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  1. Article ; Online: Automated phenotyping of patients with non-alcoholic fatty liver disease reveals clinically relevant disease subtypes.

    Vandromme, Maxence / Jun, Tomi / Perumalswami, Ponni / Dudley, Joel T / Branch, Andrea / Li, Li

    Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

    2020  Volume 25, Page(s) 91–102

    Abstract: Non-alcoholic fatty liver disease (NAFLD) is a complex heterogeneous disease which affects more than 20% of the population worldwide. Some subtypes of NAFLD have been clinically identified using hypothesis-driven methods. In this study, we used data ... ...

    Abstract Non-alcoholic fatty liver disease (NAFLD) is a complex heterogeneous disease which affects more than 20% of the population worldwide. Some subtypes of NAFLD have been clinically identified using hypothesis-driven methods. In this study, we used data mining techniques to search for subtypes in an unbiased fashion. Using electronic signatures of the disease, we identified a cohort of 13,290 patients with NAFLD from a hospital database. We gathered clinical data from multiple sources and applied unsupervised clustering to identify five subtypes among this cohort. Descriptive statistics and survival analysis showed that the subtypes were clinically distinct and were associated with different rates of death, cirrhosis, hepatocellular carcinoma, chronic kidney disease, cardiovascular disease, and myocardial infarction. Novel disease subtypes identified in this manner could be used to risk-stratify patients and guide management.
    MeSH term(s) Carcinoma, Hepatocellular/genetics ; Computational Biology ; Humans ; Liver Cirrhosis ; Liver Neoplasms/genetics ; Non-alcoholic Fatty Liver Disease/genetics
    Language English
    Publishing date 2020-01-08
    Publishing country United States
    Document type Journal Article
    ISSN 2335-6936
    ISSN (online) 2335-6936
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: A Digital Case-Finding Algorithm for Diagnosed but Untreated Hepatitis C: A Tool for Increasing Linkage to Treatment and Cure.

    Wyatt, Brooke / Perumalswami, Ponni V / Mageras, Anna / Miller, Mark / Harty, Alyson / Ma, Ning / Bowman, Chip A / Collado, Francina / Jeon, Jihae / Paulino, Lismeiry / Dinani, Amreen / Dieterich, Douglas / Li, Li / Vandromme, Maxence / Branch, Andrea D

    Hepatology (Baltimore, Md.)

    2021  Volume 74, Issue 6, Page(s) 2974–2987

    Abstract: 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) ... ...

    Abstract 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.
    MeSH term(s) Aged ; Algorithms ; Antiviral Agents/therapeutic use ; Electronic Health Records/statistics & numerical data ; Feasibility Studies ; Female ; Hepatitis C, Chronic/diagnosis ; Hepatitis C, Chronic/drug therapy ; Humans ; Information Storage and Retrieval/methods ; Male ; Middle Aged
    Chemical Substances Antiviral Agents
    Language English
    Publishing date 2021-07-29
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, P.H.S.
    ZDB-ID 604603-4
    ISSN 1527-3350 ; 0270-9139
    ISSN (online) 1527-3350
    ISSN 0270-9139
    DOI 10.1002/hep.32086
    Database MEDical Literature Analysis and Retrieval System OnLINE

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