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

Abstract

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: '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.

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