Evidence map›Paper›PMID 33574154›Full record

ArticleBMJ open2021

Can routine blood tests be modelled to detect advanced liver disease in the community: model derivation and validation using UK primary and secondary care data.

Theresa Hydes, Michael Moore, Beth Stuart, Miranda Kim, Fangzhong Su, Colin Newell, David Cable, Alan Hales, Nick Sheron

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMJ open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.5field-weighted citation impact, top 18% 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

3 citing papers in PubMed, 15 citations in OpenAlex.

  1. Review
  2. Article
  3. Observational
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 4 institutions in 1 country.

Theresa HydesSchool of Primary Care and Population Sciences, University of Southampton, Southampton, Hampshire, UK.ORCID 0000-0002-7768-6886
Michael MooreSchool of Primary Care and Population Sciences, University of Southampton, Southampton, Hampshire, UK.ORCID 0000-0002-5127-4509
Beth StuartSchool of Primary Care and Population Sciences, University of Southampton, Southampton, Hampshire, UK.
Miranda KimSchool of Primary Care and Population Sciences, University of Southampton, Southampton, Hampshire, UK.
Fangzhong SuSchool of Primary Care and Population Sciences, University of Southampton, Southampton, Hampshire, UK.
Colin NewellHuman Development and Health, University of Southampton Faculty of Medicine, Southampton, Southampton, UK.
David CableInformatics, University Hospital Southampton NHS Foundation Trust, Southampton, Southampton, UK.
Alan HalesSchool of Primary Care and Population Sciences, University of Southampton, Southampton, Hampshire, UK.
Nick SheronThe Institute of Hepatology, Foundation for Liver Research, London, UK nicksheronwork@outlook.com.
University of Southampton · GBFoundation for Liver Research · GBNIHR Southampton Biomedical Research Centre · GBUniversity Hospital Southampton NHS Foundation Trust · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesMost patients are unaware they have liver cirrhosis until they present with a decompensating event. We therefore aimed to develop and validate an algorithm to predict advanced liver disease (AdvLD) using data widely available in primary care. DESIGN, SETTING AND

participantsLogistic regression was performed on routinely collected blood result data from the University Hospital Southampton (UHS) information systems for 16 967 individuals who underwent an upper gastrointestinal endoscopy (2005-2016). Data were used to create a model aimed at detecting AdvLD: 'CIRRhosis Using Standard tests' (CIRRUS). Prediction of a first serious liver event (SLE) was then validated in two cohorts of 394 253 (UHS: primary and secondary care) and 183 045 individuals (Care and Health Information Exchange (CHIE): primary care). PRIMARY OUTCOME MEASURES: Model creation dataset: cirrhosis or portal hypertension. Validation datasets: SLE (gastro-oesophageal varices, liver-related ascites or cirrhosis).

resultsIn the model creation dataset, 931 SLEs were recorded (5.5%). CIRRUS detected cirrhosis or portal hypertension with an area under the curve (AUC) of 0.90 (95% CI 0.88 to 0.92). Overall, 3044 (0.8%) and 1170 (0.6%) SLEs were recorded in the UHS and CHIE validation cohorts, respectively. In the UHS cohort, CIRRUS predicted a first SLE within 5 years with an AUC of 0.90 (0.89 to 0.91) continuous, 0.88 (0.87 to 0.89) categorised (crimson, red, amber, green grades); and AUC 0.84 (0.82 to 0.86) and 0.83 (0.81 to 0.85) for the CHIE cohort. In patients with a specified liver risk factor (alcohol, diabetes, viral hepatitis), a crimson/red cut-off predicted a first SLE with a sensitivity of 72%/59%, specificity 87%/93%, positive predictive value 26%/18% and negative predictive value 98%/99% for the UHS/CHIE validation cohorts, respectively.

conclusionIdentification of individuals at risk of AdvLD within primary care using routinely available data may provide an opportunity for earlier intervention and prevention of liver-related morbidity and mortality.

Indexed as

Esophageal and Gastric VaricesSecondary Health CareHematologic TestsHumansLiver CirrhosisUnited Kingdomhepatobiliary diseasehepatologypreventive medicineprimary care

Identifiers

PMID33574154
PMCPMC7925927
OpenAlexW3126457181

What OpenQuestion holds

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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.