TY - JOUR
T1 - A Digital Case-Finding Algorithm for Diagnosed but Untreated Hepatitis C
T2 - A Tool for Increasing Linkage to Treatment and Cure
AU - Wyatt, Brooke
AU - Perumalswami, Ponni V.
AU - Mageras, Anna
AU - Miller, Mark
AU - Harty, Alyson
AU - Ma, Ning
AU - Bowman, Chip A.
AU - Collado, Francina
AU - Jeon, Jihae
AU - Paulino, Lismeiry
AU - Dinani, Amreen
AU - Dieterich, Douglas
AU - Li, Li
AU - Vandromme, Maxence
AU - Branch, Andrea D.
N1 - Funding Information:
Supported, in part, by a grant from Gilead Sciences and from NIOSH/CDC 1U01OH011489‐01; 1 U01 OH012263‐01.
Funding Information:
Potential conflict of interest: NP Harty advises for Gilead. Dr. Dinani advises for and is on the speakers’ bureau for Intercept. She consults for Genfit and Expert Connect. She advises for Gilead. Dr. Dieterich consults for, advises for, and is on the speakers’ bureau for Gilead and AbbVie. He consults for Glycotest. Dr. Branch received grants from Gilead and Boehringer Ingelheim.
Publisher Copyright:
© 2021 The Authors. Hepatology published by Wiley Periodicals LLC on behalf of American Association for the Study of Liver Diseases.
PY - 2021/12
Y1 - 2021/12
N2 - Background and Aims: 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. Approach and Results: 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 500 EMRs randomly selected from the ~72,000. Navigators reviewed the charts of 6,187 algorithm-defined DBUs and they attempted to contact potential treatment candidates by phone. By June 2020, 30% (n = 1,862) had completed an HCV-related appointment. Outcomes analysis revealed that DBU patients enrolled in our care coordination program were more likely to complete treatment (72% [n = 219] vs. 54% [n = 256]; P < 0.001) and to have a verified sustained virological response (67% vs. 46%; P < 0.001) than other patients. Forty-eight percent (n = 2,992) of DBU patients could not be reached by phone, which was a major barrier to engagement. Nearly half of these patients had Fibrosis-4 scores ≥ 2.67, indicating significant fibrosis. Multivariable logistic regression showed that DBUs who could not be contacted were less likely to have private insurance than those who could (18% vs. 50%; P < 0.001). Conclusions: The digital DBU case-finding algorithm efficiently identified potential HCV treatment candidates, freeing resources for navigation and coordination. The algorithm is portable and accelerated HCV elimination when incorporated in our comprehensive program.
AB - Background and Aims: 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. Approach and Results: 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 500 EMRs randomly selected from the ~72,000. Navigators reviewed the charts of 6,187 algorithm-defined DBUs and they attempted to contact potential treatment candidates by phone. By June 2020, 30% (n = 1,862) had completed an HCV-related appointment. Outcomes analysis revealed that DBU patients enrolled in our care coordination program were more likely to complete treatment (72% [n = 219] vs. 54% [n = 256]; P < 0.001) and to have a verified sustained virological response (67% vs. 46%; P < 0.001) than other patients. Forty-eight percent (n = 2,992) of DBU patients could not be reached by phone, which was a major barrier to engagement. Nearly half of these patients had Fibrosis-4 scores ≥ 2.67, indicating significant fibrosis. Multivariable logistic regression showed that DBUs who could not be contacted were less likely to have private insurance than those who could (18% vs. 50%; P < 0.001). Conclusions: The digital DBU case-finding algorithm efficiently identified potential HCV treatment candidates, freeing resources for navigation and coordination. The algorithm is portable and accelerated HCV elimination when incorporated in our comprehensive program.
UR - http://www.scopus.com/inward/record.url?scp=85120349024&partnerID=8YFLogxK
U2 - 10.1002/hep.32086
DO - 10.1002/hep.32086
M3 - Article
C2 - 34333777
AN - SCOPUS:85120349024
SN - 0270-9139
VL - 74
SP - 2974
EP - 2987
JO - Hepatology
JF - Hepatology
IS - 6
ER -