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

An individual patient data meta-analysis to determine cut-offs for and confounders of NAFLD-fibrosis staging with magnetic resonance elastography Authors Jia-xu Liang, Javier Ampuero, Hao Niu, ., Javier Castell, Rohit Loomba, Manuel Romero-Gómez Correspondence [email protected] (M. Romero-Gómez). Graphical abstract MRE-based liver stiffness measurements in 798 patients from 8 international cohorts An individual patient data meta-analysis MRE has excellent diagnostic performance for diagnosis of significant fibrosis, advanced fibrosis, and cirrhosis in patients with NAFLD High NASH activity Elevated GGT and/or Higher inflammatory activity and elevated GGT may affect accuracy for staging early liver fibrosis (F0-F1) Findings of meta-analysis F0 F1 F2 F3 F4 C u toffs : AUC: ≥F1 2.65 ≥F2 3.14 ≥F3 3.53 F4 4.45 0.82 0.92 0.92 0.94 k Pa • Not confounded by liver steatosis MRE-based fibrosis staging is: • Not confounded by body mass index Highlights Magnetic resonance elastography demonstrated excellent diagnostic accuracy for staging liver fibrosis in patients with NAFLD. Assessment of fibrosis was not confounded by steatosis or high body mass index. Cut-offs for significant fibrosis, advanced fibrosis, and cirrhosis were found to be 3.14, 3.53, and 4.45 kPa, respectively. Raised GGT levels and increased lobular inflammatory activity may result in overestimation of early-stage fibrosis (F0–F1). 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. https://doi.org/10.1016/j.jhep.2023.04.025 © 2023 The Author(s). Published by Elsevier B.V. on behalf of European Association for the Study of the Liver. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). J. Hepatol. 2023, 79, 592–604 Research Article NAFLD and Alcohol-Related Liver Diseases An individual patient data meta-analysis to determine cut-offs for and confounders of NAFLD-fibrosis staging with magnetic resonance elastography Jia-xu Liang 1,2,3 , Javier Ampuero 1,2 , Hao Niu 4,5 , Kento Imajo 6 , Mazen Noureddin 7 , Jaideep Behari 8 , Dae Ho Lee 9 , Richard L. Ehman 10 , Fredrik Rorsman 11 , Johan Vessby 11 , Juan R. Lacalle 12 , Ferenc E. Mózes 13 , Michael Pavlides 13,14 , Quentin M. Anstee 15,16 , Stephen A. Harrison 13 , Javier Castell 17 , Rohit Loomba 18,19 , Manuel Romero-Gómez 1,2, *, on behalf of the LITMUS Consortium Investigators Journal of Hepatology 2023. vol. 79 j592–604 Background & Aims: We 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. Methods: A 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. Results: Eight 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. Conclusions: MRE 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. © 2023 The Author(s). Published by Elsevier B.V. on behalf of European Association for the Study of the Liver. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Introduction NAFLD is the leading cause of chronic liver disease worldwide, affecting approximately 25% of the global population. 1 It is a spectrum comprising isolated steatosis, steatohepatitis, advanced fibrosis, and cirrhosis. 2 Liver fibrosis predicts the occurrence of liver-related events and overall mortality in patients withNAFLD. 3 Therefore,theaccuratedetectionandstagingofliver fibrosisisakeyissueforthemanagementofpatientswith NAFLD. 4 Liver biopsy is the reference standard for assessing the liver fibrosis stage in patients with NAFLD. However, it cannot be widely used in clinical practice owing to its limitations and risks, such as sampling errors, poor tolerance, its high cost, and the risk of serious adverse events. 5,6 Therefore, several biochemical 7,8 and imaging biomarkers have emerged to help in the diagnose of liver fibrosis, with imaging mechanical biomarkers such as transient elastography (TE), shear-wave elastography (SWE), and magnetic resonance elastography (MRE) playing a major role among them. MRE evaluates a larger portion of the liver, offering the option of choosing the region of interest, which can be performed in conjunction with conventional magnetic resonance imaging. 9 Several studies reported that MRE has a higher diagnostic performance than TE and SWE in assessing liver fibrosis in patients with NAFLD, 10–13 including morbidly obese patients and those with ascites, additionally decreasing the failure rate. However, there are cases in which a discrepancy Keywords: Elastography; Magnetic Resonance; Fibrosis; NAFLD; NASH; GGT. Received 7 September 2022; received in revised form 23 March 2023; accepted 12 April 2023; available online 29 April 2023 * Corresponding author. Address: Digestive Disease Department, Virgen del Rocio University Hospital, Avenida Manuel Siurot s/n, 41013, Sevilla, Spain. Tel.: +34 954 786 587. E-mail address: [email protected] (M. Romero-Gómez). https://doi.org/10.1016/j.jhep.2023.04.025 Journal of Hepatology, September 2023. vol. 79 j592–604 Research Article NAFLD and Alcohol-Related Liver Diseases between fibrosis based on histology and liver stiffness measurement (LSM) by MRE occurs. In addition, there is not a generally accepted cut-off value for diagnosing the different stages of fibrosis because most of the published studies have been based on small and heterogenous cohorts. Although two individual patient data meta-analyses (IPD-MAs) 14,15 have been previously published and presented their respective cut-off values for staging liver fibrosis, they have not been directly compared and validated in a large sample cohort. The aim of this study was therefore to conduct an IPD-MA including data from studies that evaluated LSM by MRE with histology as the reference standard, to establish diagnostic cut-offs and explore the potential confounding factors influencing the diagnostic accuracy of MRE for staging liver fibrosis in patients with NAFLD. Materials and methods Our study was an IPD-MA based on a multicentre collaboration from previously published studies. It was performed in accordance with the PRISMA-IPD Statement checklist 16 and PRISMA-DTA Statement checklist, 17 and it was registered in PROSPERO (CRD42021264458). Search strategy and selection criteria Two investigators (JxL and JA) independently identified suitable studies, published up to 31 December 2021, by searching in the MEDLINE (using PubMed as the search engine), Embase, and Cochrane databases; discrepancies were solved by a third author (MR-G). A combination of keywords and medical subject heading terms were used, including (‘MR’OR ‘Magnetic resonance’)AND (‘Elastography’OR ‘Elasticity’OR‘MRE’)AND(‘NAFLD’OR ‘Nonalcoholic fatty liver disease’OR ‘MAFLD’OR ‘Metabolic associatedfattyliverdisease’).Amanualsearchwasconductedbyusing references listed in the original articles and review articles retrieved. The search was restricted to English-language literature. The inclusion criteria were as follows: (1) patients older than 18 years; (2) no presence of other comorbid liver diseases; (3) assessment of liver fibrosis by using MRE; (4) histopathology as the reference standard; and (5) access to individual patient data. The exclusion criteria were as follows: (1) duplicate reports; (2) studies conducted on animals; (3) systematic reviews or metaanalyses; and (4) insufficient data to perform the IPD-MA despite having contacted the study investigators. Once relevant studies were identified, the corresponding author of the eligible study was contacted and provided with the background and details of the individual patient data (IPD) pooled analysis, as well as a data collection file for the input of individual patient results for the project. Then, the completed IPD collection files were sent back to the data management team. In case of non-response, the study was excluded after two contact attempts. Data harmonisation was performed by at least two independent readers (JxL, JA, JRL, and MR-G) at the site of the central data management team. Discrepancies, missing data, obvious errors, and inconsistencies between variables or outlying values were queried and rectified as necessary with input from the original authors. Finally, the following patient records were excluded: (1) no MRE measurements reported or MRE technical failure; (2) lack of essential clinical information; and (3) lack of relevant histopathological data. Data abstraction Reviewers extracted data and assessed the quality of each study. Differences in opinion between reviewers were resolved by consensus with a third reviewer. The following variables were recorded. 1. Study characteristics: study location, design, publication year, time period of study, interval between MRE and biopsy, and number of patients with NAFLD. 2. Demographic data: age, sex, BMI, weight, type 2 diabetes mellitus (T2DM), and arterial hypertension. 3. Laboratory test: aspartate aminotransferase (U/L), alanine aminotransferase (U/L), gamma-glutamyl transferase (GGT) (U/ L), albumin (g/dl), fasting blood glucose (mg/dl), triglyceride (mg/ dl), total cholesterol (mg/dl), platelet count (10 9 /L), HDL cholesterol (mg/dl), and LDL cholesterol (mg/dl). 4. Imaging data: LSM by MRE. All MREs were performed with 3-T/ 1.5-T field strength scanners using spin-echo echo-planar imaging (SE-EPI)/gradient-echo sequence (GRE) sequences, and shear waves at 60 Hz. To quantify the liver fat content, a multiecho gradient-echo sequence was used to obtain magnetic resonance imaging (MRI) proton density fat fraction from a single breath-hold acquisition; proton density fat fraction was defined to be the proportion of magnetic resonance (MR)-visible fat protons to the sum of MR-visible fat and water protons. R2* maps (relaxation rate = 1/T2*) data were also collected if performed. 5. Histopathological data: Histopathological data were extracted according to the Brunt scoring system (n = 58), 18 the NASH– Clinical Research Network (CRN) (n = 617), 19,20 and the SAF (steatosis–activity–fibrosis) score (n = 123). 21 Steatosis was defined according to the number of affected hepatocytes: S0 (<5% depending on the slide), S1 (5–33%), S2 (34–66%), and S3 (>66%). In the SAF histological scoring system, 21 each of the three features (steatosis, hepatocyte ballooning, and lobular inflammation) classified as at least grade 1 was defined as NASH, and activity (lobular inflammation 0–2 + hepatocyte ballooning 0–2) > −3 was classified as severe activity. In the NASH-CRN scoring system, the NAFLD activity score (NAS) ranged from 0 to 8 and consisted of steatosis (0–3), lobular inflammation (0–3), and hepatocyte ballooning (0–2). Therefore, NAS > −4, with a subscore of 1 or higher for each subcomponent, was defined as NASH in this scoring system; lobular inflammation (0–3) + hepatocyte ballooning (0–2) > −3 was also classified as severe activity to avoid the effect of the degree of steatosis. Moreover, on the basis of differentiating between NASH and no-NASH, all patients with NASH were classified as either NASH mild–moderate activity (NASH-MMA) or NASH severe activity (NASH-SA). These classification processes are shown in Fig. S1. Liver fibrosis was scored stage 0 (F0; none), stage 1 (F1; 1a or 1b perisinusoidal zone 3 or 1c portal fibrosis), stage 2 (F2; perisinusoidal and periportal fibrosis without bridging), stage 3 (F3; bridging fibrosis), and stage 4 (F4; cirrhosis). Quality assessment The risk of bias and applicability concerns of the included cohorts were assessed by two independent readers using the QUADAS-2 tool. 22 The quality assessment consisted of the evaluation of four components: patient selection, index tests, reference standard, and flow and timing (Fig. S2). Journal of Hepatology, September 2023. vol. 79 j592–604 593 Research Article Statistical analysis All statistical analyses were performed using MedCalc (version 19.7, MedCalc Software Inc, Ostend, Belgium) and Stata 16 (StataCorp LP, College Station, TX, USA). All statistical tests were two-sided with a significance level of 5%. For the overall population, continuous variables were reported as means with SDs, or medians with IQRs according to the data distribution. Categorical variables were displayed as percentages. Owing to the unbalanced distribution of liver fibrosis, we performed a two-stage method in the pooled cut-off metaanalysis. First, we replicated 20,000 stratified bootstrap samples to estimate the optimal cut-off (based on the Youden index) 23 and 95% CI of each fibrosis stage in an independent cohort. Second, we used these study-level cut-off values to estimate pooled cut-offs in a fixed- or random-effects model meta-analysis. Random-effects estimation was carried out if heterogeneity was higher than 50%; fixed-effects analysis was used whenever it was lower, and the pooled stiffness cut-off values were then displayed graphically as forest plots. Afterwards, the validation group generated by 3:1 random sampling was used to compare our pooled cut-off values with those of the previous IPD-MAs. 14,15 True-positive, false-positive, truenegative, and false-negative rates were calculated by sensitivity and specificity (Youden index) for each included cohort. Summary sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and area under the receiver operating characteristic curve (AUROC) with corresponding 95% CIs were calculated using the bivariate random-effects model to examine the diagnostic accuracy of MRE. 24 Heterogeneity was assessed using the Higgins inconsistency index (I 2 ) test, with values greater than 50% taken as an indicator of substantial heterogeneity. 25 Several preplanned subgroups and stratified analyses were performed based on study population (Asian or non-Asian), sex (male or female), T2DM (presence or absence), arterial hypertension (presence or absence), presence or absence of obesity (BMI > −30kg/m 2 or BMI <30 kg/m 2 ), presence or absence of NASH, liver steatosis degree (steatosis 0–1or2–3), sequence (GRE or SEEPI), field strength (1.5 T or 3.0 T), and manufacturer (Siemens and Philips or GE) of MRI. Subgroup AUCs were compared using the method by DeLong et al. 26 Furthermore, a sensitivity analysis was also performed to analyse the effect of the interval between MRE and liver biopsy, and R2* on the diagnostic performance of MRE. Univariate meta-regression analysis was also used to explore possible sources of heterogeneity; the covariates of this study level included the following: (1) sequence (GRE or SE-EPI), (2) field strength (1.5 T or 3.0 T), (3) manufacturer (Siemens and Philips or GE) of MRI, (4) study design (prospective or retrospective), and (5) population (Asian or non-Asian). We assessed the potential publication bias using Deeks’funnel plot asymmetry test, in which p<0.10 indicated statistical significance. To assess factors affecting the discordance between MRE- based staging and histopathology, multilevel models were fitted, using a one-stage approach. 27 Linear mixed-effects model (LMM) and generalised linear mixed model (GLMM), as extensions of traditional linear and logistic regression, were often used when heterogeneity was present and IPD were available. Some continuous missing data were replaced by the mean or median value according to the data distribution. A previously published study, 28 which lacked too much data, was not included in this part analysis. LMM was used to explore the effect of covariates as fixed effects on MRE, with different research centres as random effects. There are two categories of significant discordance: overestimation and underestimation. Overestimation was defined as the stage of MRE determination according to the previous pooled cut-off values at least two stages above histopathology or histopathological findings of F3 with LSM by MRE greater than 7.5 kPa. Underestimation was defined as the stage of MRE determination according to the previous pooled cut-off at least two stages lower than histopathology. For these binary outcomes (concordance/overestimation and concordance/underestimation), a GLMM was chosen to explore the covariates associated with discrepancies between LSM by MRE and histological assessment of fibrosis grading. Covariates with p<0.10 in the univariate analysis were entered into the multivariate analysis. Finally, the relationship between the selected covariates in GLMM and LSM by MRE was assessed using two-way ANOVA. In additional analyses, ‘rule-in’(at a specificity of 90%) and ‘rule-out’(at a sensitivity of 90%) cut-off values for LSM by MRE were identified with a traditional receiver operating characteristic analysis method. Based on Bayes’theorem, 29 we further investigated the impact of different prevalence settings on the predictive value under the three cut-off values (pooled cut-off, ‘rule-in’cut-off, and ‘rule-out’cut-off). Results From 252 unique studies identified using our search strategy, 16 diagnostic studies 10–12,28,30–41 from nine authors were eligible. All nine authors were contacted; however, no response was received from three authors. 30–32 The remaining six authors agreed to share their data. Moreover, two additional independent cohorts from Virgen del Rocío University Hospital, Seville (n = 61), 42 and the Department of Gastroenterology and Hepatology at Uppsala University, Sweden (n = 66), 43 were included after the manual search. Fig. 1 shows the detailed study identification and selection flow chart. We updated the additional data of each included cohort in January 2022 and received the data of 821 patients from eight independent cohorts. After screening, 23 patients were excluded, leaving a total of 798 patients in our final IPD-MA. Baseline study and patient characteristics The total analysis set comprised 798 patients from eight international patient cohorts. Four cohorts were from the USA (n = 451), two cohorts were from Europe (n = 123), and the remaining two cohorts were from Asia (n = 224). Two cohorts were retrospective studies, and the remaining cohorts were prospectively collected; two cohorts used 1.5-T MRI scanners, and six cohorts used 3.0-T MRIscanners. A 2D SE-EPI sequence was performed in four cohorts, and the remaining with a GRE sequence; all the studies had shear waves generated at 60 Hz. Five cohorts used the NASH-CRN scoring system as a reference standard. Two cohorts were based on the SAF scoring system, and one cohort was based on the Brunt scoring system. Baseline study characteristics are given in Table 1. The mean age of the pooled cohort was 51.6 ± 14.8 years, and 44% were male with a mean BMI of 31.9 ± 6.0 kg/m 2 . The median interval between the performance of MRE and liver biopsy was 41 days (IQR 15–87.25 days). The mean liver 594 Journal of Hepatology, September 2023. vol. 79 j592–604 Cut-offs for and confounders of NAFLD-fibrosis staging with magnetic resonance elastography stiffness across the entire cohort was 3.26 ± 1.51 kPa, ranging from 1.2 to 10.9 kPa. The distribution of fibrosis in the pooled cohort was as follows: F0, 203 (25.4%); F1, 281 (35.2%); F2, 122 (15.3%); F3, 123 (15.4%); and F4, 69 (8.7%). Fig. 2 shows the mean liver stiffness values corresponding to each histological liver fibrosis stage. The distribution of steatosis was as follows: grade 0, 5.5%; grade 1, 42.7%; grade 2, 37.5%; and grade 3, 14.3%. According to the SAF and NASH-CRN scoring systems, 425 patients were defined as NASH. The remaining important clinical information is available in Table 2. Quality of included studies The methodological quality of the studies assessed using the QUADAS-2 tool is summarised in Fig. S2A and B. The retrospective design of two cohorts introduced unclear risks in the patient selection domain. All studies did not report predefined cut-off values and were judged as having a high risk of bias in the index test domain. The reference standard domain was judged to have an unclear risk of bias in all included studies owing to the inherent limitations of liver biopsy. Pooled cut-offs and diagnostic accuracy of MRE The diagnostic performance of MRE for staging fibrosis and heterogeneity (I 2 ) is shown in Table 3, and the main pooled results are depicted in Fig. 3A–D and Fig. S3A–D. For mild fibrosis (> −F1), analysis was done on seven cohorts; one cohort was not included as there were not enough patients to calculate reliable cut-off values. The optimal cut-off value determined in the meta-analysis was 2.65 kPa (95% CI 2.52–2.78 kPa), with an I 2 statistic of 17.05. For significant fibrosis (> −F2), analysis of the cut-off value was done with all eight cohorts. The optimal cut-off value determined in the meta-analysis was 3.14 kPa (95% CI 3.01–3.27 kPa), with an I 2 statistic of 44.88. 223 records excluded after screening title and abstract 3 articles by 3 independent authors did not respond 13 full-text articles were excluded with justification: 3 wrong patient population 6 different aims 2 duplicate cohorts 2 different reference standard Identification 2 additional independent cohorts (Sweden and Spain) with a total of 127 patients were also included after the manual search 23 patients excluded: Missing histopathologic data (n = 5) Missing MRE data (n = 18) 530 records identified through database searching: Pubmed: 167 Embase: 277 Cochrane: 86 252 records after duplicates removed 29 full-text articles assessed for eligibility 16 articles from 9 corresponding authors fulfilling the inclusion criteria were invited to share individual patient data 7 articles from the same corresponding author provided one cohort. 2 articles from the same corresponding author provided 1 cohort, and the remaining 4 corresponding authors each provided an independent cohort. A total of 694 patients were enrolled 8 independent cohorts with a total of 798 patients included for individual patient data meta-analysis Screening Eligibility Included Fig. 1. Study identification and selection flow chart. MRE, magnetic resonance elastography. Journal of Hepatology, September 2023. vol. 79 j592–604 595 Research Article For advanced fibrosis (> −F3), analysis of the cut-off value was done with all eight cohorts. The optimal cut-off value determined in the meta-analysis was 3.53 kPa (95% CI 3.40–3.66 kPa), with an I 2 statistic of 41.15. For cirrhosis (F4), analysis was done on six cohorts; two cohorts were not included owing to the low prevalence of cirrhosis to calculate reliable cut-off values. The optimal cut-off value determined in the random-effects meta-analysis was 4.45 kPa (95% CI 3.63–5.27 kPa), with an I 2 statistic of 85.24. Subgroup and sensitivity analysis In subgroup analysis (Table S1), the AUROC (0.86, 95% CI 0.82–0.90) in patients with type 2 diabetes was significantly higher than the AUROC (0.75, 95% CI 0.70–0.79, p<0.01) in patients without type 2 diabetes for staging mild fibrosis. The presence of NASH, as defined by histopathological scoring systems, showed significantly lower diagnostic accuracy than did the absence of NASH in staging significant fibrosis (no- NASH 0.93 [0.90–0.96] vs. NASH 0.87 [0.83–0.90], p<0.01) and advanced fibrosis (no-NASH 0.96 [0.93–0.98] vs.NASH0.90 [0.87–0.93], p<0.01). The diagnostic accuracy of MRE was not affected by other factors. Patients with an interval between MRE and liver biopsy >6and>3 months and high R2* (R2* >80 Hz) were removed from the sensitivity analysis, and the diagnostic performance of MRE in the remaining patients was not significantly different compared with the entire IPD study group. Meta-regression Univariate meta-regressions showed that these study-level variables (population, study design, field strength, sequence, and manufacturer of MRI) were not associated with heterogeneity (see Fig. S4A–D). Comparison of three cut-off value systems in the overall cohort A random sampling group was used as a ‘validation set’to avoid overlapping of data as often as possible. The diagnostic performance of these three IPD-MAs (our current study, the study by Singh et al., 14 and the study by Hsu et al. 15 ) for staging liver fibrosis is shown in Table S2. However, there was no significant difference in accuracy between these three cut-off value systems. Table 1. Study characteristics of each independent cohort. Centre Country Study design Duration Patients (n) Field strengths (T) Manufacturer Sequence Histological criteria Interval between MRE and biopsy (days), median (IQR) Rochester 28 USA Retrospective January 2007 to March 2010 58 1.5 GE GRE Brunt 31 (9–40) Yokohama 10 Japan Prospective July 2013 to April 2015 142 3.0 GE SE-EPI NASH-CRN 60 (24–121) Incheon 35 Korea Prospective June 2017 to May 2020 82 3.0 Siemens SE-EPI NASH-CRN 1 (1–7) Pittsburgh 33 USA Prospective October 2015 to December 2017 59 1.5 GE GRE NASH-CRN 82 (46–150) Los Angeles 34 USA Retrospective May 2016 to March 2019 101 3.0 Siemens SE-EPI NASH-CRN 92 (39–158) Seville 42 Spain Prospective November 2019 to December 2020 61 3.0 Philips GRE SAF 77 (38–119) San Diego 11,12,36–40 USA Prospective January 2012 to Jane 2020 233 3.0 GE GRE NASH-CRN 34 (14–71) Uppsala 43 Sweden Prospective March 2017 to December 2019 62 3.0 GE SE-EPI SAF 56.5 (37–79) GRE, gradient-echo sequence; CRN, Clinical Research Network; SAF, steatosis–activity–fibrosis; SE-EPI, spin-echo echo-planar imaging. F0 F1 F2 F3 F4 0.0 2.0 4.0 6.0 8.0 10.0 12.0 Fibrosis stage MRE (kPa) Prevalence: 25.4% Prevalence: 15.3% Prevalence: 15.4% Prevalence: 8.7% Prevalence: 35.2% Fig. 2. Liver stiffness values by MRE corresponding to each liver fibrosis stage. The bars represent the minimum and maximum; the top and bottom lines in the box represent the 25% and 75% levels, respectively; and the middle line indicates the median level. MRE, magnetic resonance elastography. 596 Journal of Hepatology, September 2023. vol. 79 j592–604 Cut-offs for and confounders of NAFLD-fibrosis staging with magnetic resonance elastography Factors associated with liver stiffness measured by MRE Table 4 shows the results of the LMM to determine the influence of covariates on MRE values. Age, GGT concentration, platelet count, fibrosis stage, and NASH-SA were found to have significant and relevant relationships with LSM by MRE. Factors affecting diagnostic accuracy of MRE In GLMM, we found that NASH-SA (odds ratio [OR] 2.448, 95% CI 1.180–5.079, p<0.05) and high GGT level (>120 U/L) (OR 3.388, 95% CI 1.577–7.278, p<0.01) were independent factors causing overestimation (Table 5). Conducting a multivariate analysis excluding histological variables (which are difficult to obtain in clinical practice), high GGT level (OR 3.700, 95% CI 1.749–7.825, p<0.01) was the only significant variable in the multivariate analysis to be implemented in clinical practice (Table 6). No significant confounding factors were found between concordance and underestimation patients. Relationship between steatohepatitis presence, GGT levels, and LSM by MRE in every stage of liver fibrosis The relationship between LSM by MRE, GGT concentration (<60, 60–120, and >120 U/L), and NASH levels (no-NASH, NASH-MMA, and NASH-SA) at each fibrosis stage (F0–F4) was analysed using two-way ANOVA. The results showed that NASH-SA and higher GGT levels increased LSM by MRE, but only significantly in patients with mild fibrosis stage (F0 and F1), and did not interact with each other (Table S3). Notably, the LSM in the no-NASH group (5.14 kPa, 95% CI 4.70–5.57 kPa) was higher than that in the NASH-MMA group (4.58 kPa, 95% CI 3.82–5.33 kPa) in advanced fibrosis (F3–F4), although not statistically significant. The LSM ranges of no-NASH/NASHMMA and NASH-SA seemed to show a good distinction in the F0–F1 phase, although there was still a small overlap between them (Fig. S5). Publication bias According to Deeks’funnel plot asymmetry test, publication bias was not present (p>0.10) (Fig. S6A–D). Additional analysis The overall diagnostic accuracy of ‘rule-out’and ‘rule-in’cutoffs is presented in Table S4. To investigate the impact of fibrosis prevalence on the predictive value of LSM by MRE, we set out to calculate positive and negative predictive values by using a range of different prevalences. The prevalence figures were used to represent values from our cohort (> −F2, 39.4%; > −F3, 24.1%; and F4, 8.7%) and also values seen in cohorts of patients with T2DM, 44 and the general population. 45 For a diagnosis of > −F2, > −F3, and F4, there was a marked reduction in Table 2. Demographic details of the entire cohort. All (n = 798) F0 (n = 203, 25.4%) F1 (n = 281, 35.2%) F2 (n = 122, 15.3%) F3 (n = 123, 15.4%) F4 (n = 69, 8.7%) Age (years) 51.62 ± 14.77 47.25 ± 13.86 48.05 ± 15.05 53.04 ± 14.69 60.68 ± 11.67 60.31 ± 9.35 Sex (male/%) 353 (44) 90 (44) 126 (45) 63 (52) 45 (37) 29 (42) Weight (kg) 89.05 ± 20.86 86.91 ± 17.88 91.21 ± 21.13 91.66 ± 22.38 85.63 ± 21.82 87.26 ± 21.48 BMI (n = 764) <30 kg/m 2 30–35 kg/m 2 >35 kg/m 2 31.89 ± 5.97 304 (40) 249 (33) 211 (27) 30.84 ± 5.42 77 (42) 69 (37) 38 (21) 32.56 ± 6.05 103 (38) 79 (29) 89 (33) 31.92 ± 5.82 49 (40) 44 (36) 29 (24) 31.80 ± 6.37 50 (41) 35 (29) 36 (30) 32.14 ± 6.38 25 (38) 22 (33) 19 (29) T2DM (Y/%; n = 738) 310 (42) 34 (20) 103 (39) 55 (45) 76 (64) 42 (67) AHT (Y/%; n = 504) 196 (39) 20 (27) 57 (29) 41 (45) 53 (56) 25 (52) ALT (U/L) 46.0 (30.0–73.0) 37.0 (27.5–58.0) 47.0 (30.0–79.0) 53.0 (33.0–79.0) 54.5 (37.0–79.0) 38.0 (28.0–53.0) AST (U/L) 38.2 (26.0–57.0) 29.0 (20.0–36.0) 40.0 (27.5–60.5) 45.0 (30.0–69.4) 50.0 (30.7–69.00) 40.0 (26.0–55.0) AST/ALT 0.82 (0.62–1.09) 0.74 (0.59–0.97) 0.80 (0.57–1.13) 0.81 (0.63–1.09) 0.91 (0.71–1.12) 1.00 (0.74–1.23) GGT (U/L) 55.0 (33.0–98.3) 38.5 (23.5–83.0) 52.5 (30.9–87.1) 50.5 (31.0–88.8) 64.0 (39.0–127.5) 96.0 (58.5–158.8) Albumin (g/dl) 4.42 ± 0.51 4.50 ± 0.39 4.48 ± 0.39 4.42 ± 0.74 4.34 ± 0.42 4.07 ± 0.71 Glucose (mg/dl) 111.69 ± 44.38 103.35 ± 33.05 110.49 ± 41.77 117.61 ± 51.26 117.58 ± 53.44 117.62±47.08 Triglycerides (mg/dl) 163.64 ± 94.69 153.83 ± 89.05 169.75 ± 97.88 162.68 ± 80.25 182.74 ± 117.96 138.04±69.02 Cholesterol (mg/dl) 183.72 ± 41.76 186.67 ± 37.63 189.96 ± 42.72 181.52 ± 41.18 176.53 ± 43.37 166.86±40.85 Platelet (×10 9 /L) 242.45 ± 79.49 259.09 ± 75.74 261.68 ± 79.18 234.39 ± 67.95 207.02 ± 74.46 197.95±79.67 HDL cholesterol (mg/dl) 46.01 ± 17.67 46.75 ± 12.89 46.10 ± 19.71 43.36 ± 13.11 46.50 ± 14.89 47.70±28.89 LDL cholesterol (mg/dl) 103.20 ± 42.22 107.61 ± 36.53 110.57 ± 44.67 100.30 ± 38.83 90.36 ± 44.31 89.43±40.10 MRE (kPa) 3.26 ± 1.51 2.32 ± 0.47 2.63 ± 0.71 3.47 ± 1.16 4.58 ± 1.39 5.91±1.84 MRI-PDFF (%) 14.62 ± 8.74 12.66 ± 8.36 17.16 ± 9.38 15.94 ± 8.33 13.24 ± 6.81 10.16±7.33 R2* (Hz; n = 257) 55.0 (48.2–64.7) 51.0 (46.5–55.5) 59.3 (51.5–67.3) 57.2 (51.8–69.0) 55.2 (48.3–58.7) 47.8 (42.6–53.3) Steatosis (0/1/2/3) 44 (5.5)/341 (42.7)/299 (37.5)/ 114 (14.3) 35 (17)/97 (48)/49 (24)/ 22 (11) 5 (2)/106 (38)/128 (45)/ 42 (15) 1 (1)/47 (39)/49 (40)/ 25 (20) 0 (0)/56 (45)/49 (40)/ 18 (15) 3(4)/35(51)/ 24(33)/7(12) NASH (n = 740; no/MMA/SA) 315 (39)/172 (27)/253 (34) 124 (71)/29 (17)/21 (12) 99 (37)/79 (30)/88 (33) 29 (24)/40 (34)/50 (42) 39 (33)/20 (17)/59 (50) 24(39)/4(6)/35(55) Continuous variables are displayed as mean ± SD, or median (IQR). Categorical variables are displayed as n (%). ‘(%)’represents the percentage in this fibrosis stage. AHT, arterial hypertension; ALT, alanine aminotransferase, AST, aspartate aminotransferase, GGT, gamma-glutamyltransferase, MMA, mild–moderate activity; MRE, magnetic resonance elastography; MRI-PDFF, magnetic resonance imaging proton density fat fraction; NASH, non-alcoholic steatohepatitis; SA, severe activity; T2DM, type 2 diabetes mellitus. Journal of Hepatology, September 2023. vol. 79 j592–604 597 Research Article Table 3. The diagnostic performance in each independent cohort and in meta-analysis. Pittsburgh (n = 59) Incheon (n = 82) Yokohama (n = 142) Rochester (n = 58) Seville (n = 61) Los Angeles (n = 101) San Diego (n = 233) Uppsala (n = 62) Meta-analysis (95% CI) I 2 > −F1 Prevalence 98.0% 70.7% 90.1% 50% 78.7% 81.2% 57.0% 93.5% 74.6% Cut-off, kPa ND 2.50 (2.20–2.80) 2.40 (2.20–3.60) 2.98 (2.5–3.17) 2.72 (2.66–3.63) 2.74 (2.7–3.23) 2.66 (2.28–3.22) 2.47 (2.25–2.82) 2.65 (2.52–2.78) 17.05% AUROC ND 0.65 (0.53–0.77) 0.85 (0.78–0.91) 0.88 (0.79–0.96) 0.87 (0.76–0.97) 0.84 (0.75–0.91) 0.81 (0.75–0.86) 0.58 (0.34–0.81) 0.82 (0.79–0.85) Sensitivity ND 47% (33–60%) 77% (68–84%) 83% (64–94%) 85% (72–94%) 71% (60–80%) 61% (52–70%) 57% (43–70%) 69% (59–78%) 81.84% Specificity ND 88% (68–97%) 86% (64–100%) 77% (58–90%) 85% (55–98%) 95% (74–100%) 80% (69–86%) 75% (19–99%) 82% (76–87%) 0.00% PLR ND 3.72 (1.2–11.1) 5.46 (1.51–19.75) 4.00 (1.9–8.3) 5.55 (1.5–20.0) 13.44 (2.0–91.0) 3.86 (2.4–6.2) 2.28 (0.4–12.6) 3.8 (2.70–5.30) NLR ND 0.61 (0.5–0.8) 0.26 (0.17–0.38) 0.22 (0.10–0.5) 0.17 (0.08–0.4) 0.31 (0.2–0.4) 0.46 (04–0.6) 0.57 (0.3–1.1) 0.38 (0.27–0.52) DOR ND 6.1 (1.6–22.7) 21.49 (4.5–101.4) 18.4 (4.9–68.7) 32.2 (5.8–177.5) 43.5 (5.5–344.4) 7.6 (4.1–14.2) 4.0 (0.4–40.4) 10.0 (6.0–18.0) > −F2 Prevalence 71.2% 15.9% 54.2% 24.1% 50.8% 42.6% 27.7% 43.5% 39.4% Cut-off, kPa 3.03 (2.35–3.4) 2.80 (2.3–4.0) 3.20 (2.80–3.60) 3.17 (2.90–3.95) 3.47 (2.72–3.72) 2.86 (2.5–3.25) 3.34 (3.31–3.73) 2.82 (2.40–2.99) 3.14 (3.01–3.24) 44.88% AUROC 0.85 (0.74–0.95) 0.74 (0.57–0.91) 0.92 (0.86–0.96) 0.94 (0.84–0.98) 0.88 (0.79–0.97) 0.91 (0.83–0.96) 0.93 (0.89–0.97) 0.74 (0.61–0.87) 0.92 (0.90–0.94) Sensitivity 69% (55–81%) 54% (25–81%) 86% (78–94%) 100% (77–100%) 81% (63–93%) 93% (81–98%) 76% (63–85%) 52% (32–71%) 79% (67–88%) 75.00% Specificity 88% (64–98%) 91% (82–97%) 87% (78–95%) 73% (57–85%) 83% (65–94%) 79% (67–89%) 95% (91–98%) 91% (77–98%) 89% (82–94%) 67.64% PLR 4.94 (1.54–15.88) 6.20 (2.5–15.5) 6.72 (3.57–12.64) 3.67 (2.3–5.9) 4.84 (2.1–11.0) 4.50 (2.7–7.5) 26.15 (10.9–62.7) 6.05 (1.9–18.9) 7.30 (4.90–10.80) NLR 0.37 (0.23–0.59) 0.51 (0.3–0.9) 0.14 (0.077–0.256) 0.05 (0.00–0.71) 0.23 (0.1–0.5) 0.09 (0.03–0.3) 0.24 (0.2–0.4) 0.53 (0.4–0.8) 0.22 (0.13–0.36) DOR 16.7 (3.3–84.0) 12.3 (3.1–48.4) 53.8 (19.5–148.6) 75.4 (4.2–136.7) 20.8 (5.6–77.2) 51.1 (13.5–194.1) 110.0 (38.1–317.6) 11.5 (2.8–46.8) 33.0 (20.0–56.0) > −F3 Prevalence 39.0% 8.5% 31.7% 20.9% 39.3% 35.6% 16.2% 11.3% 24.1% Cut-off, kPa 3.40 (3.25–3.8) 3.10 (2.3–4.0) 4.10 (3.20–4.50) 3.95 (3.19–5.81) 3.72 (3.62–4.08) 3.06 (2.7–4.19) 3.60 (3.22–3.86) 3.31 (3.24–3.80) 3.53 (3.40–3.66) 41.15% AUROC 0.95 (0.89–0.99) 0.88 (0.74–0.99) 0.905 (0.84–0.95) 0.92 (0.85–0.99) 0.88 (0.78–0.97) 0.90 (0.84–0.97) 0.95 (0.92–0.98) 0.99 (0.94–0.99) 0.92 (0.90–0.94) Sensitivity 87% (67–95%) 71% (29–96%) 91% (82–98%) 82% (48–98%) 75% (53–90%) 92% (78–98%) 87% (72–96%) 100% (47–100%) 87% (80–92%) 24.42% Specificity 94% (81–99%) 96% (89–99%) 78% (70–86%) 89% (77–96%) 86% (71–95%) 77% (65–86%) 94% (90–97%) 94% (85–99%) 88% (81–94%) 82.98% PLR 31.3 (4.5–217.6) 17.86 (5.4–59.5) 3.74 (2.43–5.76) 3.54 (2.2–5.6) 9.25 (3.1–28.0) 3.97 (2.5–6.3) 14.26 (8.1–25.0) 23.6 (5.8–95.0) 7.8 (4.7–13.0) NLR 0.13 (0.05–0.40) 0.30 (0.09–1.0) 0.12 (0.04–0.33) 0.05 (0.004–0.82) 0.27 (0.1–0.5) 0.11 (0.04–0.33) 0.14 (0.06–0.3) 0.15 (0.02–0.9) 0.14 (0.09–0.22) DOR 233.3 (22.7–2,395.6) 60.0 (8.1–445.9) 37.1 (11.9–115.4) 62.0 (3.4–1123.5) 34.0 (7.6–152.2) 36.7 (9.8–136.6) 10.9 (4.1–28.7) 315.0 (13.8–7,214.5) 55.0 (31.0–98.0) F4 Prevalence 15.3% 1.2% 7.7% 12.1% 11.5% 17.8% 6.4% 4.8% 8.7% Cut-off, kPa 3.46 (3.13–4.45) ND 6.40 (5.40–7.20) 4.73 (3.33–5.81) 3.82 (3.72–5.00) 4.39 (3.98–5.51) 4.14 (3.22–4.58) ND 4.45 (3.67–5.22) 85.24% AUROC 0.95 (0.85–0.99) ND 0.95 (0.92–0.98) 0.94 (0.86–1.00) 0.90 (0.81–0.99) 0.87 (0.80–0.95) 0.95 (0.92–0.98) ND 0.94 (0.92–0.96) Sensitivity 100% (66–100%) ND 82% (48–98%) 86% (42–100%) 100% (59–100%) 72% (47–90%) 87% (60–98%) ND 88% (71–96%) 5.82% Specificity 76% (62–87%) ND 95% (89–97%) 90% (79–97%) 78% (64–88%) 89% (80–95%) 93% (89–96%) ND 89% (82–93%) 77.68% PLR 4.05 (2.39–6.84) ND 16.50 (7.1–32.6) 8.74 (3.6–21.2) 4.08 (2.3–7.2) 6.66 (3.4–13.1) 12.83 (7.9–21.0) ND 7.8 (5.1–11.9) NLR 0.07 (0.004–0.98) ND 0.10 (0.03–0.59) 0.16 (0.03–1.00) 0.16 (0.03–1.0) 0.31 (0.1–0.7) 0.07 (0.01–0.5) ND 0.13 (0.05–0.36) DOR 65.3 (3.5–1,208.4) ND 165.1 (17.6–1,344.2) 55.2 (5.5–555.8) 51.0 (2.7–956.4) 21.4 (6.2–74.0) 189.5 (23.3–118.2) ND 59.0 (21.0–169.0) The bootstrap method and maximum Youden index were applied to estimate the optimal cut-off and 95% CI. In each cohort, ROC analysis was used to estimate AUROC, sensitivity, specificity, PLR, NLR, and DOR. The bivariate randomeffects model was used for the computation. Heterogeneity was assessed using the Higgins inconsistency index test. AUROC, area under the ROC curve; DOR, diagnostic odds ratio; ND, not determined (the calculation was not possible owing to the low prevalence); NLR, negative likelihood ratio; PLR, positive likelihood ratio; ROC, receiver operating curve. 598 Journal of Hepatology, September 2023. vol. 79 j592–604 Cut-offs for and confounders of NAFLD-fibrosis staging with magnetic resonance elastography the positive predictive value as the prevalence of fibrosis was lowered (Table S5). Discussion This IPD-MA, which included data-independent cohorts from Europe, Asia, and the USA with a total of 798 patients, showed that MRE has a good or excellent diagnostic performance with AUROCs of 0.82, 0.92, 0.92, and 0.94 for staging mild (> −F1), significant (> −F2), or advanced (> −F3) fibrosis and cirrhosis (F4), respectively, in patients with NAFLD. However, discordance between MRE and pathological findings was still observed in some patients. It is recognised that this may, at least in part, be attributable to histology being an imperfect reference standard. 6,46 Although the AUROC has been the most recommended and applied method for evaluating the performance of numerical diagnostic tests, in this study, we also used multilevel modelling approaches (LMM and GLMM) to analyse the confounding factors. 47 Final binary outcomes were evaluated based on the misclassification results, which may be more clinically relevant. Compared with the conventional linear and logistic regression models, LMM and GLMM are now more widely accepted statistical analysis tools using hierarchical (random-effects) data that account for the clustering of patients within studies. 27 LMM consistently identified age, GGT level, platelet count, greater steatohepatitis grade, and liver fibrosis stage to be associated with LSM by MRE. Platelet count and age had been included in several indices, such as aspartate transaminase (AST)-to-platelet ratio index, fibrosis-4 index, and Hepamet fibrosis score for the non-invasive prediction of hepatic fibrosis. 7,48 GGT is present in the bile canaliculi of hepatocytes and biliary epithelial cells. It is strongly associated with oxidative stress, which may contribute to clinical progression from simple fatty liver to NASH. 49 In addition, a recent clinical trial of paediatric NASH 50 showed that GGT had a stronger relationship with improvement in NAS than aminotransferase (ALT and AST). GGT was associated with the development of metabolic syndrome (MetS) in a non-linear dose-response relationship. 51 It also can be considered a more sensitive and stronger biomarker of MetS than aminotransferases (ALT and AST). A previous IPD-MA 52 with a large cohort (n = 16,802) showed that significant increases in liver stiffness by TE were associated with MetS. This may suggest that patients with NAFLD with MetS may exhibit higher liver stiffness than patients with NAFLD without MetS. The impact of inflammation on ultrasound elastography biomarkers had been reported in two previous IPD-MAs on TE 53 and 2D SWE 54 for diagnosing liver fibrosis, which used AST and ALT to indicate liver inflammatory activity rather than pathological findings. Furthermore, similar results were found in several single-centre studies on MRE, 55–57 where higher inflammation activity was associated with higher LSM by MRE. Tissue inflammation increases local Yokohama Rochester Los Angeles Incheon Seville San Diego Uppsala 1.5 22.5 33.5 2.40 [1.70, 3.10] 2.98 [2.64, 3.32] 2.74 [2.48, 3.00] 2.50 [2.20, 2.80] 2.72 [2.24, 3.21] 2.66 [2.19, 3.13] 2.47 [2.19, 2.75] 2.65 [2.52, 2.78] Fixed-effects inverse-variance model Overall Heterogeneity: I2 = 17.05%, H2 = 1.21 Test of θi = θj: Q(6) = 7.23, p = 0.30 Test of θ = 0: z = 39.39, p = 0.00 Cohort Effect size with 95% CI Yokohama Pittsburgh Rochester Los Angeles Incheon Seville San Diego Uppsala 3.20 [2.80, 3.60] 3.03 [2.50, 3.56] 3.17 [2.65, 3.70] 2.86 [2.48, 3.23] 2.80 [1.95, 3.65] 3.47 [2.97, 3.97] 3.34 [3.13, 3.55] 2.82 [2.52, 3.11] 3.14 [3.01, 3.27] Fixed-effects inverse-variance model Overall Heterogeneity: I2 = 44.88%, H2 = 1.81 Test of θi = θj: Q(7) = 12.70, p = 0.08 Test of θ = 0: z = 47.69, p = 0.00 22.5 33.5 4 Cohort Effect size with 95% CI Yokohama Pittsburgh Rochester Los Angeles Incheon Seville San Diego Uppsala 2 3 4 5 4.10 [3.45, 4.75] 3.40 [3.13, 3.68] 3.95 [2.64, 5.26] 3.06 [2.31, 3.80] 3.10 [2.25, 3.95] 3.72 [3.49, 3.95] 3.60 [3.28, 3.92] 3.31 [3.03, 3.59] 3.53 [3.40, 3.66] Fixed-effects inverse-variance model Overall Heterogeneity: I2 = 41.15%, H2 = 1.70 Test of θi = θj: Q(7) = 11.90, p = 0.10 Test of θ = 0: z = 53.92, p = 0.00 Effect size with 95% CICohort 34567 Effect size with 95% CI 6.40 [5.50, 7.30] 3.46 [2.80, 4.12] 4.73 [3.49, 5.97] 4.39 [3.62, 5.15] 3.82 [3.18, 4.46] 4.14 [3.46, 4.82] 4.45 [3.63, 5.27] Yokohama Pittsburgh Rochester Los Angeles Seville San Diego Overall Heterogeneity: τ2 = 0.88, I2 = 85.24%, H2 = 6.77 Test of θi = θj: Q(5) = 29.96, p = 0.00 Test of θ = 0: z = 10.60, p = 0.00 Cohort Random-effects REML model AB CD Fig. 3. Pooled cut-offs for staging liver fibrosis stage in patients with NAFLD. (A) Pooled cut-off for mild fibrosis (> −F1). (B) Pooled cut-off for significant fibrosis (> −F2). (C) Pooled cut-off for advanced fibrosis (> −F3). (D) Pooled cut-off for cirrhosis (F4). Pooled cut-offs were estimated by study-level cut-offs in fixed or randomeffects model meta-analyses and were displayed graphically as forest plots. The diamond symbol represents the pooled cut-off value, and its width represents the 95% CI. REML, restricted maximum likelihood. Journal of Hepatology, September 2023. vol. 79 j592–604 599 Research Article