Prospective Validation of Liver Cancer Risk Computation (LIRIC) Models
- Disease/Condition
- Predictive Cancer Model
- Drug/Device/Intervention
- Liver Risk Computation Model (LIRIC)
- Study type
- Observational
- Intervention type
- other
- Primary sponsor
- Beth Israel Deaconess Medical Center
- Location
- Boston, United States of America
- Phase
- N/A
- Start date
- Apr 01, 2023
- End date
- Mar 31, 2027
- Enrollment
- 6000000 participants
Area under the receiver operating characteristic curve (AUROC) of LIRIC for all groups stratified,Calibration of LIRIC for all groups stratified,Performance metrics for LIRIC model risk quantiles
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.
- Registry
- ClinicalTrials.gov
- Trial ID
- NCT06140823
- Type
- Non-Device Trial
Access comprehensive clinical trial information for NCT06140823 through Pure Global AI's free database. This phase not specified trial is sponsored by Beth Israel Deaconess Medical Center and is currently Recruitment Completed. The study focuses on Predictive Cancer Model. Target enrollment is 6000000 participants.
This page provides complete trial specifications, intervention details, outcomes, and location information. Pure Global AI offers free access to ClinicalTrials.gov data, helping medical device and pharmaceutical companies navigate clinical research efficiently.