Evidence map›Paper›PMID 31143841›Full record

ArticleDiagnostic and prognostic research2019

Development and validation of a prediction model to estimate the risk of liver cirrhosis in primary care patients with abnormal liver blood test results: protocol for an electronic health record study in Clinical Practice Research Datalink.

Suvi Härmälä, Alastair O'Brien, Constantinos A Parisinos, Kenan Direk, Laura Shallcross, Andrew Hayward

Open access · goldAbstract read
In one paragraph

Article in Diagnostic and prognostic research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
3.2field-weighted citation impact, top 8% 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

9 citing papers in PubMed, 26 citations in OpenAlex.

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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

6 authors at 1 institution in 1 country.

Suvi Härmälä1Institute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA UK.ORCID 0000-0002-2497-5940
Alastair O'Brien2Division of Medicine, University College London, Rayne Building, 5 University Street, London, WC1E 6JJ UK.
Constantinos A Parisinos1Institute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA UK.
Kenan Direk1Institute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA UK.
Laura Shallcross1Institute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA UK.
Andrew Hayward3Institute of Epidemiology and Health Care, University College London, 1-19 Torrington Place, London, WC1E 7HB UK.
University College London · GB

Funding

Department of Health CS-2016-16-007
6 · The paper itself

Abstract

backgroundDriven by alcohol consumption and obesity, the prevalence of non-viral liver disease in the UK is increasing. Due to its silent and slow nature, the progression of liver disease is currently unpredictable and challenging to monitor. The latest National Institute for Health Care Excellence cirrhosis guidelines call for a validated risk tool that would allow general practitioners to identify patients that are at high risk of developing cirrhosis.

methodsUsing linked electronic health records from the Clinical Practice Research Datalink (a database of > 10 million patients in England), we aim to develop and validate a prediction model to estimate 2-, 5- and 10-year risk of cirrhosis. The model will provide individualised cirrhosis risk predictions for adult primary care patients, free from underlying liver disease or viral hepatitis infection, whose liver blood test results come back abnormal. We will externally validate the model in patients from 30 further Clinical Practice Research Datalink general practices in England. DISCUSSION: The prediction model will provide estimates of cirrhosis risk in primary care patients with abnormal liver blood test results to guide referral to secondary care, to identify patients who are in serious need of preventative health interventions and to help reassure patients at low risk of cirrhosis in the long term.

Indexed as

CirrhosisElectronic health recordsLiver diseasePrediction modelPreventionPrimary carePrognosisPublic health

Identifiers

PMID31143841
PMCPMC6532213
OpenAlexW2947733205

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.