Evidence map›Paper›PMID 29108017›Full record

ArticlePloS one2017

Assessing risk of fibrosis progression and liver-related clinical outcomes among patients with both early stage and advanced chronic hepatitis C.

Monica A Konerman, Dongxia Lu, Yiwei Zhang, Mary Thomson, Ji Zhu, Aashesh Verma, Boang Liu, Nizar Talaat, Ulysses Balis, Peter D R Higgins and 2 more

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 2 pooled it
1.7field-weighted citation impact, top 15% of its field
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

13 citing papers in PubMed, 2 syntheses or guidelines pooled it, 35 citations in OpenAlex.

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  13. Artificial Intelligence in Hepatology- Ready for the Primetime.Journal of clinical and experimental hepatology
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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

12 authors at 3 institutions in 1 country.

Monica A KonermanUniversity of Michigan Health System, Division of Gastroenterology and Hepatology, Ann Arbor, Michigan, United States of America.ORCID http://orcid.org/0000-0002-8381-6149
Dongxia LuUniversity of Michigan Health System, Division of Gastroenterology and Hepatology, Ann Arbor, Michigan, United States of America.
Yiwei ZhangUniversity of Michigan Health System, Division of Gastroenterology and Hepatology, Ann Arbor, Michigan, United States of America.
Mary ThomsonUniversity of Michigan Health System, Division of Gastroenterology and Hepatology, Ann Arbor, Michigan, United States of America.
Ji ZhuUniversity of Michigan Health System, Division of Gastroenterology and Hepatology, Ann Arbor, Michigan, United States of America.
Aashesh VermaUniversity of Michigan Health System, Department of Internal Medicine, Ann Arbor, Michigan, United States of America.
Boang LiuUniversity of Michigan Health System, Division of Gastroenterology and Hepatology, Ann Arbor, Michigan, United States of America.
Nizar TalaatUniversity of Michigan Health System, Division of Gastroenterology and Hepatology, Ann Arbor, Michigan, United States of America.
Ulysses BalisUniversity of Michigan Health System, Division of Pathology, Ann Arbor, Michigan, United States of America.
Peter D R HigginsUniversity of Michigan Health System, Division of Gastroenterology and Hepatology, Ann Arbor, Michigan, United States of America.
Anna S F LokUniversity of Michigan Health System, Division of Gastroenterology and Hepatology, Ann Arbor, Michigan, United States of America.
Akbar K WaljeeUniversity of Michigan Health System, Division of Gastroenterology and Hepatology, Ann Arbor, Michigan, United States of America.
Michigan United · USHealth Services Research & Development · USUniversity of Michigan · US

Funding

Training Program in Gastrointestinal EpidemiologyT32DK062708 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Peter D.R. Higgins, Elliot Tapper · 2003 to 2026
$3.9M
Application of Machine Learning Algorithms to Thiopurine Monitoring in IBDR01GM097117 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI HIGGINS, PETER D.R. · 2012 to 2016
$1.9M
NIDDK NIH HHS T32 DK062708NIGMS NIH HHS R01 GM097117
6 · The paper itself

Abstract

objectiveAssessing risk of adverse outcomes among patients with chronic liver disease has been challenging due to non-linear disease progression. We previously developed accurate prediction models for fibrosis progression and clinical outcomes among patients with advanced chronic hepatitis C (CHC). The primary aim of this study was to validate fibrosis progression and clinical outcomes models among a heterogeneous patient cohort.

designAdults with CHC with ≥3 years follow-up and without hepatic decompensation, hepatocellular carcinoma (HCC), liver transplant (LT), HBV or HIV co-infection at presentation were analyzed (N = 1007). Outcomes included: 1) fibrosis progression 2) hepatic decompensation 3) HCC and 4) LT-free survival. Predictors included longitudinal clinical and laboratory data. Machine learning methods were used to predict outcomes in 1 and 3 years.

resultsThe external cohort had a median age of 49.4 years (IQR 44.3-54.3); 61% were male, 80% white, and 79% had genotype 1. At presentation, 73% were treatment naïve and 31% had cirrhosis. Fibrosis progression occurred in 34% over a median of 4.9 years (IQR 3.2-7.6). Clinical outcomes occurred in 22% over a median of 4.4 years (IQR 3.2-7.6). Model performance for fibrosis progression was limited due to small sample size. The area under the receiver operating characteristic curve (AUROC) for 1 and 3-year risk of clinical outcomes was 0.78 (95% CI 0.73-0.83) and 0.76 (95% CI 0.69-0.81).

conclusionAccurate assessments for risk of clinical outcomes can be obtained using routinely collected data across a heterogeneous cohort of patients with CHC. These methods can be applied to predict risk of progression in other chronic liver diseases.

Indexed as

AdultCohort StudiesDisease ProgressionFemaleHepatitis C, ChronicHumansLiver CirrhosisMachine LearningMaleMiddle AgedModels, TheoreticalRisk Factors

Identifiers

PMID29108017
PMCPMC5673203
OpenAlexW2767620514

What OpenQuestion holds

Textmetadata
LicenceCC0
Read underepoch 390

Registered trials

None linked

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.