Evidence map›Paper›PMID 34333777›Full record

ArticleHepatology (Baltimore, Md.)2021

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

Brooke Wyatt, Ponni V Perumalswami, Anna Mageras, Mark Miller, Alyson Harty, Ning Ma, Chip A Bowman, Francina Collado, Jihae Jeon, Lismeiry Paulino and 5 more

Abstract read
In one paragraph

Article in Hepatology (Baltimore, Md.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Brooke WyattDivision of Liver Diseases, Icahn School of Medicine Mount Sinai, New York, NY.
Ponni V PerumalswamiDivision of Liver Diseases, Icahn School of Medicine Mount Sinai, New York, NY.
Anna MagerasDivision of Liver Diseases, Icahn School of Medicine Mount Sinai, New York, NY.
Mark MillerDivision of Liver Diseases, Icahn School of Medicine Mount Sinai, New York, NY.
Alyson HartyDivision of Liver Diseases, Icahn School of Medicine Mount Sinai, New York, NY.
Ning MaDivision of Liver Diseases, Icahn School of Medicine Mount Sinai, New York, NY.
Chip A BowmanDepartment of Medicine, Icahn School of Medicine Mount Sinai, New York, NY.ORCID 0000-0001-8293-0145
Francina ColladoDivision of Liver Diseases, Icahn School of Medicine Mount Sinai, New York, NY.
Jihae JeonDivision of Liver Diseases, Icahn School of Medicine Mount Sinai, New York, NY.
Lismeiry PaulinoDivision of Liver Diseases, Icahn School of Medicine Mount Sinai, New York, NY.
Amreen DinaniDivision of Liver Diseases, Icahn School of Medicine Mount Sinai, New York, NY.
Douglas DieterichDivision of Liver Diseases, Icahn School of Medicine Mount Sinai, New York, NY.
Li LiDivision of Liver Diseases, Icahn School of Medicine Mount Sinai, New York, NY.
Maxence VandrommeDivision of Liver Diseases, Icahn School of Medicine Mount Sinai, New York, NY.
Andrea D BranchDivision of Liver Diseases, Icahn School of Medicine Mount Sinai, New York, NY.

Funding

Hepatotoxic Exposures, Progressive Fatty Liver Disease (NASH), and Liver Cancer Risk in the World Trade Center Health Program General Responder CohortU01OH011489 · OH · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI BRANCH, ANDREA D. · 2017 to 2020
$2.4M
Evidence of Toxicant-associated Fatty Liver Disease in WTC RespondersU01OH012263 · OH · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI BRANCH, ANDREA D. · 2021 to 2023
$1.5M
NIOSH CDC HHS U01 OH011489NIOSH CDC HHS U01 OH012263
6 · The paper itself

Abstract

BACKGROUND AND

aimsAlthough 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

resultsWe 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).

conclusionsThe 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.

Indexed as

AlgorithmsAgedAntiviral AgentsElectronic Health RecordsFeasibility StudiesFemaleHepatitis C, ChronicHumansInformation Storage and RetrievalMaleMiddle AgedAntiviral Agents

Identifiers

PMID34333777
PMCPMC9299620

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.