Evidence map›Paper›PMID 36261855›Full record

ArticleDiagnostic and prognostic research2022

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.

Weiqi Liao, Peter Jepsen, Carol Coupland, Hamish Innes, Philippa C Matthews, Cori Campbell, Eleanor Barnes, Julia Hippisley-Cox, DeLIVER consortium

Open access · goldAbstract read
In one paragraph

Article in Diagnostic and prognostic research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.8field-weighted citation impact, top 28% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 5 institutions in 2 countries.

Weiqi LiaoNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK. weiqi.liao@phc.ox.ac.uk.ORCID http://orcid.org/0000-0002-8605-3749
Peter JepsenDepartment of Hepatology and Gastroenterology, Aarhus University Hospital, Aarhus, Denmark.ORCID http://orcid.org/0000-0002-6641-1430
Carol CouplandNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Hamish InnesSchool of Health and Life Sciences, Glasgow Caledonian University, Glasgow, UK.
Philippa C MatthewsThe Francis Crick Institute, London, UK.ORCID http://orcid.org/0000-0002-4036-4269
Cori CampbellNuffield Department of Medicine, University of Oxford, Oxford, UK.
Eleanor BarnesNuffield Department of Medicine, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0002-0860-0831
Julia Hippisley-CoxNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0002-2479-7283
DeLIVER consortium
University of Oxford · GBAarhus University Hospital · DKGlasgow Caledonian University · GBThe Francis Crick Institute · GBUniversity of Nottingham · GB

Funding

Cancer Research UK 29725Cancer Research UK C30358/A29725Medical Research Council MR/K01532X/1MRF_ MRF-054-0001-F-INNE-C0825
6 · The paper itself

Abstract

BACKGROUND AND RESEARCH

aimThe 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.

methodsThis 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.

Indexed as

CholangiocarcinomaComorbidityDiagnosisEarly detectionHepatocellular carcinoma (HCC)Liver cancerRisk prediction modelSymptom

Identifiers

PMID36261855
PMCPMC9583476
OpenAlexW4306828717

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.