Long-term outcomes and predictive ability of non-invasive scoring systems in patients with non-alcoholic fatty liver disease
Open accessAbstract
Background & aims Non-invasive scoring systems (NSS) are used to identify patients with non-alcoholic fatty liver disease (NAFLD) who are at risk of advanced fibrosis, but their reliability in predicting long-term outcomes for hepatic/extrahepatic complications or death and their concordance in cross-sectional and longitudinal risk stratification remain uncertain.Methods The most common NSS (NFS, FIB-4, BARD, APRI) and the Hepamet fibrosis score (HFS) were assessed in 1,173 European patients with NAFLD from tertiary centres. Performance for fibrosis risk stratification and for the prediction of long-term hepatic/extrahepatic events, hepatocarcinoma (HCC) and overall mortality were evaluated in terms of AUC and Harrell's c-index. For longitudinal data, NSS-based Cox proportional hazard models were trained on the whole cohort with repeated 5-fold cross-validation, sampling for testing from the 607 patients with all NSS available.Results Cross-sectional analysis revealed HFS as the best performer for the identification of significant (F0-1 vs. F2-4, AUC = 0.758) and advanced (F0-2 vs. F3-4, AUC = 0.805) fibrosis, while NFS and FIB-4 showed the best performance for detecting histological cirrhosis (range AUCs 0.85-0.88). Considering longitudinal data (follow-up between 62 and 110 months), NFS and FIB-4 were the best at predicting liver-related events (c-indices>0.7), NFS for HCC (c-index = 0.9 on average), and FIB-4 and HFS for overall mortality (c-indices >0.8). All NSS showed limited performance (c-indices <0.7) for extrahepatic events.Conclusions Overall, NFS, HFS and FIB-4 outperformed APRI and BARD for both cross-sectional identification of fibrosis and prediction of long-term outcomes, confirming that they are useful tools for the clinical management of patients with NAFLD at increased risk of fibrosis and liver-related complications or death.Lay summary Non-invasive scoring systems are increasingly being used in patients with non-alcoholic fatty liver disease to identify those at risk of advanced fibrosis and hence clinical complications. Herein, we compared various non-invasive scoring systems and identified those that were best at identifying risk, as well as those that were best for the prediction of long-term outcomes, such as liver-related events, liver cancer and death.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Retrospective analyses have provided further evidence of the potential of FIB-4, NFS and ELF to predict progression to cirrhosis and liver-related events.
“Retrospective analyses have provided further evidence of the potential of FIB-4, NFS and ELF to predict progression to cirrhosis and liver-related events. 181,182”
From EASL-EASD-EASO Clinical Practice Guidelines on the management of metabolic dysfunction-associated steatotic liver disease (MASLD). (2024) · cites this paper as [182]
The Results (p. 4) report that FIB-4 and NFS detected histological cirrhosis and were the best predictors of subsequent liver-related events. The paper does not evaluate ELF, and its cirrhosis-detection analysis was cross-sectional rather than a longitudinal test of progression to cirrhosis (Statistical Analysis, p. 9). (LLM, conf=0.88)
Experimental — accuracy not yet measuredNAFLD fibrosis score (NFS) has a reported c-index of up to 0.9 for the prediction of HCC in a longitudinal study of 1,173 European individuals with MASLD.
“The NAFLD fi brosis score (NFS) had a reported c-index of up to 0.9 for the prediction of HCC in a longitudinal study of 1,173 European individuals with MASLD. 182”
From EASL-EASD-EASO Clinical Practice Guidelines on the management of metabolic dysfunction-associated steatotic liver disease (MASLD). (2024) · cites this paper as [182]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Part of this paper’s reference list could not be split into single entries.
EASL Clinical Practice Guidelines: Management of hepatocellular carcinoma
Galle, P. R., Forner, A., Llovet, J. M., Mazzaferro, V., Piscaglia, F., Raoul, J.-L., Schirmacher, P., & Vilgrain, V. (2018) · Journal of Hepatology · doi:10.1016/j.jhep.2018.03.019 · Cited by 11,406 · see citing works
Design and validation of a histological scoring system for nonalcoholic fatty liver disease†
Kleiner, D. E., Brunt, E. M., Van Natta, M., Behling, C., Contos, M. J., Cummings, O. W., Ferrell, L. D., Liu, Y., Torbenson, M. S., Unalp‐Arida, A., et al. (2005) · Hepatology · doi:10.1002/hep.20701 · Cited by 10,791 · see citing works
Younossi, Z. M., Koenig, A. B., Abdelatif, D., Fazel, Y., Henry, L., & Wymer, M. (2016) · Hepatology · doi:10.1002/hep.28431 · Cited by 10,781 · see citing works
Chicco, D., & Jurman, G. (2020) · BMC Genomics · doi:10.1186/s12864-019-6413-7 · Cited by 6,143 · see citing works
Sterling, R. K., Lissen, E., Clumeck, N., Sola, R., Correa, M. C., Montaner, J., S. Sulkowski, M., Torriani, F. J., Dieterich, D. T., Thomas, D. L., et al. (2006) · Hepatology · doi:10.1002/hep.21178 · Cited by 5,021 · see citing works
EASL–EASD–EASO Clinical Practice Guidelines for the management of non-alcoholic fatty liver disease
(2016) · Journal of Hepatology · doi:10.1016/j.jhep.2015.11.004 · Cited by 4,836 · see citing works
Wai, C.-T., Greenson, J. K., Fontana, R. J., Kalbfleisch, J. D., Marrero, J. A., Conjeevaram, H. S., & Lok, A. S.-F. (2003) · Hepatology · doi:10.1053/jhep.2003.50346 · Cited by 4,406 · see citing works
MAFLD: a consensus-driven proposed nomenclature for metabolic associated fatty liver disease
Eslam, M., Sanyal, A. J., George, J., Sanyal, A., Neuschwander-Tetri, B., Tiribelli, C., Kleiner, D. E., Brunt, E., Bugianesi, E., Yki-Järvinen, H., et al. (2020) · Gastroenterology · doi:10.1053/j.gastro.2019.11.312 · Cited by 3,286 · see citing works
Self-citationAngulo, P., Hui, J. M., Marchesini, G., Bugianesi, E., George, J., Farrell, G. C., Enders, F., Saksena, S., Burt, A. D., Bida, J. P., et al. (2007) · Hepatology · doi:10.1002/hep.21496 · Cited by 3,198 · see citing works
Self-citationAngulo, P., Kleiner, D. E., Dam-Larsen, S., Adams, L. A., Bjornsson, E. S., Charatcharoenwitthaya, P., Mills, P. R., Keach, J. C., Lafferty, H. D., Stahler, A., et al. (2015) · Gastroenterology · doi:10.1053/j.gastro.2015.04.043 · Cited by 2,987 · see citing works
Perkins, N. J., & Schisterman, E. F. (2006) · American Journal of Epidemiology · doi:10.1093/aje/kwj063 · Cited by 1,753 · see citing works
Comparison of Noninvasive Markers of Fibrosis in Patients With Nonalcoholic Fatty Liver Disease
Shah, A. G., Lydecker, A. D., Murray, K. F., Tetri, B., Contos, M. J., & Sanyal, A. J. (2009) · Clinical Gastroenterology and Hepatology · doi:10.1016/j.cgh.2009.05.033 · Cited by 1,561 · see citing works
Taylor, R. S., Taylor, R. J., Bayliss, S., Hagström, H., Nasr, P., Schattenberg, J. M., Ishigami, M., Toyoda, H., Wai-Sun Wong, V., Peleg, N., et al. (2020) · Gastroenterology · doi:10.1053/j.gastro.2020.01.043 · Cited by 1,069 · see citing works
Self-citationHagström, H., Nasr, P., Ekstedt, M., Hammar, U., Stål, P., Hultcrantz, R., & Kechagias, S. (2017) · Journal of Hepatology · doi:10.1016/j.jhep.2017.07.027 · Cited by 1,067 · see citing works
McPherson, S., Hardy, T., Henderson, E., Burt, A. D., Day, C. P., & Anstee, Q. M. (2014) · Journal of Hepatology · doi:10.1016/j.jhep.2014.11.034 · Cited by 1,059 · see citing works
Self-citationBédossa, P., Poitou, C., Veyrie, N., Bouillot, J., Basdevant, A., Paradis, V., Tordjman, J., & Clément, K. (2012) · Hepatology · doi:10.1002/hep.25889 · Cited by 909 · see citing works
Harrison, S. A., Oliver, D., Arnold, H. L., Gogia, S., & Neuschwander‐Tetri, B. A. (2008) · Gut · doi:10.1136/gut.2007.146019 · Cited by 869 · see citing works
Vilar-Gomez, E., Calzadilla-Bertot, L., Wai-Sun Wong, V., Castellanos, M., Aller-de la Fuente, R., Metwally, M., Eslam, M., Gonzalez-Fabian, L., Alvarez-Quiñones Sanz, M., Conde-Martin, A. F., et al. (2018) · Gastroenterology · doi:10.1053/j.gastro.2018.04.034 · Cited by 806 · see citing works
Self-citationAngulo, P., Bugianesi, E., Bjornsson, E. S., Charatcharoenwitthaya, P., Mills, P. R., Barrera, F., Haflidadottir, S., Day, C. P., & George, J. (2013) · Gastroenterology · doi:10.1053/j.gastro.2013.06.057 · Cited by 502 · see citing works
Determinants of Fibrosis Progression and Regression in NASH
Schuppan, D., Surabattula, R., & Wang, X. Y. (2018) · Journal of Hepatology · doi:10.1016/j.jhep.2017.11.012 · Cited by 479 · see citing works
Evaluating Prediction Rules for <i>t</i> -Year Survivors With Censored Regression Models
Uno, H., Cai, T., Tian, L., & Wei, L. J. (2007) · Journal of the American Statistical Association · doi:10.1198/016214507000000149 · Cited by 413 · see citing works
Adaptive immunity: an emerging player in the progression of NAFLD
Sutti, S., & Albano, E. (2020) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/s41575-019-0210-2 · Cited by 374 · see citing works
Ampuero, J., Pais, R., Aller, R., Gallego‐Durán, R., Crespo, J., García‐Monzón, C., Boursier, J., Vilar‐Gómez, E., Petta, S., Zheng, M., et al. (2019) · Clinical Gastroenterology and Hepatology · doi:10.1016/j.cgh.2019.05.051 · Cited by 184 · see citing works
Self-citationHagström, H., Talbäck, M., Andreasson, A., Walldius, G., & Hammar, N. (2020) · Gastroenterology · doi:10.1053/j.gastro.2019.09.008 · Cited by 182 · see citing works
Hardy, T., Wonders, K., Younes, R., Aithal, G. P., Aller, R., Allison, M., Bédossa, P., Betsou, F., Boursier, J., Brosnan, M. J., et al. (2020) · Contemporary Clinical Trials · doi:10.1016/j.cct.2020.106175 · Cited by 138 · see citing works
Self-citationLambert, J., Halfon, P., Pénaranda, G., Bédossa, P., Cacoub, P., & Carrat, F. (2008) · Clinical Chemistry · doi:10.1373/clinchem.2007.097923 · Cited by 137 · see citing works
Progression and Natural History of Nonalcoholic Fatty Liver Disease in Adults
Marengo, A., Jouness, R. I. K., & Bugianesi, E. (2015) · Clinics in Liver Disease · doi:10.1016/j.cld.2015.10.010 · Cited by 136 · see citing works
Self-citationHagström, H., Nasr, P., Ekstedt, M., Stål, P., Hultcrantz, R., & Kechagias, S. (2019) · Clinical Gastroenterology and Hepatology · doi:10.1016/j.cgh.2018.11.030 · Cited by 132 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Tacke, F., Horn, P., Wai-Sun Wong, V., Ratziu, V., Bugianesi, E., Francque, S., Zelber-Sagi, S., Valenti, L., Roden, M., Schick, F., et al. (2024) · Journal of Hepatology · doi:10.1016/j.jhep.2024.04.031 · Cited by 2,210 · see citing works
Self-citationNonalcoholic steatohepatitis-related hepatocellular carcinoma: pathogenesis and treatment
Llovet, J. M., Willoughby, C. E., Singal, A. G., Greten, T. F., Heikenwälder, M., El-Serag, H. B., Finn, R. S., & Friedman, S. L. (2023) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/s41575-023-00754-7 · Cited by 354 · see citing works
Natural history and progression of metabolic dysfunction-associated steatotic liver disease
Hagström, H., Shang, Y., Hegmar, H., & Nasr, P. (2024) · The Lancet Gastroenterology & Hepatology · doi:10.1016/s2468-1253(24)00193-6 · Cited by 310 · see citing works
Mózes, F. E., Lee, J. A., Vali, Y., Alzoubi, O., Staufer, K., Trauner, M., Paternostro, R., Stauber, R. E., Holleboom, A. G., van Dijk, A.-M., et al. (2023) · The Lancet Gastroenterology & Hepatology · doi:10.1016/s2468-1253(23)00141-3 · Cited by 214 · see citing works
Self-citationA proteo-transcriptomic map of non-alcoholic fatty liver disease signatures
Govaere, O., Hasoon, M., Alexander, L., Cockell, S., Tiniakos, D., Ekstedt, M., Schattenberg, J. M., Boursier, J., Bugianesi, E., Ratziu, V., et al. (2023) · Nature Metabolism · doi:10.1038/s42255-023-00775-1 · Cited by 178 · see citing works
Self-citationNoninvasive assessment of liver fibrosis in NAFLD
Sanyal, A. J., Castera, L., & Wong, V. W.-S. (2023) · Clinical Gastroenterology and Hepatology · doi:10.1016/j.cgh.2023.03.042 · Cited by 157 · see citing works
- Authors
- Younes, R., Caviglia, G. P., Govaere, O., Rosso, C., Armandi, A., Sanavia, T., Pennisi, G., Liguori, A., Francione, P., Gallego-Durán, R., Ampuero, J., Garcia Blanco, M. J., Aller, R., Tiniakos, D., Burt, A., David, E., Vecchio, F. M., Maggioni, M., Cabibi, D., Pareja, M. J., Zaki, M. Y. W., Grieco, A., Fracanzani, A. L., Valenti, L., Miele, L., Fariselli, P., Petta, S., Romero-Gomez, M., Anstee, Q. M., & Bugianesi, E.
- Affiliations
- Boehringer Ingelheim (Germany) (DE)
- Azienda Ospedaliera Citta' della Salute e della Scienza di Torino (IT)
- Newcastle University (GB)
- University of Palermo (IT)
- Università Cattolica del Sacro Cuore (IT)
- University of Milan (IT)
- Instituto de Biomedicina de Sevilla (ES)
- Hospital Clínico Universitario de Valladolid (ES)
- National and Kapodistrian University of Athens (GR)
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico (IT)
- Hospital Universitario de Valme (ES)
- Newcastle upon Tyne Hospitals NHS Foundation Trust (GB)
- Corresponding author
- Anstee, Quentin M.
- Bugianesi, Elisabetta
- ORCID
- Younes, Ramy
- Caviglia, Gian Paolo
- Govaere, Olivier
- Rosso, Chiara
- Armandi, Angelo
- Sanavia, Tiziana
- Pennisi, Grazia
- Liguori, Antonio
- Ampuero, Javier
- Garcia Blanco, Maria J.
- Aller, Rocio
- Tiniakos, Dina
- Burt, Alastair
- David, Ezio
- Maggioni, Marco
- Cabibi, Daniela
- Pareja, María Jesús
- Zaki, Marco Y.W.
- Grieco, Antonio
- Fracanzani, Anna L.
- Valenti, Luca
- Miele, Luca
- Fariselli, Piero
- Petta, Salvatore
- Anstee, Quentin M.
- Bugianesi, Elisabetta
- Venue
- Journal of Hepatology
- Publisher
- Elsevier BV
- Published
- 2021-10
- Volume
- 75
- Issue
- 4
- Pages
- 786-794
- ISSN
- 0168-8278 (ISSN-L)
- OpenAlex
- W3162991784
- Cited by
- 196
- References
- 29
- Citations by year
- 2026: 23 · 2025: 34 · 2024: 38 · 2023: 44 · 2022: 47 · 2021: 8
- FWCI
- 24.67
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- Journal of Hepatology (publishedVersion)
- PubMed (publishedVersion)
- Institutional Research Information System University of Turin (University of Turin) (submittedVersion, other-oa)
- DIGITAL.CSIC (Spanish National Research Council (CSIC)) (submittedVersion)
- Nova Science Publishers (Nova Science Publishers, Inc.) (submittedVersion)
- Lirias (KU Leuven) (submittedVersion)
- Newcastle University ePrints (Newcastle Univesity) (cc-by-nc-nd)
- Funders
- MIUR
- Ministero della Salute
- Horizon 2020
- Topics
- Health Sciences › Medicine › Epidemiology › Liver Disease Diagnosis and Treatment
- Health Sciences › Medicine › Hepatology › Liver Disease and Transplantation
- Health Sciences › Medicine › Hepatology › Liver Diseases and Immunity
- Keywords
- non-alcoholic fatty liver disease, liver fibrosis, FIB-4, APRI, bards, NAFLD fibrosis score, hepatocellular carcinoma, liver-related mortality, extrahepatic complications, long-term outcomes, risk prediction, liver cirrhosis, prognostic performance
- MeSH terms
- Adult, Cross-Sectional Studies, Female, Humans, Liver, Male, Middle Aged, Predictive Value of Tests, Prognosis, Research Design, ROC Curve, Severity of Illness Index, Time, Reproducibility of Results, Area Under Curve, Non-alcoholic Fatty Liver Disease
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review