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DeLIVER

Title

Development and validation of personalised risk prediction models for early detection and diagnosis of hepatocellular carcinoma (HCC) among the English population from primary care

What is the aim of the study and why is it important?

The WP6 of the DeLIVER project aims for early detection and diagnosis (ED&D) of hepatocellular carcinoma (HCC, the most common type of liver cancer) among patients at high risk from the English primary care setting using routinely collected electronic health records (EHRs) linked in the QResearch database. There are three research objectives around this aim:
1) To describe the basic patient characteristics and epidemiologic indicators of people diagnosed with HCC in England – the incidence (overall and by age groups, sex, ethnicity, socioeconomic status, etc.), route to diagnosis, stage at diagnosis, treatments, and survival duration of patients diagnosed with HCC, and the trend over the last 15 years (2005-2020);
2) To determine the symptom and comorbidity profile for patients diagnosed with HCC, compared with people without HCC (two control groups), and identify the early signs and clinical features of HCC;
a. controls from the general population;
b. controls diagnosed with cirrhosis;
3) To develop and validate personalised prediction models for future risk of getting an HCC diagnosis using primary care EHRs, which could be applied to identify patients at the highest risk who will benefit the most from active surveillance and early medical intervention.

The first research objective is to understand the basic patient characteristics and the epidemiologic indicators of people diagnosed with HCC in England with a longitudinal case series study design. We are interested in the incidence of HCC, route to cancer diagnosis, stage at diagnosis, the treatments patients received, and the length of survival after patients diagnosed with HCC, and the trend of these indicators over the last 15 years (2005-2020). We will do this by linking electronic health records from the GP and hospital database with the national cancer registry, which has detailed information on the histological type and how advanced the cancer is. Descriptive statistics will be used primarily in this research objective, with line/bar charts to show the changes over the years where appropriate. The overall incidence rate of HCC (and by age groups, sex, ethnicity, socioeconomic status, etc.) in person-years will be calculated.

The second research objective is to understand the patterns of symptoms and illnesses in patients who have developed HCC (liver cancer). For each patient diagnosed with HCC (referred to as “case”) in the last 15 years, we will match up to 10 patients from the general English primary care population without liver cancer (referred to as “controls”) by age, sex, practice, and calendar year. We will investigate how common each symptom is in cases compared with controls in the different periods (e.g. six months, one year) before the diagnosis of liver cancer or the equivalent date in controls. We will identify all relevant symptoms and health conditions that are more commonly found in people diagnosed with liver cancer, compared with those who do not have liver cancer. We can use these symptoms and comorbidities (diagnosed health conditions) to develop risk prediction models in research objective 3, which can be used to identify patients at an earlier stage when their cancer may be curable.

We will have a second control group from patients diagnosed with cirrhosis. The reason to include this specific control group is that 80%-90% of patients diagnosed with HCC have cirrhosis. Therefore, cirrhosis could be understood as a precursor, and on the way of developing HCC. The results of this specific analysis may give us some insight on the clinical features indicating that the disease trajectory starts to transit from cirrhosis to liver cancer.

The final research objective of this study is to develop and validate risk prediction models for future risk of getting liver cancer (HCC) in the English population. Here provides an example of the final product of personalised prediction models for future risks of getting diagnosed with cancers, and how GP and patients can use such risk prediction models as decision support tools. For instance, a 78 year-old white man, 185cm tall and 95kg in weight, who smoked and consumed alcohol heavily in his life, previously diagnosed with type 2 diabetes and chronic obstructive airway disease, also has a family history of gastrointestinal cancer. Based on the above information, we can calculate his risk of getting different types of cancers (lung, colorectal, prostate, gastro-oesophageal, renal, pancreatic, oral, and blood) in the next 1-10 years (future risk), using the QCancer 10-year scores (Hippisley-Cox and Coupland, 2015). Liver cancer (HCC) is currently not included in this tool. Therefore, this study (DeLIVER WP6) will develop and validate models that can estimate an individual patient’s future risk of developing liver cancer (HCC). For more information and further exploration of risk prediction models for cancer, please visit the QCancer website at https://www.qcancer.org

https://deliver.cancer.ox.ac.uk

Reference:
Hippisley-Cox, J. & Coupland, C. 2015. Development and validation of risk prediction algorithms to estimate future risk of common cancers in men and women: prospective cohort study. BMJ Open, 5, e007825.

Chief Investigator

Professor Ellie Barnes

Sponsor

University of Oxford

Location of research

University of Oxford

Date on which research approved

08-Jul-2021

Project reference ID

OX30

Generic ethics approval reference

18/EM/0400

Are all data accessed are in anonymised form?

Yes

Brief summary of the dataset to be released (including any sensitive data)

Linked electronic health records from primary care (EMIS), secondary care (Hospital Episode Statistics, HES), Office for National Statistics (ONS) mortality data, and cancer registry, will be needed for this study. These four data sources are linked in the QResearch database.

Research Team

Prof Julia Hippisley-Cox (WP6 lead)

Dr Weiqi Liao

Prof Ellie Barnes (DeLIVER project CI)

Prof Carol Coupland

Prof Peter Jepsen (Aarhus University, Denmark)

Dr Philippa Matthews

Dr Hamish Innes (Glasgow Caledonian University)

Ms Cori Campbell

Dr Katja Pfafferott (project manager)

Mrs Allison Hirst (project manager)

 

Approval Letter

Download Approval Letter

Publications

  • Development and validation of personalised risk prediction models for early detection and diagnosis of primary liver cancer among the English primary care population using the QResearch® database: research protocol and statistical analysis plan
    Authors: Weiqi Liao, Peter Jepsen, Carol Coupland, Hamish Innes, Philippa C. Matthews, Cori Campbell, Eleanor Barnes & Julia Hippisley-Cox
    Ref:
    https://diagnprognres.biomedcentral.com/articles/10.1186/s41512-022-00133-x

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