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*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Objectives Most 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 participants Logistic 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: a € 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). Results In 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. Conclusion Identification 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.

Original languageEnglish
Article numbere044952
JournalBMJ Open
Issue number2
Publication statusPublished - 11 Feb 2021
Externally publishedYes

Bibliographical note

Funding Information:
1School of Primary Care and Population Sciences, University of Southampton, Southampton, UK 2Human Development and Health, University of Southampton Faculty of Medicine, Southampton, UK 3Southampton Biomedical Research Centre, Southampton, UK 4Informatics, University Hospital Southampton NHS Foundation Trust, Southampton, UK 5AH IT Solutions, Southampton, Hampshire, UK 6The Institute of Hepatology, Foundation for Liver Research, London, UK Acknowledgements The authors thank NHS South, Central and West Commissioning Support (SCWCSU) Unit and the Care and Health Information Exchange Information Governance Group (CHIEIGG) for their support and for provision of access to CHIE data. We would also like to thank Hugh Sanderson for the initial CHIE SQL queries, Mandy Lu for the help in digitising text-based pathology reports, and Hazel Inskip and Paul Roderick for advice on analysis. This research was funded by the British Liver Trust and Southampton Biomedical Research Centre.

Publisher Copyright:


  • hepatobiliary disease
  • hepatology
  • preventive medicine
  • primary care


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