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| Name | Class |
|---|---|
| Massachusetts Institute of Technology | OTHER |
| TriNetX, LLC | INDUSTRY |
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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:
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).
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| prospective general population cohort | Males and females age >= 40 years, without a personal history of HCC or current HCC and at least two clinical visits to their HCO, within the last year, before the study start date. |
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| Prospective cirrhosis population cohort | Males and females age >= 40 years, with liver cirrhosis and without a personal history of HCC or current HCC, that have at least two clinical visits to their HCO, within the last year, before the study start date. |
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| Prospective no_cirrhosis population cohort | Males and females age >= 40 years, without a personal history of HCC or current HCC and without a diagnosis of liver cirrhosis, that have at least two clinical visits to their HCO, within the last year, before the study start date. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 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 |
| Measure | Description | Time Frame |
|---|---|---|
| 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 |
| 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 |
| 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 |
| Measure | Description | Time Frame |
|---|---|---|
| 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 |
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The investigators will utilize the following criteria for all 3 models:
Inclusion criteria:
Exclusion Criteria:
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The cohort will be selected from 44 eligible HCOs comprised of community hospitals, outpatient clinics and academic medical centers from across the US.
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Beth Israel Deaconess Medical Center | Boston | Massachusetts | 02115 | United States |
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| Liver Risk Computation Model (LIRIC)_cirrhosis | 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 population with liver cirrhosis |
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| Liver Risk Computation Model (LIRIC)_no_cirrhosis | Other | 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 |
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| Tumor stage at HCC diagnosis | TNM staging at HCC diagnosis | 6 months from index date, at 1 year, 2 years and 3 years |