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*, Vinay Balachandrakumar, Anya Burton, Jessica Shearer, Amy Downing, Tim Cross, Eva Morris, Ian A. Rowe

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

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.

Original languageEnglish
Article numbere028571
JournalBMJ Open
Volume9
Issue number7
DOIs
Publication statusPublished - 1 Jul 2019
Externally publishedYes

Bibliographical note

Funding Information:
The strengths of this study are the systematic development of an algorithm which uses routinely available inpatient episode codes, and its applicability to large population studies in HCC. These patients often require hospital admission to manage complications in advanced cirrhosis and to receive HCC therapies, or day case procedures such as paracentesis and endoscopy which are also coded in the HES dataset. The high-performance characteristics (particularly the PPVs) derived from inpatient codes here are in part a consequence of the high pretest probability of cirrhosis in patients with HCC. This observation is supported by the improved PPVs seen in existing algorithms in our cohort. In summary, this suggests that inpatient episodes are sufficient for high-quality analyses of the impact of cirrhosis and its severity on the outcomes of patients with HCC.

Publisher Copyright:
© 2019 Author(s).

Keywords

  • epidemiology
  • hepatobiliary tumours
  • hepatology

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