Ignite Creation Date:
2024-05-06 @ 7:49 PM
Last Modification Date:
2024-10-26 @ 3:14 PM
Study NCT ID:
NCT06140823
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2023-11-24
First Post:
2023-11-15
Brief Title:
Prospective Validation of Liver Cancer Risk Computation LIRIC Models
Sponsor:
Beth Israel Deaconess Medical Center
Organization:
Beth Israel Deaconess Medical Center
Study Overview
Official Title:
Prospective Validation of Liver Cancer Risk Computation LIRIC Models on Multicenter EHR Data
Status:
ACTIVE_NOT_RECRUITING
Status Verified Date:
2023-11
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief 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 models discriminatory performance and calibration
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 models 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 PPVNPV 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
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None