Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Observational Study
. 2019 Jul 9;9(7):e028571.
doi: 10.1136/bmjopen-2018-028571.

Validation of an algorithm using inpatient electronic health records to determine the presence and severity of cirrhosis in patients with hepatocellular carcinoma in England: an observational study

Robert J Driver  1   2   3 , Vinay Balachandrakumar  4 , Anya Burton  5 , Jessica Shearer  2 , Amy Downing  1   3 , Tim Cross  4 , Eva Morris  1   3 , Ian A Rowe  1   2   3
Affiliations
Observational Study

Validation of an algorithm using inpatient electronic health records to determine the presence and severity of cirrhosis in patients with hepatocellular carcinoma in England: an observational study

Robert J Driver et al. BMJ Open. .

Abstract

Objectives: Outcomes in hepatocellular carcinoma (HCC) are determined by both cancer characteristics and liver disease severity. This study aims to validate the use of inpatient electronic health records to determine liver disease severity from treatment and procedure codes.

Design: Retrospective observational study.

Setting: Two National Health Service (NHS) cancer centres in England.

Participants: 339 patients with a new diagnosis of HCC between 2007 and 2016.

Main outcome: Using inpatient electronic health records, we have developed an optimised algorithm to identify cirrhosis and determine liver disease severity in a population with HCC. The diagnostic accuracy of the algorithm was optimised using clinical records from one NHS Trust and it was externally validated using anonymised data from another centre.

Results: The optimised algorithm has a positive predictive value (PPV) of 99% for identifying cirrhosis in the derivation cohort, with a sensitivity of 86% (95% CI 82% to 90%) and a specificity of 98% (95% CI 96% to 100%). The sensitivity for detecting advanced stage cirrhosis is 80% (95% CI 75% to 87%) and specificity is 98% (95% CI 96% to 100%), with a PPV of 89%.

Conclusions: Our optimised algorithm, based on inpatient electronic health records, reliably identifies and stages cirrhosis in patients with HCC. This highlights the potential of routine health data in population studies to stratify patients with HCC according to liver disease severity.

Keywords: epidemiology; hepatobiliary tumours; hepatology.

PubMed Disclaimer

Conflict of interest statement

Competing interests: VB has received an unrestricted educational grant from Bayer, Bristol-Myers Squibb and Sirtex.

Figures

Figure 1
Figure 1
Box-and-whisker plots showing the distribution of MELD scores (A) and pie graphs showing the distribution of Child-Pugh class (B) within compensated and decompensated cirrhosis groups determined by the algorithm. MELD, Model for End-Stage Liver Disease.
See this image and copyright information in PMC

References

    1. Cancer Research UK. Liver Cancer Statistics. 2015. https://www.cancerresearchuk.org/health-professional/cancer-statistics/s... (Cited 5 Jul 2018).
    1. El-Serag HB, Rudolph KL. Hepatocellular carcinoma: epidemiology and molecular carcinogenesis. Gastroenterology 2007;132:2557–76. 10.1053/j.gastro.2007.04.061 - DOI - PubMed
    1. D’Amico G, Garcia-Tsao G, Pagliaro L. Natural history and prognostic indicators of survival in cirrhosis: a systematic review of 118 studies. J Hepatol 2006;44:217–31. 10.1016/j.jhep.2005.10.013 - DOI - PubMed
    1. El-Serag HB. Hepatocellular carcinoma. N Engl J Med 2011;365:1118–27. 10.1056/NEJMra1001683 - DOI - PubMed
    1. Coleman MP, Forman D, Bryant H, et al. Cancer survival in Australia, Canada, Denmark, Norway, Sweden, and the UK, 1995–2007 (the International Cancer Benchmarking Partnership): an analysis of population-based cancer registry data. The Lancet 2011;377:127–38. 10.1016/S0140-6736(10)62231-3 - DOI - PMC - PubMed

Publication types

MeSH terms