{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Wyatt B"],"funding":["NIOSH CDC HHS","Gilead Sciences"],"pagination":["2974-2987"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9299620"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["74(6)"],"pubmed_abstract":["<h4>Background and aims</h4>Although chronic HCV infection increases mortality, thousands of patients remain diagnosed-but-untreated (DBU). We aimed to (1) develop a DBU phenotyping algorithm, (2) use it to facilitate case finding and linkage to care, and (3) identify barriers to successful treatment.<h4>Approach and results</h4>We developed a phenotyping algorithm using Java and SQL and applied it to ~2.5 million EPIC electronic medical records (EMRs; data entered January 2003 to December 2017). Approximately 72,000 EMRs contained an HCV International Classification of Diseases code and/or diagnostic test. The algorithm classified 10,614 cases as DBU (HCV-RNA positive and alive). Its positive and negative predictive values were 88% and 97%, respectively, as determined by manual review of "],"journal":["Hepatology (Baltimore, Md.)"],"pubmed_title":["A Digital Case-Finding Algorithm for Diagnosed but Untreated Hepatitis C: A Tool for Increasing Linkage to Treatment and Cure."],"pmcid":["PMC9299620"],"funding_grant_id":["U01 OH011489","U01 OH012263"],"pubmed_authors":["Miller M","Wyatt B","Mageras A","Ma N","Dinani A","Dieterich D","Jeon J","Li L","Bowman CA","Paulino L","Collado F","Perumalswami PV","Harty A","Vandromme M","Branch AD"],"additional_accession":[]},"is_claimable":false,"name":"A Digital Case-Finding Algorithm for Diagnosed but Untreated Hepatitis C: A Tool for Increasing Linkage to Treatment and Cure.","description":"<h4>Background and aims</h4>Although chronic HCV infection increases mortality, thousands of patients remain diagnosed-but-untreated (DBU). We aimed to (1) develop a DBU phenotyping algorithm, (2) use it to facilitate case finding and linkage to care, and (3) identify barriers to successful treatment.<h4>Approach and results</h4>We developed a phenotyping algorithm using Java and SQL and applied it to ~2.5 million EPIC electronic medical records (EMRs; data entered January 2003 to December 2017). Approximately 72,000 EMRs contained an HCV International Classification of Diseases code and/or diagnostic test. The algorithm classified 10,614 cases as DBU (HCV-RNA positive and alive). Its positive and negative predictive values were 88% and 97%, respectively, as determined by manual review of ","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Dec","modification":"2025-04-05T15:04:20.584Z","creation":"2022-08-06T14:48:04.209Z"},"accession":"S-EPMC9299620","cross_references":{"pubmed":["34333777"],"doi":["10.1002/hep.32086"]}}