Society: AASLD

Introduction
The rising prevalence of non-alcoholic fatty liver disease (NAFLD) will result in increased referrals to gastroenterology and hepatology units. Non-invasive tests (NITs) predict advanced liver fibrosis in NAFLD, and NIT thresholds help triage referrals for patients who require assessment in secondary care. However, these cut-offs may not appropriately predict long term risk of liver-related morbidity, and many patients continue having secondary care follow-up with low-risk disease. This study prospectively assessed the utility of NITs in predicting hepatic morbidity and mortality.

Methods
The NAFLD-fibrosis score (NFS) and Fibrosis-4 Index (FIB-4) were calculated at baseline in patients with NAFLD recruited between 2002 to 2021. The primary study endpoints were incident liver-related events (composite of hepatic decompensation, hepatocellular carcinoma, liver transplantation, and liver-related death) and all-cause mortality. Univariable and multivariable Cox’s regression models assessed associations of baseline factors with study endpoints. Multivariable models tested NIT thresholds (Low risk, NFS -1.455, FIB-4 1.3; Medium risk, NFS 0.20, FIB-4 2.0; High risk, NFS 0.676, FIB-4 2.67) in predicting liver-related events (LRE) and mortality.

Results
In total, 487 patients were included (187 females; median age 49 years (IQR 38-58); median body mass index 32.3 kg/m²(IQR 29.0-37.0); median follow up 44 months (IQR 17-88)). LRE was observed in 18 patients at a median follow-up of 43 months (IQR 16-87). Baseline NFS (adjusted Hazard ratio (aHR)=2.17, 95% CI 1.51-3.13) and FIB-4 (aHR=1.88, 95% CI 1.50-2.35) scores were independently associated with LRE. Death was observed in 27 patients at a median follow-up of 69 months (IQR 36-112). Baseline NFS (aHR=2.08, 95% CI 1.50-2.88) and FIB-4 (aHR=1.40, 95% CI 1.14-1.72) scores were independently associated with all-cause mortality. Low risk NIT thresholds (NFS -1.455, FIB-4 1.3) that are commonly used to triage patients from primary to secondary care did not predict either study endpoint (p>0.05) (Table 1). Medium NIT thresholds predicted LRE (aHR_NFS=10.70, 95% CI 2.68-42.74, Negative predictive value (NPV)_NFS=0.99; aHR_FIB-4=10.05, 95% CI 2.74-36.87, NPV_FIB-4=0.99) and all-cause mortality (aHR_NFS=6.71, 95% CI 2.28-19.71, NPV_NFS=0.98; aHR_FIB-4=5.51, 95% CI 1.91-15.88, NPV_FIB-4=0.98) (Table 1, Figure 1). Liver-related events were observed in 1 patient (<2%) with indeterminate NITs between low and medium risk thresholds (NFS -1.455 – 0.20; FIB-4 1.3 – 2.0).

Conclusions
NITs accurately predict liver-related events and all-cause mortality in patients with NAFLD. Higher NIT thresholds better predict liver-specific prognosis than those used in current referral pathways. Higher NIT thresholds predict liver-related events for up to 3 years and can guide resource utilization once patients enter secondary care.

<b>Table 1.</b> Associations of non-invasive tests of liver fibrosis (FIB-4, fibrosis-4 index; NFS, non-alcoholic fatty liver disease fibrosis score) at different thresholds in predicting the risk of liver-related events and death in patients with non-alcoholic fatty liver disease using multivariable Cox regression models.

Table 1. Associations of non-invasive tests of liver fibrosis (FIB-4, fibrosis-4 index; NFS, non-alcoholic fatty liver disease fibrosis score) at different thresholds in predicting the risk of liver-related events and death in patients with non-alcoholic fatty liver disease using multivariable Cox regression models.

<b>Figure 1.</b> Kaplan Meier curves showing survival free of liver-related events and death in patients with non-alcoholic fatty liver disease (NAFLD) when stratified by baseline non-invasive tests of liver fibrosis including the NAFLD fibrosis score (NFS, A and C, respectively) and fibrosis-4 index (FIB-4, B and D, respectively).

Figure 1. Kaplan Meier curves showing survival free of liver-related events and death in patients with non-alcoholic fatty liver disease (NAFLD) when stratified by baseline non-invasive tests of liver fibrosis including the NAFLD fibrosis score (NFS, A and C, respectively) and fibrosis-4 index (FIB-4, B and D, respectively).

Background and Aims: A multidisciplinary task force commissioned by the American Gastroenterological Association (AGA) recently developed a clinical care pathway with guidance on screening high-risk populations including those with type 2 diabetes (T2DM). We aimed to validate the pathway in a prospectively recruited cohort of patients with T2DM and evaluate the diagnostic performance of new American Association for the Study of Liver Disease (AASLD) practice guidelines with enhanced liver fibrosis (ELF) testing.

Method: This prospective study enrolled adults age ≥ 50 years with T2DM recruited from primary care or endocrinology clinics. Participants underwent a standardized clinical research visit with magnetic resonance imaging proton-density-fat-fraction (MRI-PDFF), magnetic resonance elastography (MRE), vibration-controlled transient elastography (VCTE) and controlled attenuation parameter (CAP) and ELF testing. The primary outcome was the diagnostic performance of the AGA clinical pathway and AASLD pathway with ELF for advanced fibrosis using MRE ≥ 3.63 kPa as the reference.

Results: 417 patients (36% men) with T2DM with FIB-4 and MRE data were included. The mean (±SD) age and BMI were 65 (±8) years and 30 (±5) kg/m2, respectively. The prevalence of NAFLD (MRI-PDFF ≥ 5% after exclusion of other liver diseases) was 64% and 12% had advanced fibrosis (MRE ≥ 3.63 kPa). FIB-4 values of < 1.3, 1.3-2.67 and ≥ 2.67 were present in 208 (50%), 183 (44%), and 26 (6%) of patients respectively. VCTE values < 8 kPa, 8-12 kPa and ≥ 12 kPa were present in 309 (77%), 58 (14%), and 36 (9%) of patients respectively. ELF scores of < 9.8, 9.8-11.3 and > 11.3 were present in 236 (58%), 146 (36%) and 22 (6%) patients respectively. Applying the AGA pathway (N=403), 202 patients were low risk by FIB-4 and 128 additional patients had VCTE < 8 kPa and were classified as low risk. The false negative rate was 3.3% and 18% would qualify for specialty referral (Figure 1A). Applying the expanded pathway with ELF (N=404), 200 patients were low risk by FIB-4 and 85 additional patients had low ELF and were low risk. The false negative rate was 4.8% and 29% would qualify specialty referral (Figure 1B).

Conclusion: Validation of the AGA clinical pathway in a well-phenotyped, prospectively recruited cohort with T2DM revealed a low false negative rate, 3.3% and identified 82% of patients as low-risk. An alternative pathway with FIB-4 + ELF, which does not require VCTE prior to specialty referral, had a low false negative rate and similar performance and may serve as a viable alternative for primary care and endocrinology clinics without access to VCTE.

<b>Figure: </b>Validation of the AGA Clinical Pathway for NAFLD (A) and an expanded pathway using ELF (B) with MRE as the reference standard in patients with type 2 Diabetes Mellitus.

Figure: Validation of the AGA Clinical Pathway for NAFLD (A) and an expanded pathway using ELF (B) with MRE as the reference standard in patients with type 2 Diabetes Mellitus.

Background
The Fibroscan-aspartate aminotransferase (FAST) score is a non-invasive test developed to identify patients with at-risk non-alcoholic steatohepatitis (NASH) and incorporates AST with vibration-controlled transient elastography (VCTE) parameters of liver stiffness measurement (LSM) and controlled attenuation parameter (CAP). However, histological steatohepatitis has not been consistently associated with risk of liver-related events (LREs) independent of fibrosis stage, possibly due to small cohort sizes, and it is unclear if FAST is associated with LREs in patients with established non-alcoholic fatty liver disease (NAFLD). We sought to determine whether the FAST score predicts LREs in NAFLD.

Methods
This was a retrospective study of consecutive patients with NAFLD undergoing VCTE seen at the University of Michigan Health System from 2015-2021. NAFLD was defined by hepatic steatosis and absence of alternative chronic liver diseases or excess alcohol intake. We included all patients undergoing VCTE with measurement of LSM and CAP, who had an AST value within 6 months of the VCTE. The index date was set as the date of VCTE. The primary outcome was LREs defined as hepatic decompensation or hepatocellular carcinoma. The primary predictor was FAST score, stratified by established cutoffs (PMID 32027858): low (<= 0.35), indeterminate (0.35-0.67), and high (>= 0.67). We computed incidence rates for LREs based on number of events divided by follow-up time, with confidence intervals and p values calculated using a Poisson distribution. We conducted subgroup analysis in patients with compensated advanced chronic liver disease (cACLD), defined by LSM >= 10 kPA.

Results
We included 1,143 NAFLD patients with available FAST scores. Median age was 51 years, 47% were female, and 79% were white. Probability of NASH based on FAST score was low, indeterminate, and high in 40%, 40%, and 20% of patients, respectively, and LSM was >10 kPa in 24%. Median follow-up was 35 months during which time 12 patients experienced LREs. High FAST score predicted higher incidence rate of LREs than low or indeterminate FAST: 14.2 (6.5-27.0) vs. 1.2 (0.2-3.4) events/1,000 person-years (p<0.0001; Figure 1). In a subgroup analysis among patients with cACLD, high FAST score was associated with higher incidence of LREs than low/intermediate FAST score, with incidence rate 18.6 (8.5-35.3) vs. 3.1 (0.1-17.4) events/1,000 person-years, respectively (p=0.045; Figure 2). In patients without cACLD, LREs occurred very infrequently (0.8 events/1000 person-years) with no difference based on FAST score (p=0.71).

Conclusions
The FAST score is predictive of LREs in patients with NAFLD, including among those with cACLD.

Objective: Non-alcoholic fatty liver disease (NAFLD)-related fibrosis is heritable, but it is unclear how family history may be used to identify first-degree relatives with advanced fibrosis. We aimed to develop and validate a simple risk score to identify first-degree relatives with NAFLD and advanced fibrosis.

Design: This prospective, cross-sectional, familial study consisted of a derivation cohort from San Diego, USA, and a validation cohort from Helsinki, Finland. This study included consecutive adult probands (n=242) with NAFLD and advanced fibrosis, NAFLD without advanced fibrosis, and non-NAFLD, with at least one of their first-degree relatives. All participants underwent evaluation of liver fibrosis using magnetic resonance elastography and other noninvasive imaging modalities. The primary objective was to create and validate a simple risk score utilizing readily available clinical parameters to detect NAFLD with advanced fibrosis among first-degree relatives. Univariable and multivariable logistic regression analyses were performed in the UCSD (derivation) cohort for factors associated with NAFLD and advanced fibrosis. To develop a simple risk score based on a points system, regression coefficients from the logistic regression model were transformed into scores by rounding to an integer. The score was then externally validated in the Helsinki (validation) cohort.

Results: A total of 396 first-degree relatives (64% male) were included. The median (IQR) age and BMI were 47 (32-62) years and 27.6 (24.1-32.5) kg/m², respectively. Age (1-point), male sex (1-point), diabetes (1-point), and a proband with NAFLD and advanced fibrosis (2-points) were independent predictors of NAFLD with advanced fibrosis among first-degree relatives in the derivation cohort (n=220) and formed the NAFLD Familial Risk score. The area under the receiver operator characteristic curve (AUC) of the NAFLD Familial Risk score for detecting NAFLD with advanced fibrosis was 0.89 in the validation cohort (n=176). The optimal cut-off based on Youden’s index that maximized both sensitivity and specificity of the NAFLD Familial Risk Score for identifying NAFLD with advanced fibrosis was 3 points, corresponding to a 13% risk of NAFLD with advanced fibrosis. The NAFLD Familial Risk score outperformed the Fibrosis-4 index in the combined cohort (AUC 0.85 versus 0.74, p=0.03) (Figure 1).

Conclusion: In summary, the NAFLD Familial Risk Score accurately identifies NAFLD with advanced fibrosis in first-degree relatives. It is simple and does not require a calculator or extensive laboratory investigations. These data have important implications for screening (Figure 2).

Figure 1. Area under the receiver operating characteristic curve (auROC) of the Non-alcoholic fatty liver disease (NAFLD) Familial Risk Score (FRS) versus the Fibrosis-4 index (FIB-4) for identifying NAFLD with advanced fibrosis among first-degree relatives in the combined cohort

Figure 1. Area under the receiver operating characteristic curve (auROC) of the Non-alcoholic fatty liver disease (NAFLD) Familial Risk Score (FRS) versus the Fibrosis-4 index (FIB-4) for identifying NAFLD with advanced fibrosis among first-degree relatives in the combined cohort

Proposed clinical algorithm to identify first-degree relatives at risk of non-alcoholic fatty liver disease (NAFLD) with advanced fibrosis using the NAFLD Familial Risk Score

Proposed clinical algorithm to identify first-degree relatives at risk of non-alcoholic fatty liver disease (NAFLD) with advanced fibrosis using the NAFLD Familial Risk Score

Background:
Approximately half of people with cirrhosis remain undiagnosed, leading to preventable morbidity and mortality. Population-level, risk-based screening for cirrhosis has been proposed as a potential solution. This pilot quality improvement project aimed to 1) develop a stepwise screening algorithm for cirrhosis in the Veterans Health Administration (VHA) health system and 2) assess the diagnostic yield, acceptability, and feasibility of this approach.

Methods:
Hepatology clinicians (“Champions”) at 11 VHA facilities were invited to participate in a pilot program from June 2020 to October 2022. Champions worked with local leadership to determine an acceptable outreach process locally. Each facility Champion received a list of potentially eligible patients generated from the VHA Corporate Data Warehouse. This list included patients aged 45-75 years with a FIB-4 ≥ 3.25, no prior diagnosis of cirrhosis or visit to a hepatology clinic, and at least one risk factor for cirrhosis (i.e., alcohol use disorder, diabetes, obesity, and/or viral hepatitis). Champions at each site performed chart reviews to confirm eligibility and either reached out to Veterans or sent a culled list to primary care physicians (PCP). Veterans were called and/or sent letters that offered staging elastography. Those with a liver stiffness measurement ≥ 8 kPa were deemed to have advanced fibrosis and were referred to hepatology care. Evaluation surveys were sent to patients and providers after completion of the program.

Results:
Four facilities (Ann Arbor, Pittsburgh, Puget Sound, and San Diego) completed the project. Of 490 patient charts reviewed, 219 patients (45%) were considered eligible for outreach. Champions and PCPs reached out to 166 of these patients (76%). Of the 53 (32%) patients who completed elastography (see Table 1), 21 (13%) were found to have advanced liver fibrosis and 71% (15/21) of these patients were subsequently seen by hepatology. PCP outreach resulted in a 47% (9/19) elastography completion rate compared to a 30% (44/147) completion rate when outreach was conducted by Champions, who were unknown to patients. Key barriers to site participation in the project and patient outreach included staffing limitations, lack of PCP comfort in interpreting and communicating results, and limited in-person appointments due to the COVID-19 pandemic. Of the 17 patients who completed surveys, 71% were satisfied with the process.

Conclusion:
This pilot project demonstrated the feasibility and acceptability of a population-level approach to cirrhosis screening in a nationalized healthcare system. However, more efficient approaches are needed to optimize patient selection and communication.

Demographics and Results of Patients who Completed Staging Elastography by Location

Demographics and Results of Patients who Completed Staging Elastography by Location

Jonathan Dounel

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