Using surveillance data to determine treatment rates and outcomes for patients with chronic hepatitis C virus infection

The aim of this work was to develop and validate an algorithm to monitor rates of, and response to, treatment of patients infected with hepatitis C virus (HCV) across England using routine laboratory HCV RNA testing data. HCV testing activity between January 2002 and December 2011 was extracted from...

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Main Authors: Lattimore, Sam, Irving, William L., Collins, Sarah, Penman, Celia, Ramsay, Mary
Format: Article
Published: Wiley 2014
Online Access:https://eprints.nottingham.ac.uk/35703/
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author Lattimore, Sam
Irving, William L.
Collins, Sarah
Penman, Celia
Ramsay, Mary
author_facet Lattimore, Sam
Irving, William L.
Collins, Sarah
Penman, Celia
Ramsay, Mary
author_sort Lattimore, Sam
building Nottingham Research Data Repository
collection Online Access
description The aim of this work was to develop and validate an algorithm to monitor rates of, and response to, treatment of patients infected with hepatitis C virus (HCV) across England using routine laboratory HCV RNA testing data. HCV testing activity between January 2002 and December 2011 was extracted from the local laboratory information systems of a sentinel network of 23 laboratories across England. An algorithm based on frequency of HCV RNA testing within a defined time period was designed to identify treated patients. Validation of the algorithm was undertaken for one center by comparison with treatment data recorded in a clinical database managed by the Trent HCV Study Group. In total, 267,887 HCV RNA test results from 100,640 individuals were extracted. Of these, 78.9% (79,360) tested positive for viral RNA, indicating an active infection, 20.8% (16,538) of whom had a repeat pattern of HCV RNA testing suggestive of treatment monitoring. Annual numbers of individuals treated increased rapidly from 468 in 2002 to 3,295 in 2009, but decreased to 3,110 in 2010. Approximately two thirds (63.3%; 10,468) of those treated had results consistent with a sustained virological response, including 55.3% and 67.1% of those with a genotype 1 and non-1 virus, respectively. Validation against the Trent clinical database demonstrated that the algorithm was 95% sensitive and 93% specific in detecting treatment and 100% sensitive and 93% specific for detecting treatment outcome. Conclusions: Laboratory testing activity, collected through a sentinel surveillance program, has enabled the first country-wide analysis of treatment and response among HCV-infected individuals. Our approach provides a sensitive, robust, and sustainable method for monitoring service provision across England
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spelling nottingham-357032020-05-04T16:44:24Z https://eprints.nottingham.ac.uk/35703/ Using surveillance data to determine treatment rates and outcomes for patients with chronic hepatitis C virus infection Lattimore, Sam Irving, William L. Collins, Sarah Penman, Celia Ramsay, Mary The aim of this work was to develop and validate an algorithm to monitor rates of, and response to, treatment of patients infected with hepatitis C virus (HCV) across England using routine laboratory HCV RNA testing data. HCV testing activity between January 2002 and December 2011 was extracted from the local laboratory information systems of a sentinel network of 23 laboratories across England. An algorithm based on frequency of HCV RNA testing within a defined time period was designed to identify treated patients. Validation of the algorithm was undertaken for one center by comparison with treatment data recorded in a clinical database managed by the Trent HCV Study Group. In total, 267,887 HCV RNA test results from 100,640 individuals were extracted. Of these, 78.9% (79,360) tested positive for viral RNA, indicating an active infection, 20.8% (16,538) of whom had a repeat pattern of HCV RNA testing suggestive of treatment monitoring. Annual numbers of individuals treated increased rapidly from 468 in 2002 to 3,295 in 2009, but decreased to 3,110 in 2010. Approximately two thirds (63.3%; 10,468) of those treated had results consistent with a sustained virological response, including 55.3% and 67.1% of those with a genotype 1 and non-1 virus, respectively. Validation against the Trent clinical database demonstrated that the algorithm was 95% sensitive and 93% specific in detecting treatment and 100% sensitive and 93% specific for detecting treatment outcome. Conclusions: Laboratory testing activity, collected through a sentinel surveillance program, has enabled the first country-wide analysis of treatment and response among HCV-infected individuals. Our approach provides a sensitive, robust, and sustainable method for monitoring service provision across England Wiley 2014-03-24 Article PeerReviewed Lattimore, Sam, Irving, William L., Collins, Sarah, Penman, Celia and Ramsay, Mary (2014) Using surveillance data to determine treatment rates and outcomes for patients with chronic hepatitis C virus infection. Hepatology, 59 (4). pp. 1343-1350. ISSN 1527-3350 http://dx.doi.org/10.1002/hep.26926 doi:10.1002/hep.26926 doi:10.1002/hep.26926
spellingShingle Lattimore, Sam
Irving, William L.
Collins, Sarah
Penman, Celia
Ramsay, Mary
Using surveillance data to determine treatment rates and outcomes for patients with chronic hepatitis C virus infection
title Using surveillance data to determine treatment rates and outcomes for patients with chronic hepatitis C virus infection
title_full Using surveillance data to determine treatment rates and outcomes for patients with chronic hepatitis C virus infection
title_fullStr Using surveillance data to determine treatment rates and outcomes for patients with chronic hepatitis C virus infection
title_full_unstemmed Using surveillance data to determine treatment rates and outcomes for patients with chronic hepatitis C virus infection
title_short Using surveillance data to determine treatment rates and outcomes for patients with chronic hepatitis C virus infection
title_sort using surveillance data to determine treatment rates and outcomes for patients with chronic hepatitis c virus infection
url https://eprints.nottingham.ac.uk/35703/
https://eprints.nottingham.ac.uk/35703/
https://eprints.nottingham.ac.uk/35703/