Prospective Validation of Liver Cancer Risk Computation (LIRIC) Models on Multicenter EHR Data
概览
- 阶段
- 不适用
- 干预措施
- Liver Risk Computation Model (LIRIC)
- 疾病 / 适应症
- Predictive Cancer Model
- 发起方
- Beth Israel Deaconess Medical Center
- 入组人数
- 6000000
- 试验地点
- 2
- 主要终点
- Area under the receiver operating characteristic curve (AUROC) of LIRIC for all groups stratified
- 状态
- 进行中（未招募）
- 最后更新
- 10天前
概览
简要总结
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.
详细描述
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).
研究者
Limor Appelbaum
Principal Investigator
Beth Israel Deaconess Medical Center
入排标准
入选标准
- •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
排除标准
- •Personal history of HCC or current HCC (ICD-9: 155.0; ICD-10: C22.0)
- •Age below
- •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.
研究组 & 干预措施
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.
干预措施: Liver Risk Computation Model (LIRIC)
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.
干预措施: Liver Risk Computation Model (LIRIC)_cirrhosis
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.
干预措施: Liver Risk Computation Model (LIRIC)_no_cirrhosis
结局指标
主要结局
Area under the receiver operating characteristic curve (AUROC) of LIRIC for all groups stratified
时间窗: 6 months from index date, at 1 year, 2 years and 3 years
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.
Calibration of LIRIC for all groups stratified
时间窗: 6 months from index date, at 1 year, 2 years and 3 years
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.
Performance metrics for LIRIC model risk quantiles
时间窗: 6 months from index date, at 1 year, 2 years and 3 years
To evaluate the sensitivity, specificity, number of individuals/number of HCC cases, PPV, NNS in each predicted risk quantile for multiple risk thresholds
次要结局
- Timing of incident HCC occurrence(6 months from index date, at 1 year, 2 years and 3 years)
- Tumor stage at HCC diagnosis(6 months from index date, at 1 year, 2 years and 3 years)