Prospective Validation of Liver Cancer Risk Computation (LIRIC) Models

N/AActive Not RecruitingNCT06140823

Beth Israel Deaconess Medical Center6,000,000 enrolled

Overview

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.

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).

Study Type

OBSERVATIONAL

Enrollment

6,000,000

Conditions

Predictive Cancer Model

Interventions

Liver Risk Computation Model (LIRIC)OTHER

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)_cirrhosisOTHER

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_cirrhosisOTHER

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

Eligibility

Sex: ALLMin age: 40 YearsMax age: 100 YearsHealthy volunteers:

Medical Language ↔ Plain English

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.

Outcomes

Primary Outcomes

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.