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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

Liao, Weiqi; Jepsen, Peter; Coupland, Carol; Innes, Hamish; Matthews, Philippa C; Campbell, Cori; Barnes, Eleanor; ... DeLIVER consortium, .; + view all (2022) 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. Diagnostic and Prognostic Research , 6 (1) , Article 21. 10.1186/s41512-022-00133-x. Green open access

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Abstract

BACKGROUND AND RESEARCH AIM: The incidence and mortality of liver cancer have been increasing in the UK in recent years. However, liver cancer is still under-studied. The Early Detection of Hepatocellular Liver Cancer (DeLIVER-QResearch) project aims to address the research gap and generate new knowledge to improve early detection and diagnosis of primary liver cancer from general practice and at the population level. There are three research objectives: (1) to understand the current epidemiology of primary liver cancer in England, (2) to identify and quantify the symptoms and comorbidities associated with liver cancer, and (3) to develop and validate prediction models for early detection of liver cancer suitable for implementation in clinical settings. METHODS: This population-based study uses the QResearch® database (version 46) and includes adult patients aged 25-84 years old and without a diagnosis of liver cancer at the cohort entry (study period: 1 January 2008-30 June 2021). The team conducted a literature review (with additional clinical input) to inform the inclusion of variables for data extraction from the QResearch database. A wide range of statistical techniques will be used for the three research objectives, including descriptive statistics, multiple imputation for missing data, conditional logistic regression to investigate the association between the clinical features (symptoms and comorbidities) and the outcome, fractional polynomial terms to explore the non-linear relationship between continuous variables and the outcome, and Cox/competing risk regression for the prediction model. We have a specific focus on the 1-year, 5-year, and 10-year absolute risks of developing liver cancer, as risks at different time points have different clinical implications. The internal-external cross-validation approach will be used, and the discrimination and calibration of the prediction model will be evaluated. DISCUSSION: The DeLIVER-QResearch project uses large-scale representative population-based data to address the most relevant research questions for early detection and diagnosis of primary liver cancer in England. This project has great potential to inform the national cancer strategic plan and yield substantial public and societal benefits.

Type: Article
Title: 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
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1186/s41512-022-00133-x
Publisher version: https://doi.org/10.1186/s41512-022-00133-x
Language: English
Additional information: © 2022 BioMed Central Ltd unless otherwise stated. This article is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
Keywords: Cholangiocarcinoma, Comorbidity, Diagnosis, Early detection, Hepatocellular carcinoma (HCC), Liver cancer, Risk prediction model, Symptom
UCL classification: UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Infection and Immunity
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL
URI: https://discovery.ucl.ac.uk/id/eprint/10158021
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