ABSTRACT Nucleos/tide analogue (NA) drugs are used for long‐term treatment of chronic hepatitis B virus (HBV) infection, with treatment eligibility criteria changing rapidly amidst globally evolving clinical guidelines. We aimed to quantify the prescription of NA drugs to date, and to undertake a preliminary assessment of the impact of relaxing treatment eligibility thresholds, leveraging a unique large real‐world secondary care dataset. We assimilated longitudinal clinical data, collected between February 1997 and April 2023 from adults with chronic HBV infection from six centres in England through the UK NIHR Health Informatics Collaborative (HIC) Viral Hepatitis and Liver Disease theme. We describe factors currently associated with the receipt of NA treatment and determine the proportion of the population who would become treatment eligible as thresholds change. Across 7558 adults with a mean follow‐up of 4.0 years (SD 3.9), NA treatment was prescribed in 2014/7558 (26.6%), and as expected according to guidelines at the time, was associated with HBV e‐antigen (HBeAg) positivity and alanine transferase (ALT) above the upper limit of normal (&gt; ULN). Treatment was more likely in males, older adults, in Asian and Other ethnicities (compared to White), and less likely in socioeconomically deprived individuals. The proportion of treatment‐eligible individuals was 32.3% based on 2 records of ALT &gt; ULN over 6–12 months, 41.7% based on ALT &gt; ULN and viral load (VL) &gt; 2000 IU/mL, and 95.1% based on detectable VL and either ALT &gt; ULN or age &gt; 30 years. Evolving clinical guidelines will lead to substantial increases in the proportion of individuals living with HBV who are eligible for treatment, underlining the need for services to adapt rapidly to the changing clinical environment. 

Hepatitis B Virus ( <scp>HBV</scp> ) Treatment Eligibility in the <scp>UK</scp> : Retrospective Longitudinal Cohort Data to Explore the Impact of Changes in Clinical Guidelines

Background: The National Institute for Health Research Health Informatics Collaborative (NIHR HIC) viral hepatitis theme is working to overcome governance and data challenges to collate routine clinical data from electronic patients records from multiple UK hospital sites for translational research. The development of hepatocellular carcinoma (HCC) is a critical outcome for patients with viral hepatitis with the drivers of cancer transformation poorly understood. Objective: This study aims to develop a natural language processing (NLP) algorithm for automatic HCC identification from imaging reports to facilitate studies into HCC. Methods: 1140 imaging reports were retrieved from the NIHR HIC viral hepatitis research database v1.0. These reports were from two sites, one used for method development (site 1) and the other for validation (site 2). Reports were initially manually annotated as binary classes (HCC vs. non-HCC). We designed inference rules for recognising HCC presence, wherein medical terms for eligibility criteria of HCC were determined by domain experts. A rule-based NLP algorithm with five submodules (regular expressions of medical terms, terms recognition, negation detection, sentence tagging, and report label generation) was developed and iteratively tuned. Results: Our rule-based algorithm achieves an accuracy of 99.85% (sensitivity: 90%, specificity: 100%) for identifying HCC on the development set and 99.59% (sensitivity: 100%, specificity: 99.58%) on the validation set. This method outperforms several off-the-shelf models on HCC identification including 'machine learning based' and 'deep learning based' text classifiers in achieving significantly higher sensitivity. Conclusion: Our rule-based NLP method gives high sensitivity and high specificity for HCC identification, even from imbalanced datasets with a small number positive cases, and can be used to rapidly screen imaging reports, at large-scale to facilitate epidemiological and clinical studies into HCC.

Identifying Hepatocellular Carcinoma from imaging reports using natural language processing to facilitate data extraction from electronic patient records

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Identifying Hepatocellular Carcinoma from imaging reports using natural language processing to facilitate data extraction from electronic patient records