Diagnostic performance of FibroTest-ActiTest, transient elastography, and the fibrosis-4 index in patients with autoimmune hepatitis using histological reference

Abstract

BACKGROUND

Noninvasive tests are crucial for the management and follow-up of patients with autoimmune hepatitis, but their validation is limited because of insufficient data.

AIM

To investigate the diagnostic performance of three fibrosis noninvasive tests [FibroTest, vibration-controlled transient elastography (VCTE), and the fibrosis-4 index (FIB-4) and two activity biomarkers (alanine aminotransferase (ALT) and ActiTest].

METHODS

This study enrolled 103 patients for whom liver biopsy, hepatic elastography results, and laboratory markers were available. Diagnostic performance was assessed with receiver operating characteristic (ROC) curves, the Obuchowski measure (OM), and the Bayesian latent class model.

RESULTS

FibroTest and VCTE outperformed FIB-4 in cases of significant fibrosis (≥ F2), with areas under the ROC curve of 0.83 [95% confidence interval (CI): 0.73-0.90], 0.86 (95%CI: 0.77-0.92), and 0.71 (95%CI: 0.60-0.80), respectively. The mean (standard error) OM values were 0.92 (0.01), 0.93 (0.01), and 0.88 (0.02) for FibroTest, VCTE, and FIB-4, respectively; FibroTest and VCTE performed comparably, and both were superior to FIB-4 (P = 0.03 and P = 0.005). The areas under the ROC curve values for activity biomarkers were 0.86 (95%CI: 0.76-0.92) for ActiTest and 0.84 (95%CI: 0.73-0.90) for ALT (P = 0.06). The OM values for ActiTest and ALT were 0.92 (0.02) and 0.90 (0.02), respectively (P = 0.005).

CONCLUSION

FibroTest and VCTE outperformed FIB-4 according to the OM. FibroTest-ActiTest facilitated the evaluation of both fibrosis and activity.

Key Words: Autoimmune hepatitis; FibroTest; FibroSure; ActiTest; Fibrosis-4 index; Fibrosis; Vibration-controlled transient elastography

Core Tip: Our study investigated the efficacy of noninvasive tests in assessing liver fibrosis in autoimmune hepatitis (AIH). Using the Obuchowski measure, we found that FibroTest and vibration-controlled transient elastography outperformed the fibrosis-4 index in the detection of liver fibrosis. FibroTest showed promising results in AIH. ActiTest demonstrated superior performance in the estimation of inflammatory activity compared to alanine aminotransferase and IgG levels. The Bayesian latent class model confirmed the robustness of these noninvasive tests, highlighting their potential to complement liver biopsy in AIH management, particularly during follow-up when repeated biopsies are impractical.

INTRODUCTION

Autoimmune hepatitis (AIH) is an immune-mediated inflammatory liver disease that can affect people of any sex, ethnicity, or age; however, it is most common in middle-aged females[1,2]. The exact cause of AIH is unknown, but it is believed to arise due to a combination of immunological, genetic, and environmental factors[3,4]. Recent epidemiological data estimate that the prevalence of AIH ranges from 17.3-18.3/100000 inhabitants in Europe[5] to 31.2/100000 inhabitants in the United States[6].

AIH is diagnosed based on a combination of histological, immunological, and biochemical abnormalities. American and European guidelines require liver biopsy for the diagnosis and management of AIH[7,8]. The clinical presentation of AIH is very heterogeneous, ranging from no symptoms to acute liver failure[9,10]. Recent data suggest that in adult patients, the prevalence of both AIH and cirrhosis is approximately 28%-33%[11] and that the presence of advanced fibrosis is associated with a worse prognosis[12]. Moreover, individuals can exhibit the characteristics of more than one autoimmune liver disease. This phenomenon is called “overlap syndrome”, and one of the most frequent forms is a combination of AIH and primary biliary cholangitis (PBC). Patients with AIH-PBC tend to exhibit more advanced fibrosis and portal hypertension-related complications than patients with AIH or PBC alone[8,13].

A recent study by Sripongpun et al[14] reported a 5.8% complication rate and three serious adverse events, including bleeding, in 207 biopsies from patients with cirrhotic AIH, even among those classified as Child–Pugh class A. This study recommended exercising caution when considering the use of biopsy in patients with suspected AIH and cirrhosis, even in those with normal coagulation test results. As a result and because evaluating liver fibrosis and inflammation via biopsy during disease follow-up is not feasible, noninvasive assessment is crucial for the management of AIH. Several studies have evaluated the performance of vibration-controlled transient elastography (VCTE) in AIH patients, reporting a significant correlation between test performance and fibrosis histological stage[15-18]. However, VCTE-estimated liver stiffness is affected by inflammatory activity with high levels of aminotransferase, leading to an overestimation of liver fibrosis in patients with AIH[19-21]. Therefore, because of the acute inflammation that occurs during the first months of AIH, VCTE is recommended after at least 6 months of immunosuppressive therapy[7,19].

Recently, several patented and unpatented serum-based biomarker panels, such as the fibrosis-4 index (FIB-4) and FibroTest, have been developed to assess advanced fibrosis and cirrhosis in patients with viral hepatitis and metabolic dysfunction-associated steatotic liver disease (MASLD). However, few studies have evaluated their performance in patients with AIH. In those that have, they have generally yielded conflicting results. A systematic review demonstrated that FIB-4 was inferior to VCTE for the detection of advanced fibrosis and cirrhosis in AIH[17], and a recent meta-analysis showed that FIB-4 demonstrated modest diagnostic accuracy with mediocre sensitivity and specificity with liver biopsy as a reference[22].

Since 2001, ActiTest (for activity grading) and FibroTest (for fibrosis staging) have been extensively validated in patients with chronic hepatitis C virus (HCV) and HBV[23-25], but their performance in patients with AIH remains uncertain. A study of patients with drug-induced liver injury (DILI) showed that after a DILI episode, FibroTest should not be used to predict fibrosis before 8 weeks[26]. To date, only IgG levels and alanine aminotransferase (ALT) are considered to be associated with hepatic histological activity in patients with AIH, and the correlation between their normalization and histological activity remains controversial[27]. Because of the limited and controversial data in the literature, this study evaluated and compared the diagnostic performance of three noninvasive tests (NITs) for fibrosis (FibroTest, VCTE, and FIB-4) and two activity biomarkers (ALT and ActiTest) in AIH patients for whom liver biopsy results were available.

MATERIALS AND METHODS

Study design and participants

This study enrolled 103 adult patients diagnosed with AIH or AIH-PBC overlap syndrome who underwent a liver biopsy between 2017 and 2023 at the Moscow Clinical Scientific Center Department of Hepatology and for whom hepatic elastography and laboratory data were available to calculate serum NITs of interest. The exclusion criteria were as follows: (1) Under 18 years old; (2) Concomitant presence of viral HBV or HCV; (3) Positive HIV status; (4) Significant alcohol consumption, defined according to European and American guidelines as more than 20 g/day for females and 30 g/day for males over a period of at least 6 months[28,29]; and (5) Presence of a hepatic tumor. The study, (approval No. АААА-А18-118021590195-4 at www.rosrid.ru) was approved by the local ethics committee of Moscow Clinical Scientific Center and conducted in accordance with the Declaration of Helsinki. All subjects signed a written informed consent form for the use and processing of personal data.

Histopathological assessments

Liver tissue samples were obtained following puncture biopsy using a 16-gauge needle under ultrasound guidance. Liver tissue samples for standard histological examination were stained with hematoxylin and eosin. They were also stained with Van Gieson’s stain to evaluate connective tissue, Perl’s stain to detect iron deposits, and rubeanic acid to detect copper deposits. Slides were analyzed by one experienced morphologist. The Meta-analysis of Histological Data in Viral Hepatitis score was utilized to assess both the activity and severity of liver fibrosis[30]. Hepatic steatosis was graded separately using the steatosis, activity, and fibrosis score[31].

Clinical assessment

AIH was diagnosed according to the criteria proposed by the International AIH Group in 2008[32]. Diagnosis was based on the evaluation of four main categories: (1) The presence of autoantibodies; (2) Increased IgG levels; (3) The detection of typical or characteristic histology; and (4) The absence of viral hepatitis. A diagnosis of AIH was considered probable with a cumulative score of ≥ 6 points and certain with a score of ≥ 7 points.

NITs

Blood biomarker assays were carried out on a Beckman Coulter AU5800 analyzer (Beckman-Coulter Co., Brea, CA, United States) using Diagam reagents (Liège, Belgium) for alpha-2-macroglobulin and the manufacturer’s reagents for all other markers. FibroTest-ActiTest results were calculated according to the manufacturer’s recommendations[23], and FIB-4 was calculated using a standard formula[33]. Liver stiffness measurements were performed with a VCTE instrument (FibroScan 502 Touch, Echosens, Paris, France) equipped with medium and extra-large probes by experienced and trained nurses or physicians who were blinded to the patients’ histological evaluations. Only VCTE examinations with ≥ 10 valid individual measurements and an interquartile range/median ratio of < 0.30 were considered reliable[34].

Predetermined cutoffs

Significant fibrosis (≥ F2) was defined as FibroTest result ≥ 0.48, VCTE ≥ 8 kPa, and FIB-4 ≥ 1.3. Advanced fibrosis (≥ F3) was defined as FibroTest result ≥ 0.58, VCTE ≥ 9.5 kPa, and FIB-4 ≥ 2.67. Activity grade ≥ A2 was defined as ActiTest result ≥ 0.52 and ALT ≥ 0.50. The NITs cutoffs, including those for FibroTest and ActiTest, were selected based on previous validation studies in patients with chronic liver diseases, particularly viral hepatitis and MASLD, as no specific values have been established for AIH.

Statistical analysis

All statistical analyses were performed with R version 4.0.3 or NCSS 2023. Qualitative data were presented as absolute (n) and relative (%) frequencies, and quantitative data were presented as medians and interquartile ranges. P values of < 0.05 were considered statistically significant. The diagnostic performances of the NITs for significant fibrosis (≥ F2), advanced fibrosis (≥ F3), cirrhosis, and advanced activity (≥ A2) were assessed with the empirical binary area under the receiver operating characteristic (AUROC) curve.

Sensitivity, specificity, prevalence, positive predictive value (PPV), and negative predictive value (NPV) were estimated with the gold standard model, which assumes that the liver biopsy is perfect, and the Bayesian latent class model (LCM), assuming that all tests evaluated are imperfect. In this study, the Bayesian LCM was chosen as a complementary approach to traditional biopsy-based analysis because it accounts for the potential imperfections of all diagnostic tests, including the gold standard. This model assumes that all tests are subject to measurement error and estimates the true disease state as a latent variable. The key assumptions of the LCM include conditional independence between tests given the true disease status, constant test properties across populations, and identifiability of the model parameters. This approach provides more robust and unbiased estimates of diagnostic accuracy, especially when the gold standard is known to have limitations. For LCM analysis, a web-based application (http://mice.tropmedres.ac) constructed using R and WinBUGS was used[35].

We also assessed the performances of the NITs with the Obuchowski measure (OM), which with respect to the area under the curve includes all pairwise stage and grade comparisons. The OM represents the probability that under a specific weighting scheme the noninvasive index accurately ranks two randomly selected patient samples from distinct stages, with a penalty for misclassifying patients. More specifically, the OM “weighted AUROC” is a weighted average of the different N (N - 1)/2 curves that correspond to all pairwise comparisons between 2 × 2 of the N categories. This metric averages all pairwise comparisons between N stages, weighted by a penalty function reflecting the difference in Meta-analysis of Histological Data in Viral Hepatitis (METAVIR) fibrosis classes. Penalties increase with staging distance: 0.25 for one stage (e.g., F0 vs F1); 0.50 for two stages (e.g., F0 vs F2); 0.75 for three stages (e.g., F0 vs F3); and 1.00 for four stages (e.g., F0 vs F4). This approach ensures greater penalties for larger discrepancies, aligning with their clinical significance[36].

RESULTS

Patient characteristics

Between 2017 and 2023, a total of 161 patients (130 females and 31 males) were diagnosed with AIH. Of these 161 patients, 58 were excluded: 52 with at least one unavailable test and 6 with at least one test that could not be evaluated (Figure 1). Therefore, 103 patients for whom biopsies were available and who underwent NITs within 3 days of their biopsy were eligible and included in the analysis. The median age of the cohort was 53 years, and 86 patients (83%) were female. According to the liver biopsy results, significant fibrosis (≥ F2) was present in 63% of patients, advanced fibrosis (≥ F3) was found in 43% of patients, and cirrhosis (F4) was diagnosed in 19% of patients. Advanced activity (≥ A2) was detected in 61% of patients (Table 1).

Figure 1 Flowchart of participant inclusion, data collection, and analysis methods. AIH: Autoimmune hepatitis; PBC: Primary biliary cholangitis; VCTE: Vibration-controlled transient elastography; HCV: Hepatitis C virus; FIB4: Fibrosis-4 index; ALT: Alanine aminotransferase; NITs: Noninvasive tests; AUROC: Areas under the receiver operating characteristic curve; LCM: Latent class model; METAVIR: Meta-analysis of Histological Data in Viral Hepatitis.

Table 1 Characteristics of participants.

Characteristics	Included patients (n = 103)
Demographic
Age (years), median (IQR)	53 (38-61)
Female	86 (83)
BMI, median (IQR)	26 (21-28)
Baseline histological features (METAVIR)
Biopsy specimen length (mm), median (IQR)	12 (11-15)
F0	10 (10)
F1	28 (27)
F2	21 (20)
F3	24 (23)
F4	20 (19)
Fibrosis ≥ F2	65 (63)
Fibrosis ≥ F3	44 (43)
Cirrhosis F4	20 (19)
A0	10 (10)
A1	30 (29)
A2	42 (41)
A3	21 (20)
Activity ≥ A2	63 (61)
Steatosis > 0	12 (12)
Biopsy diagnosis
AIH	83 (81)
AIH and MASLD	1 (1)
AIH and PBC	19 (18)
NITs for significant fibrosis (≥ F2)
FibroTest > 0.48	55 (53)
FIB-4 ≥ 1.3	66 (64)
VCTE ≥ 8 kPa	61 (59)
NITs for advanced activity (≥ A2)
ActiTest > 0.52	48 (47)
ALT ≥ 50	63 (61)
Other serum components, median (IQR)
ALT (IU/L)	67 (37-160)
AST (IU/L)	52 (34-117)
GGT (IU/L)	73 (31-170)
Total bilirubin, μmol/L	16.7 (11-25)
Apolipoprotein-A1 (g/L)	1.57 (1.25-1.80)
Haptoglobin (g/L)	0.58 (0.56-1.30)
Alpha-2 macroglobulin (g/L)	2.34 (1.93-2.88)
Platelets (g/L)	201 (156-266)

Data presented as n (%). IQR: Interquartile range; BMI: Body mass index; METAVIR: Meta-analysis of Histological Data in Viral Hepatitis; AIH: Autoimmune hepatitis; MASLD: Metabolic dysfunction-associated steatotic liver disease; PBC: Primary biliary cholangitis; NITs: Noninvasive tests; ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; GGT: Gamma-glutamyl transpeptidase; VCTE: Vibration-controlled transient elastography; FIB4: Fibrosis-4 index.

Comparison of the diagnostic performances of different NITs for fibrosis

In this study, we investigated the ability of three noninvasive serum biomarkers, FibroTest, VCTE, and FIB-4, to predict liver fibrosis in patients with AIH. The AUROCs for significant fibrosis (≥ F2) were as follows: 0.83 [95% confidence interval (CI): 0.73-0.90] for FibroTest; 0.86 (95%CI: 0.77-0.92) for VCTE; and 0.71 (95%CI: 0.60-0.80) for FIB-4. A significant difference between FibroTest and FIB-4 (P = 0.01) as well as between VCTE and FIB-4 (P = 0.002) was observed (Figure 2A, Table 2).

Figure 2 Area under the receiver operating characteristic curves for fibrosis and activity assessment using noninvasive tests. A: Area under the receiver operating characteristic curves (AUROCs) of FibroTest, vibration-controlled transient elastography (VCTE), and fibrosis-4 index (FIB-4) for the detection of significant fibrosis (F2, F3, and F4); B: AUROCs of FibroTest, VCTE, and FIB-4 for the detection of advanced fibrosis (F3 and F4); C: AUROCs of FibroTest, VCTE, and FIB-4 for the detection of liver cirrhosis (F4); D: AUROCs of ActiTest and alanine aminotransferase in the detection of advanced activity (A2 and A3).

Table 2 Test performance according to statistical methods by features.

Features and test	Obuchowski weighted AUROC mean (standard error)	Standard binary AUROC mean (95%CI)
Fibrosis	5 stages, 10 comparisons	F2, F3, F4 vs F0, F1	F3, F4 vs F0, F1, F2	F4 vs F0, F1, F2, F3
FibroTest	0.919 (0.014)	0.83 (0.73-0.90)	0.83 (0.73-0.90)	0.80 (0.68-0.88)
VCTE	0.933 (0.014)	0.86 (0.77-0.92)	0.87 (0.78-0.93)	0.87 (0.78-0.94)
FIB-4	0.882 (0.019)	0.71 (0.60-0.80)	0.75 (0.63-0.83)	0.80 (0.67-0.89)
Activity	4 stages, 8 comparisons	A2, A3 vs A0, A1	-	-
ActiTest	0.921 (0.016)	0.86 (0.76-0.92)	-	-
ALT	0.905 (0.019)	0.83 (0.73-0.90)	-	-

AUROC: Areas under the receiver operating characteristic curve; CI: Confidence interval; VCTE: Vibration-controlled transient elastography; FIB-4: Fibrosis-4 index; ALT: Alanine aminotransferase.

For predicting advanced fibrosis (≥ F3), the AUROCs were 0.83 (95%CI: 0.73-0.90), 0.87 (95%CI: 0.78-0.93), and 0.75 (95%CI: 0.63-0.83) for FibroTest, VCTE, and FIB-4, respectively (Figure 2B, Table 2). For cirrhosis (F4), the AUROCs were 0.80 (95%CI: 0.68-0.88), 0.87 (95%CI: 0.78-0.94), and 0.80 (95%CI: 0.67-0.89) for FibroTest, VCTE, and FIB-4, respectively (Figure 2C, Table 2). A statistically significant difference was observed only for significant fibrosis in the case of VCTE vs FIB-4 (P = 0.01). All P values for NIT comparisons are reported in Supplementary Table 1. The mean (standard error) OM values were 0.92 (0.01) for FibroTest, 0.93 (0.01) for VCTE, and 0.88 (0.02) for FIB-4 (Table 2). No significant difference was observed between FibroTest and VCTE (P = 0.38), but significant differences were revealed between FibroTest and FIB-4 (P = 0.03) as well as between VCTE and FIB-4 (P = 0.005).

Comparison of the diagnostic performances of different NITs for advanced activity

We investigated the ability of two noninvasive serum biomarkers, ActiTest and ALT, to predict advanced activity (≥ A2) in patients with AIH. The AUROCs calculated were 0.86 (95%CI: 0.76-0.92) for ActiTest and 0.84 (95%CI: 0.73-0.90) for ALT (P = 0.06) (Figure 2D, Table 2). We also determined the AUROC of IgG to assess its ability to predict advanced activity, but its performance was significantly lower than that of ActiTest and ALT (Supplementary Figure 1). The mean (standard error) OM values for the ActiTest and ALT were 0.92 (0.02) and 0.90 (0.02), respectively (P = 0.005) (Table 2).

Performance of NITs for significant and advanced fibrosis according to the gold standard model and Bayesian LCM

Using liver biopsy as the reference for the diagnosis of significant fibrosis (≥ F2), the sensitivities were 73.8% (95%CI: 61.2-83.6), 69.2% (95%CI: 56.4-79.8), and 81.5% (95%CI: 69.6-89.7) for FibroTest, FIB-4, and VCTE, respectively. Their respective specificities were 81.6% (95%CI: 65.1-91.7), 44.7% (95%CI: 29.0-61.5), and 78.9% (95%CI: 62.2-89.9) (Table 3). Statistically significant differences were observed only for the specificity of FibroTest vs FIB-4 and VCTE vs FIB-4 (P = 0.002 and P = 0.003, respectively) (Supplementary Table 2). The PPV and NPV were 87.3 (95%CI: 74.9-94.3) and 64.6 (95%CI: 49.4-77.4), respectively, for FibroTest, 68.2 (95%CI: 55.4-78.8) and 45.9 (95%CI: 29.8-62.9), respectively, for FIB-4, and 86.9 (95%CI: 75.2-93.8) and 71.4 (95%CI: 55.2-83.8), respectively, for VCTE. Statistically significant differences in PPV were observed for FibroTest vs FIB-4 (P = 0.04), and in both PPV and NPV for VCTE vs FIB-4 (P = 0.04 and P = 0.03, respectively) (Table 3 and Supplementary Table 2).

Table 3 Diagnostic performance of biopsy, FibroTest, fibrosis-4 index, and vibration-controlled transient elastography for significant fibrosis (F2, F3, and F4) and advanced fibrosis (F3 and F4) compared with that of the gold standard model and Bayesian latent class model.

Parameters and cutoffs	Biopsy assumed as perfect reference	Bayesian LCM	Gold standard model vs LCM
Number of patients	103	103	Difference (Z-test P-value)
Prevalence significant fibrosis	63.1 (53.0-72.2)	66.2 (56.0-77.0)	3.1% increase (0.67)
Biopsy stage METAVIR ≥ F2
Sensitivity	100	95.4 (83.1-100)	4.6% decrease (NA)
Specificity	100	100 (100-100)	NA (NA)
Positive predictive value	100	100 (100-100)	NA (NA)
Negative predictive value	100	91.9 (67.3-99.9)	8.1% decrease (NA)
FibroTest with cutoff of > 0.48
Sensitivity	73.8 (61.2-83.6)	73.1 (61.5-82.6)	0.7% decrease (0.93)
Specificity	81.6 (65.1-91.7)	86.1 (70.6-98.4)	4.5% increase (0.69)
Positive predictive value	87.3 (74.9-94.3)	91.1 (79.9-99.1)	3.8% increase (0.65)
Negative predictive value	64.6 (49.4-77.4)	62.0 (45.7-75.4)	2.6% decrease (0.82)
FIB-4 with cutoff of ≥ 1.3
Sensitivity	69.2 (56.4-79.8)	70.0 (58.6-80.2)	0.8% increase (0.93)
Specificity	44.7 (29.0-61.5)	49.8 (40.8-66.1)	5.1% increase (0.58)
Positive predictive value	68.2 (55.4-78.8)	73.5 (61.5-85.0)	5.3% increase (0.55)
Negative predictive value	45.9 (29.8-62.9)	46.1 (31.2-61.9)	0.2% increase (0.99)
VCTE with LSM cutoff of ≥ 8 kPa
Sensitivity	81.5 (69.6-89.7)	80.7 (69.9-89.2)	0.8% decrease (0.92)
Specificity	78.9 (62.2-89.9)	83.6 (67.2-97.2)	4.7% increase (0.69)
Positive predictive value	86.9 (75.2-93.8)	90.6 (79.5-98.7)	3.7% increase (0.65)
Negative predictive value	71.4 (55.2-83.8)	68.8 (50.6-82.1)	2.6% decrease (0.83)
Prevalence advanced fibrosis	42.7 (33.1-52.8)	50.2 (37.1-65.2)	7.5% increase (0.37)
Biopsy stage METAVIR ≥ F3
Sensitivity	100	85.2 (64.6-99.8)	14.8% decrease (NA)
Specificity	100	100 (100-100)	NA (NA)
Positive predictive value	100	100 (100-100)	NA (NA)
Negative predictive value	100	87.0 (63.7-99.9)	13% decrease (NA)
FibroTest with cutoff of > 0.58
Sensitivity	70.5 (54.6-82.8)	66.1 (51.2-80.1)	4.4% decrease (0.69)
Specificity	83.1 (70.6-91.1)	86.4 (74.2-96.3)	3.3% increase (0.71)
Positive predictive value	75.6 (59.4-87.1)	83.3 (66.6-95.9)	7.7% increase (0.52)
Negative predictive value	79.0 (66.5-87.9)	71.9 (53.2-85.2)	7.1% decrease (0.54)
FIB-4 with cutoff of ≥ 2.67
Sensitivity	38.6 (24.7-54.5)	38.2 (25.0-52.3)	0.4% decrease (0.97)
Specificity	88.1 (76.5-94.7)	91.7 (79.9-99.5)	3.6% increase (0.67)
Positive predictive value	70.8 (48.8-86.6)	82.4 (56.5-99.2)	11.6% increase (0.50)
Negative predictive value	65.8 (54.2-75.9)	59.8 (44.2-72.6)	6% decrease (0.55)
VCTE with LSM cutoff of ≥ 9.5
Sensitivity	84.1 (69.3-92.8)	84.0 (72.2-92.9)	0.1% decrease (0.94)
Specificity	72.9 (59.5-83.3)	81.7 (65.2-98.8)	8.8% increase (0.42)
Positive predictive value	69.8 (55.5-81.3)	82.2 (62.4-99.0)	12.4% increase (0.32)
Negative predictive value	86.0 (72.6-93.7)	83.5 (69.4-92.9)	2.5% decrease (0.80)

LCM: Latent class model; METAVIR: Meta-analysis of Histological Data in Viral Hepatitis; FIB-4: Fibrosis-4 index; VCTE: Vibration-controlled transient elastography; NA: Not applicable; NA: Not available; LSM: Liver stiffness median.

Similar results were obtained for the diagnosis of advanced fibrosis (≥ F3), with sensitivities and specificities of 70.5% (95%CI: 54.6-82.8) and 83.1% (95%CI: 70.6-91.1), respectively, for FibroTest, 38.6% (95%CI: 24.7-54.5) and 88.1% (95%CI: 76.5-94.7), respectively, for FIB-4, and 84.1% (95%CI: 69.3-92.8) and 72.9% (95%CI: 59.5-83.3), respectively, for VCTE. The PPV and NPV were 75.6 (95%CI: 59.4-87.1) and 79.0 (95%CI: 66.5-87.9), respectively, for FibroTest, 70.8 (95%CI: 48.8-86.6) and 65.8 (95%CI: 54.2-75.9), respectively, for FIB-4, and 69.8 (95%CI: 55.5-81.3) and 86.0 (95%CI: 72.6-93.7), respectively, for VCTE. Statistically significant differences in sensitivity were only observed for FibroTest vs FIB-4 and VCTE vs FIB-4 (P = 0.003 and P ≤ 0.001, respectively) (Table 3 and Supplementary Table 2).

We used the LCM to evaluate the prevalence of significant and advanced fibrosis and compare the performance of the different NITs. In some cases, this mathematical model revealed slight improvements in NIT performance (from 0.1% to 12.0%); however, the comparison with the classical model using biopsy as the gold standard showed no statistically significant improvement (Table 3). Therefore, the classical and mathematical models appear to assess the prevalence of fibrosis and the performance of the different NITs in the same manner. All test performance comparisons (P values) using either liver biopsy as the reference or the LCM are shown in Supplementary Table 2.

Performance of NITs for advanced activity according to the gold standard model and Bayesian LCM

Using liver biopsy as the reference for the diagnosis of advanced activity (≥ A2), the sensitivities and specificities were 73.0% (95%CI: 60.1-83.1) and 95.0% (95%CI: 81.8-99.1), respectively, for ActiTest and 81.0% (95%CI: 68.7-89.4) and 70.0% (95%CI: 53.3-82.9), respectively, for ALT, with PPVs and NPVs of 95.8 (95%CI: 84.6-99.3) and 69.1 (95%CI: 55.0-80.5), respectively, for ActiTest and 81.0 (95%CI: 68.7-89.4) and 70.0 (95%CI: 53.3-82.9), respectively, for ALT (Table 4). According to the gold standard model, the ActiTest showed a higher specificity than ALT (P < 0.001) for the detection of advanced activity. We used the LCM to evaluate advanced activity and compare the two NITs. This mathematical model showed a slight, nonsignificant improvement (approximately 3%-4%) in specificity and PPV for the two NITs and a major significant increase (approximately 18%-29%) in sensitivity and NPV for both blood tests (Table 4). All test performance comparisons (P values) using the LCM are shown in Supplementary Table 1.

Table 4 Comparison of the diagnostic performance of biopsy, ActiTest, and alanine aminotransferase for advanced activity (A2 and A3) with the gold standard model and Bayesian latent class model.

Parameters and cutoffs	Biopsy assumed as perfect reference	Bayesian LCM	Gold standard model vs LCM
Number of patients	103	103	Difference (Z-test P-value)
Prevalence advanced activity	61.2 (51.0-70.5)	47.8 (37.7-58.3)	13.4% decrease (0.07)
Biopsy stage METAVIR ≥ A2
Sensitivity	100	95.8 (87.5-99.6)	4.2% decrease (NA)
Specificity	100	70.9 (57.4-82.3)	29.1% decrease (NA)
Positive predictive value	100	75.1 (62.7-85.6)	24.9% decrease (NA)
Negative predictive value	100	94.8 (84.5-99.5)	5.2% decrease (NA)
ActiTest with cutoff of > 0.52
Sensitivity	73.0 (60.1-83.1)	97.3 (85.8-100)	24.3% increase (0.006)
Specificity	95.0 (81.8-99.1)	99.4 (93.4-100)	4.4% increase (0.59)
Positive predictive value	95.8 (84.6-99.3)	99.3 (92.6-100)	3.5% increase (0.60)
Negative predictive value	69.1 (55.0-80.5)	97.7 (86.6-100)	28.6% increase (0.002)
ALT with cutoff of ≥ 50
Sensitivity	81.0 (68.7-89.4)	99.5 (95.0-100)	18.5% increase (0.006)
Specificity	70.0 (53.3-82.9)	74.3 (61.6-85.7)	4.2% increase (0.69)
Positive predictive value	81.0 (68.7-89.4)	78.0 (65.8-88.4)	3.0% decrease (0.73)
Negative predictive value	70.0 (53.3-82.9)	99.4 (94.0-100)	29.4% increase (0.006)

LCM: Latent class model; METAVIR: Meta-analysis of Histological Data in Viral Hepatitis; ALT: Alanine aminotransferase; NA: Not applicable.

DISCUSSION

In recent years, studies of the ability of NITs to assess fibrosis in liver disease have surged, particularly in the context of chronic viral hepatitis and MASLD. However, only a handful of studies have investigated the performance of these NITs in patients with AIH[37]. To date, no specific markers have been validated or approved, rendering liver biopsy crucial for the diagnosis of AIH and the evaluation of both inflammatory activity and fibrosis[7,8]. Indeed, as recently suggested by Sripongpun et al[14], determining whether to conduct a liver biopsy, especially in patients with cirrhosis with suspected AIH, remains a challenge. Specifically, their study showed that adverse events occurred in 10.2% and 4.1% of patients with and without cirrhosis, respectively. Of the 12 adverse events observed, three serious bleeding events occurred in patients with compensated cirrhosis with normal platelet counts and international normalized ratio values. Accordingly, the decision to perform liver biopsies in these patients should be considered with caution. For all these reasons, the validation of NITs in the context of AIH becomes of crucial importance in clinical practice.

In the present study, which used the OM, FibroTest, and VCTE appeared to outperform FIB-4 for the detection of liver fibrosis. Since 2001, FibroTest has been extensively validated in patients with viral HCV or HBV[23-25], metabolic dysfunction-associated steatohepatitis[38], and alcoholic hepatitis[39], but its performance in the setting of AIH has been evaluated in few patients and therefore remains uncertain. Anastasiou et al[40] compared the performance of transient elastography and FibroTest-ActiTest in patients with viral (HCV and HBV) and non-viral chronic liver disease (including 13 patients with AIH) to that of liver biopsy. Their study showed that FibroTest-ActiTest was a reliable method for predicting both liver fibrosis and necro-inflammatory activity in patients with viral and non-viral chronic liver diseases.

In another recent study, Poo et al[41] tested pirfenidone, an oral antifibrotic drug, in patients with advanced liver fibrosis (including 21 patients with AIH) and assessed its antifibrotic effects using FibroTest and VCTE. In their study, both NITs detected significant reductions in fibrosis in patients who received pirfenidone and confirmed the utility of noninvasive markers during treatment follow-up, which is crucial for patients with AIH after immunosuppressive therapy. Finally, a study of patients with DILI showed that after a DILI episode, FibroTest should not be used to predict fibrosis before 8 weeks, and for liver elasticity (as estimated by VCTE) a high risk of false-positive results was still observed between 8 weeks and 12 weeks[26].

Despite the paucity of studies evaluating FibroTest in patients with AIH, its performance in our AIH cohort herein (AUROC: 0.83) was comparable to that established in previous studies. Indeed, meta-analysis has reported AUROCs of 0.80 in patients with viral hepatitis[25]; in MASLD populations, AUROCs of 0.86 for advanced fibrosis have been reported[38]. These findings suggest that FibroTest maintains similar diagnostic accuracy across these liver diseases and AIH-overlapping conditions.

The data in the literature concerning the performance of FIB-4 and VCTE in AIH are more consistent than those for FibroTest, although in some cases they revealed conflicting results. Our results regarding FIB-4 align with those from other studies. For example, a systematic review (including 6 studies with 861 patients) demonstrated that FIB-4 performed poorly for the detection of advanced fibrosis in AIH compared to VCTE[17]. Additionally, a recent meta-analysis (including 14 studies with 10105 patients) showed that FIB-4 demonstrated modest diagnostic accuracy, especially for the detection of significant and advanced fibrosis, and better performance for the detection of cirrhosis[22]. Finally, a very recent study provided novel data about NIT performance in the prediction of cirrhosis in patients with AIH-PBC overlap syndrome, showing that FIB-4 performed well in this subgroup[42]. These results are consistent with those of our study, which showed a better AUROC of FIB-4 in the context of cirrhosis detection.

The performance of VCTE in AIH has been evaluated in several studies; significant correlations with fibrosis histological stage have been observed[15-18]. However, several studies in diverse liver diseases have shown that VCTE is affected by inflammatory activity and high levels of aminotransferases, which can lead to an overestimation of liver fibrosis[19-21]. Additionally, a recent multicenter cohort study suggested that liver stiffness measurements may not accurately predict cirrhosis in AIH in the long term because of changes in necro-inflammation, liver remodeling, or fibrosis resorption following immunosuppressive therapy, potentially reducing its diagnostic reliability[43]. Taken together, it is recommended that VCTE be performed after at least 6 months of immunosuppressive therapy and consequent reductions in liver inflammation are achieved[7,19].

Our study also found that ActiTest, typically calculated in combination with FibroTest in patients with HCV and HBV, performed strongly in the estimation of advanced inflammatory activity (≥ A2) in patients with AIH. In fact, according to the OM, it outperformed high ALT. Our study also found that the ability of IgG to predict advanced activity was significantly lower than that of ActiTest and ALT. Currently, IgG levels and ALT are considered the only biochemical markers associated with hepatic histological activity in patients with AIH[27]. However, a recent multicentric study demonstrated that ALT and IgG levels were suboptimal for assessing histologic activity in patients with AIH; a high proportion of advanced inflammation was detected in AIH patients with or without fibrosis/cirrhosis and with normal levels of ALT and IgG[44]. A second study confirmed the poor capacity of both ALT and IgG to detect histological activity remission in AIH patients with cirrhosis[45].

The performance of ActiTest in our cohort of AIH patients (AUROC: 0.86) appears comparable to that observed in previous studies in patients with viral hepatitis, with AUROCs of 0.75-0.86[23] and 0.74-0.80[24] in chronic HCV and HBV, respectively. The higher performance of ActiTest in our AIH cohort may reflect the predominantly inflammatory nature of this disease compared to viral hepatitis, potentially rendering ActiTest more sensitive in this context. The clinical presentation of AIH is very heterogeneous, and AIH-PBC is one of the most frequent overlapping conditions[8,13]. In our study, only 15% of the cohort presented with AIH-PBC overlap syndrome; consequently, this small sample size limited our ability to perform formal subgroup analyses. Nevertheless, the high performance of FibroTest and VCTE in our mixed cohort aligns with other studies of viral hepatitis and metabolic liver diseases, suggesting that these tests could be valuable across a range of chronic liver conditions[25,46].

In our study, we decided to test the Bayesian LCM, a mathematical model estimating the diagnostic accuracy of NITs in AIH, in a scenario lacking a gold standard and assuming that all tests evaluated are imperfect[35]. To date, few studies have used the LCM to evaluate NIT performance for fibrosis assessment. Our results showed that the three fibrosis NITs performed comparably when the two models were compared, indicating only minor alterations that were not statistically significant. However, in the case of advanced activity, the LCM showed significant increases (approximately 18%-29%) in sensitivity and NPV for both blood tests (ActiTest and ALT), indicating possible limitations of liver biopsy in the setting of necro-inflammatory activity. Some studies have reported interobserver and intraobserver variability of liver biopsy, especially in the detection of inflammatory activity. Specifically, these studies have reported lower weighted kappa values for the assessment of inflammatory activity compared to other parameters such as fibrosis, steatosis, and cell injury[47-49]. We agree that the LCM cannot replace biopsies in the estimation of NIT performance for fibrosis and activity; instead, this mathematical model can be a useful tool for estimating disease prevalence or diagnostic accuracy in the absence of a gold standard or for a pilot assessment of new NITs.

We acknowledge some limitations of the present study. First, although our sample size was higher than that reported in other studies of AIH, it was still limited. Indeed, the limited sample size (n = 103) of our study may reduce the statistical power of our analysis, particularly for subgroup comparisons and the detection of smaller differences between NITs. This limitation is particularly relevant for patients with cirrhosis (18%) where the smaller number of cases may impact the reliability of our findings in this specific context. Moreover, in our study, the median biopsy length was 12 mm, which despite not being fragmented is shorter than the recommended 15 mm in patients with AIH[50]. In addition, we did not consider potential confounders that could interfere with the NIT results; these include the impact of concurrent autoimmune conditions, the use of immunosuppressants, and the influence of liver inflammation on VCTE measurements. These factors can potentially reduce the diagnostic reliability of NITs.

Furthermore, in our study, the biopsy slides were read by only one morphologist. The reliance on a single morphologist, while experienced, introduces potential observer biases. Studies have shown that interobserver agreement for liver biopsy assessment can vary significantly, with kappa values ranging from 0.4 to 0.7 for inflammatory activity grading. For liver fibrosis, interobserver agreement is particularly variable for the assessment of intermediate stages of fibrosis. This limitation could affect the accuracy of our gold standard[51]. Lastly, a limitation that concerns all studies of patients with AIH is the prospective validation of predefined cutoffs. Thus, it is difficult for clinicians to apply these tests without specific cutoff recommendations stipulated by guidelines.

To our knowledge, this study is the first that has used the OM, which includes all pairwise stage and grade comparisons. This study represents the first evaluation of NIT performance in the context of AIH, aiming to fill a knowledge gap in this field. Nevertheless, further studies, especially in patients undergoing repeated biopsy, will be carried out to establish the ability of these NITs to track disease progression, particularly concerning the remission of inflammatory activity during follow-up, and to validate their links to treatment response and outcomes.

CONCLUSION

The present study showed that FibroTest and VCTE performed well in the detection of significant or advanced fibrosis in patients with AIH; their performance was better than that of FIB-4 according to the OM. The OM also showed that the ActiTest performed better than high ALT for the detection of inflammatory activity. The use of FibroTest-ActiTest allows the simultaneous evaluation of inflammatory activity and liver fibrosis in patients with AIH. This evaluation is paramount, especially during follow-up, when the use of repeat biopsies is not feasible.