Evidence map›Paper›PMID 37121437›Full record

SynthesisJournal of hepatology2023

An individual patient data meta-analysis to determine cut-offs for and confounders of NAFLD-fibrosis staging with magnetic resonance elastography.

Jia-Xu Liang, Javier Ampuero, Hao Niu, Kento Imajo, Mazen Noureddin, Jaideep Behari, Dae Ho Lee, Richard L Ehman, Fredrik Rorsman, Johan Vessby and 9 more

Open access · hybridAbstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in Journal of hepatology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
81citing papers in PubMed, 5 pooled it
17.6field-weighted citation impact, top 1% 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

81 citing papers in PubMed, 5 syntheses or guidelines pooled it, 82 citations in OpenAlex.

  1. Guideline
  2. Pooled it
  3. Guideline
  4. Pooled it
  5. Guideline
  6. Trial
  7. Article
  8. Review
  9. Article
  10. Review
  11. Review
  12. Article
  13. Article
  14. Review
  15. Review
  16. Review
  17. Article
  18. Review
  19. Review
  20. Review

21 more citing papers are in PubMed but not listed here.

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

19 authors at 14 institutions in 7 countries.

Jia-Xu LiangDigestive Diseases Unit and CIBERehd, Virgen del Rocío University Hospital, Seville, Spain; Institute of Biomedicine of Seville (HUVR/CSIC/US), University of Seville, Seville, Spain; Department of Diagnostic Radiology, The Fifth Clinical Medical College of Henan University of Chinese Medicine (Zhengzhou People's Hospital), Zhengzhou, China.
Javier AmpueroDigestive Diseases Unit and CIBERehd, Virgen del Rocío University Hospital, Seville, Spain; Institute of Biomedicine of Seville (HUVR/CSIC/US), University of Seville, Seville, Spain.
Hao NiuDigestive System and Clinical Pharmacology Unit, Virgen de la Victoria University Hospital, Biomedical Research Institute of Malaga and Nanomedicine Platform-IBIMA (Plataforma BIONAND), University of Malaga, Málaga, Spain; Biomedical Research Network Center for Hepatic and Digestive Diseases (CIBERehd), Carlos III Health Institute, Madrid, Spain.
Kento ImajoDepartment of Gastroenterology, Yokohama City University Graduate School of Medicine; Yokohama, Japan.
Mazen NoureddinFatty Liver Program, Division of Digestive and Liver Diseases, Comprehensive Transplant Program, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Jaideep BehariDepartment of Medicine, Division of Gastroenterology, Hepatology and Nutrition, Center for Liver Diseases, University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
Dae Ho LeeDepartment of Internal Medicine, Gachon University College of Medicine (Gachon University Gil Medical Center), Incheon, South Korea.
Richard L EhmanDepartment of Diagnostic Radiology, Mayo Clinic College of Medicine, Rochester, MN, USA.
Fredrik RorsmanDepartment of Medical Sciences, Section of Gastroenterology and Hepatology, Uppsala University, Uppsala, Sweden.
Johan VessbyDepartment of Medical Sciences, Section of Gastroenterology and Hepatology, Uppsala University, Uppsala, Sweden.
Juan R LacalleBiostatistics Unit, Department of Preventive Medicine and Public Health, University of Seville, Seville, Spain.
Ferenc E MózesOxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.
Michael PavlidesOxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK; Translational Gastroenterology Unit, University of Oxford, Oxford, UK.
Quentin M AnsteeTranslational and Clinical Research Institute; Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, UK; Newcastle NIHR Biomedical Research Centre, Newcastle Upon Tyne Hospitals, NHS Trust, Newcastle Upon Tyne, UK.
Stephen A HarrisonOxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.
Javier CastellDepartment of Radiology, Virgen del Rocío University Hospital, Seville, Spain.
Rohit LoombaDivision of Epidemiology, Department of Family Medicine and Public Health, University of California San Diego, La Jolla, CA, USA; NAFLD Research Center, Division of Gastroenterology and Hepatology, Department of Medicine, University of California San Diego, La Jolla, CA, USA.
Manuel Romero-GómezDigestive Diseases Unit and CIBERehd, Virgen del Rocío University Hospital, Seville, Spain; Institute of Biomedicine of Seville (HUVR/CSIC/US), University of Seville, Seville, Spain. Electronic address: mromerogomez@us.es.
LITMUS Consortium Investigators
John Radcliffe Hospital · GBCentro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas · ESUppsala University · SECedars-Sinai Medical Center · USClinical Research Institute · USFifth Affiliated Hospital of Zhengzhou University · CNGachon University Gil Medical Center · KRHospital Universitario Virgen del Rocío · ESInstituto de Salud Carlos III · ESMayo Clinic · USUniversidad de Sevilla · ESUniversity of California San Diego · USUniversity of Pittsburgh Medical Center · USYokohama City University · JP

Funding

UC San Diego Clinical and Translational Research InstituteUL1TR001442 · NCATS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI FIRESTEIN, GARY S, HOGARTH, MICHAEL · 2015 to 2024
$88.3M
Pediatric Trials in Non-Alcoholic Steatohepatitis (NASH)U01DK061734 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI ROHIT LOOMBA · 2002 to 2026
$24.4M
Tissue-specific roles of FXR in CVD and NASHP01HL147835 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI LOOMBA, ROHIT · 2020 to 2024
$12.2M
San Diego Digestive Diseases Research CenterP30DK120515 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI LARS ECKMANN, Bernd G. Schnabl · 2019 to 2026
$10.8M
HIV MASLD Clinical Research Network (HCRN)R01DK121378 · NIDDK · INDIANA UNIVERSITY INDIANAPOLIS · PI NAGA P CHALASANI, ROHIT LOOMBA · 2020 to 2026
$8.7M
Magnetic Resonance ElastographyR37EB001981 · NIBIB · MAYO CLINIC ROCHESTER · PI EHMAN, RICHARD L. · 2016 to 2025
$4.0M
Novel IL-23 inhibitor for the treatment of alcohol associated liver diseaseU01AA029019 · NIAAA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI KISSELEVA, TATIANA, LOOMBA, ROHIT · 2020 to 2024
$3.7M
Novel Determinants for Progression of Non-Alcoholic Fatty Liver Disease to Hepatocellular Carcinoma and Other Health OutcomesR01CA255809 · NCI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI BEHARI, JAIDEEP, YUAN, JIAN-MIN · 2021 to 2025
$3.7M
QUS Technology for Diagnosis and Grading of Hepatic Steatosis in NAFLDR01DK106419 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI LOOMBA, ROHIT, SIRLIN, CLAUDE B · 2015 to 2019
$3.4M
San Diego Cirrhosis Clinical Research NetworkU01DK130190 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI ROHIT LOOMBA · 2021 to 2026
$2.3M
A vascularized patient-derived iPSC liver acinus microphysiology system as an innovative precision medicine platform for optimizing clinical trial design for nonalcoholic fatty liver diseaseUH3TR003289 · NCATS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI BEHARI, JAIDEEP, SOTO-GUTIERREZ, ALEJANDRO · 2022 to 2024
$2.3M
Alcoholic Hepatitis Clinical and Translational Network Late Phase Clinical Trials and Observational Studies 7/9U01AA026978 · NIAAA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI BEHARI, JAIDEEP · 2018 to 2022
$1.8M
Department of HealthNCATS NIH HHS UH3 TR003289NCATS NIH HHS UL1 TR001442NCI NIH HHS R01 CA255809NHLBI NIH HHS P01 HL147835NIAAA NIH HHS U01 AA026978NIAAA NIH HHS U01 AA029019NIBIB NIH HHS R37 EB001981NIDDK NIH HHS P30 DK120515NIDDK NIH HHS R01 DK106419NIDDK NIH HHS R01 DK121378NIDDK NIH HHS R01 DK124318NIDDK NIH HHS U01 DK061734NIDDK NIH HHS U01 DK130190
6 · The paper itself

Abstract

BACKGROUND &

aimsWe conducted an individual patient data meta-analysis to establish stiffness cut-off values for magnetic resonance elastography (MRE) in staging liver fibrosis and to assess potential confounding factors.

methodsA systematic review of the literature identified studies reporting MRE data in patients with NAFLD. Data were obtained from the corresponding authors. The pooled diagnostic cut-off value for the various fibrosis stages was determined in a two-stage meta-analysis. Multilevel modelling methods were used to analyse potential confounding factors influencing the diagnostic accuracy of MRE in staging liver fibrosis.

resultsEight independent cohorts comprising 798 patients were included in the meta-analysis. The area under the receiver operating characteristic curve (AUROC) for MRE in detecting significant fibrosis was 0.92 (sensitivity, 79%; specificity, 89%). For advanced fibrosis, the AUROC was 0.92 (sensitivity, 87%; specificity, 88%). For cirrhosis, the AUROC was 0.94 (sensitivity, 88%, specificity, 89%). Cut-offs were defined to explore concordance between MRE and histopathology: ≥F2, 3.14 kPa (pretest probability, 39.4%); ≥F3, 3.53 kPa (pretest probability, 24.1%); and F4, 4.45 kPa (pretest probability, 8.7%). In generalized linear mixed model analysis, histological steatohepatitis with higher inflammatory activity (odds ratio 2.448, 95% CI 1.180-5.079, p <0.05) and high gamma-glutamyl transferase (GGT) concentration (>120U/L) (odds ratio 3.388, 95% CI 1.577-7.278, p <0.01] were significantly associated with elevated liver stiffness, and thus affecting accuracy in staging early fibrosis (F0-F1). Steatosis, as measured by magnetic resonance imaging proton density fat fraction, and body mass index(BMI) were not confounders.

conclusionsMRE has excellent diagnostic performance for significant, advanced fibrosis and cirrhosis in patients with NAFLD. Elevated inflammatory activity and GGT level may lead to overestimation of early liver fibrosis, but anthropometric measures such as BMI or the degree of steatosis do not. IMPACT AND IMPLICATIONS: This individual patient data meta-analysis of eight international cohorts, including 798 patients, demonstrated that MRE achieves excellent diagnostic accuracy for significant, advanced fibrosis and cirrhosis in patients with NAFLD. Cut-off values (significant fibrosis, 3.14 kPa; advanced fibrosis, 3.53 kPa; and cirrhosis, 4.45 kPa) were established. Elevated inflammatory activity and gamma-glutamyltransferase level may affect the diagnostic accuracy of MRE, leading to overestimation of liver fibrosis in early stages. We observed no impact of diabetes, obesity, or any other metabolic disorder on the diagnostic accuracy of MRE.

Indexed as

Elasticity Imaging TechniquesNon-alcoholic Fatty Liver DiseaseFibrosisHumansLiverLiver CirrhosisMagnetic Resonance ImagingROC CurveElastographyFibrosisGGTMagnetic ResonanceNAFLDNASH

Identifiers

PMID37121437
PMCPMC10623141
OpenAlexW4367301730

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.