Cancer Model

• NCT number: NCT06140823
• Study type: Observational
• Source: Beth Israel Deaconess Medical Center
• Status: Active, not recruiting
• Start date: April 1, 2023
• Completion date: March 31, 2027