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Record W4379467566 · doi:10.1016/s2468-1253(23)00141-3

Performance of non-invasive tests and histology for the prediction of clinical outcomes in patients with non-alcoholic fatty liver disease: an individual participant data meta-analysis

2023· review· en· W4379467566 on OpenAlexafffund
Ferenc E. Mózes, Jenny Lee, Yasaman Vali, Osama Alzoubi, Katharina Staufer, Michael Trauner, Rafael Paternostro, Rudolf Stauber, Adriaan G. Holleboom, Anne‐Marieke van Dijk, Anne Linde Mak, Marc Loup, Toshihide Shima, Elisabetta Bugianesi, Silvia Gaia, Angelo Armandi, Monica Lupșor‐Platon, Vincent Wai‐Sun Wong, Guanlin Li, Grace Lai‐Hung Wong, Jeremy Cobbold, Thomas Karlas, Johannes Wiegand, Giada Sebastiani, Emmanuel Tsochatzis, Antonio Liguori, Masato Yoneda, Atsushi Nakajima, Hannes Hagström, Camilla Akbari, Masashi Hirooka, Wah‐Kheong Chan, Sanjiv Mahadeva, Ruveena Bhavani Rajaram, Ming‐Hua Zheng, Jacob George, Mohammed Eslam, Salvatore Petta, Grazia Pennisi, Mauro Viganò, Sofia Ridolfo, Guruprasad P. Aithal, Naaventhan Palaniyappan, Dae Ho Lee, Mattias Ekstedt, Patrik Nasr, Christophe Cassinotto, Victor de Lédinghen, Annalisa Berzigotti, Yuly P. Mendoza, Mazen Noureddin, Emily Truong, Céline Fournier‐Poizat, Andreas Geier, Miljen Martić, Theresa Tuthill, Quentin M. Anstee, Stephen A. Harrison, Patrick M. Bossuyt, Michael Pavlides, Ann K. Daly, Olivier Govaere, Simon Cockell, Dina Tiniakos, Pierre Bédossa, Alastair D. Burt, Fiona Oakley, Heather J. Cordell, Christopher P. Day, Kristy Wonders, Paolo Missier, Matthew McTeer, Luke Vale, Yemi Oluboyede, Matt Breckons, Hadi Zafarmand, Max Nieuwdorp, Joanne Verheij, Vlad Ratziu, Karine Clément, Rafael Patiño‐Navarrete, Raluca Pais, Valérie Paradis, Detlef Schuppan, Jörn M. Schattenberg, Rambabu Surabattula, Sudha Rani Myneni, Beate K. Straub, Toni Vidal-Puig, Michèle Vacca, Sergio Rodrigues-Cuenca, Mike Allison, Ioannis Kamzolas, Evangelia Petsalaki, Mark Campbell, Chris Lelliott, Matej Orešič, Tuulia Hyötyläinen, Aiden McGlinchey, José M. Mato, Óscar Millet, Jean‐François Dufour, Mojgan Masoodi, Stefan Neubauer, Salma Akhtar, Seliat Olodo-Atitebi, Rajarshi Banerjee, Matt Kelly, Elizabeth Shumbayawonda, Andrea Dennis, Anneli Andersson, Ioan Wigley, Manuel Romero‐Gómez, Emilio Gómez-González, Javier Ampuero, Javier Castell, Rocío Gallego‐Durán, Isabel Fernández-Lizaranzu, Rocío Montero‐Vallejo, M.A. Karsdal, Daniel Guldager Kring Rasmussen, Antonia Sinisi, Kishwar Musa, Estelle Sandt, Manuela Tonini, Chiara Rosso, Fabio Marra, Amalia Gastaldelli, Gianluca Svegliati, Sven Francque, Luisa Vonghia, Ann Driessen, Stergios Kechagias, Hannele Yki‐Järvinen, Kimmo Porthan, Johanna Arola, Saskia W. C. van Mil, George Papatheodoridis, Helena Cortez‐Pinto, Cecília M. P. Rodrigues, Luca Valenti, Serena Pelusi, Luca Miele, Christian Trautwein, Johanna Reißing, Susan Francis, Christopher R. Bradley, Paul Hockings, Moritz Schneider, Philip N. Newsome, Stefan G. Hübscher, David Wenn, Christian Rosenquist, Aldo Trylesinski, Rebeca Mayo, Cristina Alonso, Kevin L. Duffin, James W. Perfield, Yu Chen, Carla Yunis, Magdalena Alicia Harrington, Melissa Miller, Yan Chen, Euan McLeod, Trenton T. Ross, Barbara Bernardo, Corinna Schölch, Judith Ertle, Ramy Younes, Anouk Oldenburger, Harvey O. Coxson, Rachel Ostroff, Leigh Alexander, Hannah Biegel, Mette Skalshøi Kjær, Lea Mørch Harder, Peter K. Davidsen, Maria‐Magdalena Balp, Clifford A. Brass, Lori L. Jennings, Jürgen Löffler, Douglas Applegate, Sudha S. Shankar, Richard Torstenson, Daniel Lindén, Anne Llorca, Michael Kalutkiewicz, Kay Pepin, Richard L. Ehman, Gerald Horan, Gideon Ho, Dean Tai, Elaine Chng, Scott D. Patterson, Andrew N. Billin, Lynda Doward, James Twiss, Paresh Thakker, Zoltán Derdák, Henrik Landgren, Carolin Lackner, Annette S.H. Gouw, Prodromos Hytiroglou

Bibliographic record

Venue˜The œLancet. Gastroenterology & hepatology · 2023
Typereview
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsMcGill University Health Centre
FundersNovartis PharmaJulius ClinicalIpsenNovo NordiskEisaiUniversity of NottinghamInnovative Medicines InitiativeNovo Nordisk FondenNewcastle UniversityKorea Health Industry Development InstituteAbbott LaboratoriesCSL BehringNational Institute for Health and Care ResearchNIHR Newcastle Biomedical Research CentreInnovative Health InitiativeAmryt PharmaRoyal SocietyFonds de Recherche du Québec - SantéIntercept PharmaceuticalsEli Lilly and CompanyAllerganAstraZenecaWellcome TrustAlexion PharmaceuticalsServierGilead SciencesGlaxoSmithKlineNGM BiopharmaceuticalsKowa CompanyInventiva PharmaPfizerEuropean Federation of Pharmaceutical Industries and AssociationsSanofiMinistry of Health and Welfare
KeywordsMedicineInternal medicineCirrhosisGastroenterologyFatty liverTransient elastographySteatohepatitisClinical endpointHepatic encephalopathyLiver diseaseAscitesStage (stratigraphy)Clinical trialDiseaseLiver fibrosis

Abstract

fetched live from OpenAlex

BACKGROUND: Histologically assessed liver fibrosis stage has prognostic significance in patients with non-alcoholic fatty liver disease (NAFLD) and is accepted as a surrogate endpoint in clinical trials for non-cirrhotic NAFLD. Our aim was to compare the prognostic performance of non-invasive tests with liver histology in patients with NAFLD. METHODS: This was an individual participant data meta-analysis of the prognostic performance of histologically assessed fibrosis stage (F0-4), liver stiffness measured by vibration-controlled transient elastography (LSM-VCTE), fibrosis-4 index (FIB-4), and NAFLD fibrosis score (NFS) in patients with NAFLD. The literature was searched for a previously published systematic review on the diagnostic accuracy of imaging and simple non-invasive tests and updated to Jan 12, 2022 for this study. Studies were identified through PubMed/MEDLINE, EMBASE, and CENTRAL, and authors were contacted for individual participant data, including outcome data, with a minimum of 12 months of follow-up. The primary outcome was a composite endpoint of all-cause mortality, hepatocellular carcinoma, liver transplantation, or cirrhosis complications (ie, ascites, variceal bleeding, hepatic encephalopathy, or progression to a MELD score ≥15). We calculated aggregated survival curves for trichotomised groups and compared them using stratified log-rank tests (histology: F0-2 vs F3 vs F4; LSM: <10 vs 10 to <20 vs ≥20 kPa; FIB-4: <1·3 vs 1·3 to ≤2·67 vs >2·67; NFS: <-1·455 vs -1·455 to ≤0·676 vs >0·676), calculated areas under the time-dependent receiver operating characteristic curves (tAUC), and performed Cox proportional-hazards regression to adjust for confounding. This study was registered with PROSPERO, CRD42022312226. FINDINGS: Of 65 eligible studies, we included data on 2518 patients with biopsy-proven NAFLD from 25 studies (1126 [44·7%] were female, median age was 54 years [IQR 44-63), and 1161 [46·1%] had type 2 diabetes). After a median follow-up of 57 months [IQR 33-91], the composite endpoint was observed in 145 (5·8%) patients. Stratified log-rank tests showed significant differences between the trichotomised patient groups (p<0·0001 for all comparisons). The tAUC at 5 years were 0·72 (95% CI 0·62-0·81) for histology, 0·76 (0·70-0·83) for LSM-VCTE, 0·74 (0·64-0·82) for FIB-4, and 0·70 (0·63-0·80) for NFS. All index tests were significant predictors of the primary outcome after adjustment for confounders in the Cox regression. INTERPRETATION: Simple non-invasive tests performed as well as histologically assessed fibrosis in predicting clinical outcomes in patients with NAFLD and could be considered as alternatives to liver biopsy in some cases. FUNDING: Innovative Medicines Initiative 2.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.029
metaresearch head score (Gemma)0.052
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.983
Threshold uncertainty score0.154

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.052
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0170.061
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0030.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.334
GPT teacher head0.419
Teacher spread0.085 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designMeta-analysis
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations187
Published2023
Admission routes2
Has abstractyes

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