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

Clinical Trial Details — Status: Active, not recruiting

Administrative data

• NCT number: NCT06140823
• Other study ID #: Nov2023Trial
• Status: Active, not recruiting
• Start date: April 1, 2023
• Est. completion date: March 31, 2027

Study information

• Verified date: November 2023
• Source: Beth Israel Deaconess Medical Center
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

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.

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).

Recruitment information / eligibility

• Status: Active, not recruiting
• Enrollment: 6000000
• Est. completion date: March 31, 2027
• Est. primary completion date: March 31, 2026
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 40 Years to 100 Years

Eligibility

The investigators will utilize the following criteria for all 3 models:

Inclusion criteria:

• Male and females age =40 years from all US HCOs available on the platform

• at least at least 2 clinical encounters to the HCO, within the last year, before the study start date

Exclusion Criteria:

• Personal history of HCC or current HCC (ICD-9: 155.0; ICD-10: C22.0)

• Age below 40. The same dataset will be utilized for the non-cirrhosis validation, with exclusion of all cases with cirrhosis (ICD-9: 571.2, 571.5; ICD-10: K70, K70.3, K70.30, K70.31, K74, K74.0, K74.6, K74.60, K74.69). For the cirrhosis validation, the investigators will include only patients with the above cirrhosis codes.

Related Conditions & MeSH terms

• Liver Neoplasms
• Predictive Cancer Model

Intervention

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
• 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
• 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

Locations

Country	Name	City	State
United States	Beth Israel Deaconess Medical Center	Boston	Massachusetts

Sponsors (3)

Lead Sponsor	Collaborator
Beth Israel Deaconess Medical Center	Massachusetts Institute of Technology, TriNetX, LLC

Country where clinical trial is conducted

United States, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Area under the receiver operating characteristic curve (AUROC) of LIRIC for all groups stratified	To assess the discriminatory performance of LIRIC for prospective identification of high-risk individuals for HCC development. ROCs and AUROC numbers will be calculated for the whole population and groups stratified by age, sex, race, and geographical location.	6 months from index date, at 1 year, 2 years and 3 years
Primary	Calibration of LIRIC for all groups stratified	To assess how well the risk prediction by LIRIC aligns with observed risk without recalibration. Calibration plots will be created for the whole population and groups stratified by age, sex, race, and geographical location.	6 months from index date, at 1 year, 2 years and 3 years
Primary	Performance metrics for LIRIC model risk quantiles	To evaluate the sensitivity, specificity, number of individuals/number of HCC cases, PPV, NNS in each predicted risk quantile for multiple risk thresholds	6 months from index date, at 1 year, 2 years and 3 years
Secondary	Timing of incident HCC occurrence	To evaluate how long in advance before HCC occurrence should be expected for LIRIC models to make high-risk predictions based on different thresholds for high-risk. Distribution plots of the date of HCC incidence for multiple risk thresholds will be created.	6 months from index date, at 1 year, 2 years and 3 years
Secondary	Tumor stage at HCC diagnosis	TNM staging at HCC diagnosis	6 months from index date, at 1 year, 2 years and 3 years