FibroScan-AST (FAST) score for the non-invasive identification of patients with non-alcoholic steatohepatitis with significant activity and fibrosis: a prospective derivation and global validation study

Philip N Newsome, Magali Sasso, Jonathan J Deeks, Angelo Paredes, Jérôme Boursier, Wah-Kheong Chan, Yusuf Yilmaz, Sébastien Czernichow, Ming-Hua Zheng, Vincent Wai-Sun Wong, Michael Allison, Emmanuel Tsochatzis, Quentin M Anstee, David A Sheridan, Peter J Eddowes, Indra N Guha, Jeremy F Cobbold, Valérie Paradis, Pierre Bedossa, Véronique Miette, Céline Fournier-Poizat, Laurent Sandrin, Stephen A Harrison, Philip N Newsome, Magali Sasso, Jonathan J Deeks, Angelo Paredes, Jérôme Boursier, Wah-Kheong Chan, Yusuf Yilmaz, Sébastien Czernichow, Ming-Hua Zheng, Vincent Wai-Sun Wong, Michael Allison, Emmanuel Tsochatzis, Quentin M Anstee, David A Sheridan, Peter J Eddowes, Indra N Guha, Jeremy F Cobbold, Valérie Paradis, Pierre Bedossa, Véronique Miette, Céline Fournier-Poizat, Laurent Sandrin, Stephen A Harrison

Abstract

Background: The burden of non-alcoholic fatty liver disease (NAFLD) is increasing globally, and a major priority is to identify patients with non-alcoholic steatohepatitis (NASH) who are at greater risk of progression to cirrhosis, and who will be candidates for clinical trials and emerging new pharmacotherapies. We aimed to develop a score to identify patients with NASH, elevated NAFLD activity score (NAS≥4), and advanced fibrosis (stage 2 or higher [F≥2]).

Methods: This prospective study included a derivation cohort before validation in multiple international cohorts. The derivation cohort was a cross-sectional, multicentre study of patients aged 18 years or older, scheduled to have a liver biopsy for suspicion of NAFLD at seven tertiary care liver centres in England. This was a prespecified secondary outcome of a study for which the primary endpoints have already been reported. Liver stiffness measurement (LSM) by vibration-controlled transient elastography and controlled attenuation parameter (CAP) measured by FibroScan device were combined with aspartate aminotransferase (AST), alanine aminotransferase (ALT), or AST:ALT ratio. To identify those patients with NASH, an elevated NAS, and significant fibrosis, the best fitting multivariable logistic regression model was identified and internally validated using boot-strapping. Score calibration and discrimination performance were determined in both the derivation dataset in England, and seven independent international (France, USA, China, Malaysia, Turkey) histologically confirmed cohorts of patients with NAFLD (external validation cohorts). This study is registered with ClinicalTrials.gov, number NCT01985009.

Findings: Between March 20, 2014, and Jan 17, 2017, 350 patients with suspected NAFLD attending liver clinics in England were prospectively enrolled in the derivation cohort. The most predictive model combined LSM, CAP, and AST, and was designated FAST (FibroScan-AST). Performance was satisfactory in the derivation dataset (C-statistic 0·80, 95% CI 0·76-0·85) and was well calibrated. In external validation cohorts, calibration of the score was satisfactory and discrimination was good across the full range of validation cohorts (C-statistic range 0·74-0·95, 0·85; 95% CI 0·83-0·87 in the pooled external validation patients' cohort; n=1026). Cutoff was 0·35 for sensitivity of 0·90 or greater and 0·67 for specificity of 0·90 or greater in the derivation cohort, leading to a positive predictive value (PPV) of 0·83 (84/101) and a negative predictive value (NPV) of 0·85 (93/110). In the external validation cohorts, PPV ranged from 0·33 to 0·81 and NPV from 0·73 to 1·0.

Interpretation: The FAST score provides an efficient way to non-invasively identify patients at risk of progressive NASH for clinical trials or treatments when they become available, and thereby reduce unnecessary liver biopsy in patients unlikely to have significant disease.

Funding: Echosens and UK National Institute for Health Research.

Copyright © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.

Figures

Figure 1
Figure 1
Derivation cohort trial profile
Figure 2
Figure 2
Diagnostic performance in the derivation cohort of the FAST score for the diagnostic of NASH + NAS ≥ 4 + F ≥ 2 (A) Receiver operating characteristic curve. (B) Calibration plot and calibration intercept and slope. The shaded area indicates 95% CI. The calibration plot characterises the agreement between observed proportion and predicted probabilities. The intercept compares the mean of all predicted risks with the mean observed risk and indicates the extent that predictions are systematically too low or too high. The slope accounts for differences in performance in groups at high or low risk. Calibration of the data is estimated using a smoothed regression line (dotted line) using locally estimated scatterplot smoothing (Loess) that allows inspection of the calibration across the range of predicted values and determination of whether there are segments of the range in which the model is poorly calibrated. Triangles represent deciles of participants (n=50) grouped by similar predicted risk. Calibration of the score is satisfactory since the intercept is not significantly different from 0, slope is not significantly different from 1, the flexible calibration curve is close to the ideal calibration (solid line), and its CI zone includes the ideal curve. (C) Sensitivity, specificity, positive predictive value, and negative predictive value versus all possible FAST score values. (D) Screen failure rate, missed cases rate, and proportion of patients identified, versus FAST scores values. Plot of the screen failure rate (equal to 1–positive predictive value) and missed cases rate (equal to 1–sensitivity) versus all possible FAST score values. At given FAST score cutoffs, it is possible to graphically assess the screen failure rate and missed cases rate together with the proportion of patients above the FAST score who would be given liver biopsy in the context of patients screening in drug trials for NASH. NASH=non-alcoholic steatohepatitis. FAST=FibroScan-aspartate aminotransferase. NASH + NAS ≥ 4 + F ≥ 2=NASH, elevated non-alcoholic fatty liver disease activity score (≥4), and advanced fibrosis (≥stage 2). AUROC=area under the receiver operating curve.
Figure 3
Figure 3
Calibration plots in external validation cohorts (A) French bariatric cohort (n=110). Prevalence of NASH + NAS ≥ 4 + F ≥ 2=15%. (B) USA screening cohort (n=242). Prevalence of NASH + NAS ≥ 4 + F ≥ 2=12%. (C) China Hong-Kong NAFLD cohort (n=83). Prevalence of NASH+NAS≥4+F≥2=43%. (D) China Wenzhou NAFLD cohort (n=104). Prevalence of NASH + NAS ≥ 4 + F ≥ 2=9%. (E) French NAFLD cohort (n=182). Prevalence of NASH + NAS ≥ 4 + F ≥ 2=43%. (F) Malaysian NAFLD cohort (n=176). Prevalence of NASH + NAS ≥ 4 + F ≥ 2=20%. (G) Turkish NAFLD cohort (n=129). Prevalence of NASH + NAS ≥ 4 + F ≥ 2=57%. The solid line in each image represents the ideal calibration. The dotted line represents the calibrations estimated on the data using locally estimated scatterplot smoothing (Loess). The shaded area indicates 95% CI. Triangles represent deciles of participants grouped by similar predicted risk. The distribution of participants is indicated with spikes at the bottom of the graph (patients with NASH + NAS ≥ 4 + F ≥ 2 above the x-axis, patients without NASH + NAS ≥ 4 + F ≥ 2 below the x-axis). The French (E) and Turkish (G) NAFLD external validation cohorts are well calibrated; their calibration curve is nearly linear, their intercept is close to zero (CIs include zero), and their slope is close to one (CIs include one). The Chinese Hong-Kong NAFLD cohort (C) has a zone in which the risk of being NASH + NAS ≥ 4 + F ≥ 2 is overestimated using the FAST score (grey ribbon below the ideal calibration curve) and a zone in which the calibration seem adequate (grey ribbon zone includes the ideal calibration curve). However, this cohort size is quite small (n=83). The French bariatric surgery (A), USA screening (B), Chinese Wenzhou NALFD (D), and the Malaysian NAFLD (F) cohort have a range of prevalence of NASH + NAS ≥ 4 + F ≥ 2 (9% to 20%), which is lower than the derivation cohort. In those four cohorts, the FAST score overestimates the probability of being NASH + NAS ≥ 4 + F ≥ 2. The discrepancy is mainly driven by the intercept (CIs do not include zero). All slopes are within an acceptable range (the CI includes one), except for the French bariatric cohort, which seems to be at the limit. NAFLD=non-alcoholic fatty liver disease. FAST=FibroScan-aspartate aminotransferase. NASH + NAS ≥ 4 + F ≥ 2=non-alcoholic steatohepatitis, elevated non-alcoholic fatty liver disease activity score (≥4) and advanced fibrosis (≥stage 2).

References

    1. Younossi Z, Anstee QM, Marietti M. Global burden of NAFLD and NASH: trends, predictions, risk factors and prevention. Nat Rev Gastroenterol Hepatol. 2017;15:11–20.
    1. Haldar D, Kern B, Hodson J. Outcomes of liver transplantation for non-alcoholic steatohepatitis: a European Liver Transplant Registry study. J Hepatol. 2019;71:313–322.
    1. Charlton MR, Burns JM, Pedersen RA, Watt KD, Heimbach JK, Dierkhising RA. Frequency and outcomes of liver transplantation for nonalcoholic steatohepatitis in the United States. Gastroenterology. 2011;141:1249–1253.
    1. Tsochatzis EA, Newsome PN. Non-alcoholic fatty liver disease and the interface between primary and secondary care. Lancet Gastroenterol Hepatol. 2018;3:509–517.
    1. Sanyal AJ. Past, present and future perspectives in nonalcoholic fatty liver disease. Nat Rev Gastroenterol Hepatol. 2019;16:377–386.
    1. Srivastava A, Gailer R, Tanwar S. Prospective evaluation of a primary care referral pathway for patients with non-alcoholic fatty liver disease. J Hepatol. 2019;71:371–378.
    1. Angulo P, Hui JM, Marchesini G. The NAFLD fibrosis score: a noninvasive system that identifies liver fibrosis in patients with NAFLD. Hepatology. 2007;45:846–854.
    1. Guha IN, Parkes J, Roderick P. Noninvasive markers of fibrosis in nonalcoholic fatty liver disease: validating the European Liver Fibrosis Panel and exploring simple markers. Hepatology. 2008;47:455–460.
    1. Sanyal AJ, Friedman SL, McCullough AJ. Challenges and opportunities in drug and biomarker development for nonalcoholic steatohepatitis: findings and recommendations from an American Association for the Study of Liver Diseases—US Food and Drug Administration Joint Workshop. Hepatology. 2015;61:1392–1405.
    1. Kleiner DE, Brunt EM, Van Natta M. Design and validation of a histological scoring system for nonalcoholic fatty liver disease. Hepatology. 2005;41:1313–1321.
    1. Eddowes PJ, Sasso M, Allison M. Accuracy of FibroScan controlled attenuation parameter and liver stiffness measurement in assessing steatosis and fibrosis in patients with nonalcoholic fatty liver disease. Gastroenterology. 2019;156:1717–1730.
    1. Ratziu V, Harrison S, Francque S. Elafibranor, an agonist of the peroxisome proliferator-activated receptor-alpha and -delta, induces resolution of nonalcoholic steatohepatitis without fibrosis worsening. Gastroenterology. 2016;150:1147–1159.
    1. Moons KG, Altman DG, Reitsma JB. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med. 2015;162:W1–W73.
    1. Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ. 2015;350
    1. Bedossa P. Utility and appropriateness of the fatty liver inhibition of progression (FLIP) algorithm and steatosis, activity, and fibrosis (SAF) score in the evaluation of biopsies of nonalcoholic fatty liver disease. Hepatology. 2014;60:565–575.
    1. Sandrin L, Fourquet B, Hasquenoph JM. Transient elastography: a new noninvasive method for assessment of hepatic fibrosis. Ultrasound Med Biol. 2003;29:1705–1713.
    1. Sasso M, Beaugrand M, de Ledinghen V. Controlled attenuation parameter (CAP): a novel VCTE guided ultrasonic attenuation measurement for the evaluation of hepatic steatosis: preliminary study and validation in a cohort of patients with chronic liver disease from various causes. Ultrasound Med Biol. 2010;36:1825–1835.
    1. Harrell FE. Springer-Verlag; New York: 2006. Regression Modeling Strategies.
    1. Steyerberg EW. Springer; New York: 2008. Clinical prediction models: a practical approach to development, validation, and updating.
    1. Steyerberg EW, van der Ploeg T, Van Calster B. Risk prediction with machine learning and regression methods. Biom J. 2014;56:601–606.
    1. R Core Team . R Foundation for Statistical Computing; Vienna: 2018. A language and environment for statistical computing.
    1. Angulo P, Kleiner DE, Dam-Larsen S. Liver fibrosis, but no other histologic features, is associated with long-term outcomes of patients with nonalcoholic fatty liver disease. Gastroenterology. 2015;149:389–397.
    1. Dulai PS, Singh S, Patel J. Increased risk of mortality by fibrosis stage in nonalcoholic fatty liver disease: systematic review and meta-analysis. Hepatology. 2017;65:1557–1565.
    1. Ekstedt M, Hagstrom H, Nasr P. Fibrosis stage is the strongest predictor for disease-specific mortality in NAFLD after up to 33 years of follow-up. Hepatology. 2015;61:1547–1554.
    1. Friedman SL, Ratziu V, Harrison SA. A randomized, placebo-controlled trial of cenicriviroc for treatment of nonalcoholic steatohepatitis with fibrosis. Hepatology. 2018;67:1754–1767.
    1. Poynard T, Muntenau M, Morra R. Methodological aspects of the interpretation of non-invasive biomarkers of liver fibrosis: a 2008 update. Gastroenterol Clin Biol. 2008;32(suppl 1):8–21.
    1. Vuppalanchi R, Siddiqui MS, Van Natta ML. Performance characteristics of vibration-controlled transient elastography for evaluation of nonalcoholic fatty liver disease. Hepatology. 2018;67:134–144.

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