This study aimed at identifying patients with significant fibrosis using noninvasive fibrosis markers, such as the direct and/or indirect models. This research showed that most of the noninvasive markers could distinguish significant fibrosis from mild fibrosis with different accuracies and significances. However, SHFI-1 as a new noninvasive fibrosis model had the highest AUC (95% CI). Since only serum MMP-2 level within the tested direct markers was associated with the hepatic fibrosis stage, this marker was included in the new noninvasive model. Age, PT, direct bilirubin, albumin, and PLT, which are indirect markers and are associated with the function and stage of hepatic disease, are also widely used within noninvasive models. In the current study, these parameters, which have the highest correlation with the degree of fibrosis were also included in the SHFI-1. According to the cut off value of 6.82 for SHFI-1, sensitivity, specificity, PPV, and NPV were respectively, 73.3%, 95.6%, 91.7%, and 84.3%. Logistic regression analysis showed that SHFI-1 may be used as a predictor of significant liver fibrosis. The OR of SHFI-1 was 1.231 and statistically significant (P = 0.006). It was assumed that SHFI-1 with this data is satisfactory to distinguish significant fibrosis. Similarly, Wu et al. indicated that models containing direct serum markers (Fibrometer, Shanghai Liver Fibrosis Group’s index, Hepascore) were more accurate predictors of significant fibrosis than models containing only indirect serum markers (APRI, FIB-4, Forn’s index) (33). However, noninvasive models are easy to calculate without additional costs, and contain validated and routinely available parameters in laboratories. Thus, a second model (SHFI-2) was formed by excluding MMP-2. The SHFI-2 showed similar performance to SHFI-1 and had better specificity (97.8%) and positive predictive value (95.5%) than all models. Thus, this final model, which is easy to practice with low cost, can be used to distinguish specific fibrosis with high specificity.
The first 5 models with the best AUC values except SHFI-1 or SHFI-2 were respectively, King score, Fib-4, ELF, API, and Fibro-Q. Most of the studies associated with these models have been firstly performed on hepatitis C patients. However, the performance of King score with 13.08 cut off value in the CHC patients was not found to be superior than the King score with 11.00 cut off value in patients with CHB. While the AUC, sensitivity, specificity, PPV, and NPV were respectively 0.783, 61%, 84%, 75%, and 72% in chronic hepatitis (CHC) patients, the AUC, sensitivity, specificity, PPV, and NPV were respectively 0.770, 60%, 83%, 66%, and 76% in CHB patients (34). Karacaer et al. found that the King score with cut off value of 5.76, had AUC of 0.807, sensitivity of 73%, specificity of 73%, PPV of 92.4%, and NPV of 36.3% [35]. This research found that optimum cut off value was 7.44, and the AUC, sensitivity, specificity, PPV, and NPV were 0.885, 80.6%, 87.2%, 80.6%, and 87.2%, respectively.
In studies performed on patients with CHB, AUC values of Fib-4 were 0.768, 0.687, 0.701, 0.720, and 0.750 for 0.73 (35), 1.09 (36), 1.02 (37), 1.7 (38), and 1.45 (39) cut off values, respectively. It was shown that AUC was 0.885 for cut off value of 1.09 and more effective for predicting significant fibrosis in patients with CHB. In the present study, the sensitivity, specificity, PPV, and NPV were 87.1%, 76.6%, 71.1%, and 90.0%, respectively.
The ELF is a biochemical test panel made up of serum markers reflecting ECM metabolism. This test panel consists of 3 parameters: HA, PIIINP, and TIMP-1. Serum levels of these parameters are used to calculate ELF score, which is used as a predictor of hepatic fibrosis. In studies, the ELF test used to identify ≥ 2 METAVİR score (equivalent to ≥ 3 ISHAK score) in patients with HBV, the AUC values were 0.901 (40), 0.820 (41) and 0.800 (42) for 8.5, 10.41, 8.75 cut off values, respectively. It was found that the 9.0 cut off value for ELF score had AUC of 0.872. The suggested cut off values might change among studies, and they may be influenced by etiologies, sample size, or ethnicity. According to the manufacturer, the AUC was 0.786 with cut-off value of 7.7. For this reason, it is reasonable to recommend that each laboratory might determine its own ELF score and cut-off values as part of good clinical practice.
Some researchers found that AUC of API was 0.680 and 0.580 (43, 44). The other ones also showed that AUC of API was 0.767 and 0.529 for 3.5 and 5.5 cut off values, respectively (34, 37). In this study, the AUC of API (0.861 for 5.0 cut off value) was higher than in other studies. The API was first implemented in patients with CHC and one of its 2 variables was age. This may explain why AUR for API was lower than other reports.
In order to Fibro-Q used for discriminating significant fibrosis, it was stated that different cut off values have different AUCs (for example 0.640 for 3.73 (39) and 0.783 for 1.6 (45). Zeng et al. showed that Fibro-Q had poor predictive value and was 0.569 of AUC (44). The current results showed that if Fibro-Q was above 2.26 for predicting significant fibrosis, AUC was 0.856. The sensitivity of Fibro-Q among all fibrosis models was the highest (83.9%).
The AUC values of Forn’s index, APRI, GPRI, S index, RPR, FCI, and AAR were lower than AUC of the first 7 models. Additionally, the AUC values of S index, RPR, FCI, and AAR had statically significant differences with AUC of SHFI-1. However, there were significant differences between mild and significant fibrosis for all models, except AAR. The AAR was more or less effective for differential diagnosis.
Liver biopsy was chosen as the reference method, yet it is known that it has some limitations. The tissues from different sites of the liver can be obstacles in the evaluation step (5). For example, a single biopsy specimen in a disease that does not affect each region of the liver equally cannot accurately reflect the characteristics of the disease since the biopsy sample represents approximately 1/50.000 of the adult liver (46). Liver biopsy is not ideal for frequent evaluations of the fibrosis stage under or after treatment due to its invasiveness (4) and this topic constitutes another limitation.
Transient Elastography (TE) is a novel and noninvasive technique for measuring liver stiffness (47). The TE includes a short procedure time, and the ability to perform the test at the bedside or in an outpatient clinic (48). The TE provides good performance for detecting significant fibrosis (40). However, applicability (80%) of TE is lower than serum biomarkers in case of obesity, ascites or operator experience. The TE also requires a specialized device with experienced operator (2). Due to the lack of TE device in the current hospital, this research was not able to show the TE results in the cohort and this was a major drawback of the study together with limited sample size. Lack of comparison of TE and SHFI-1 or SHFI-2 performances with each other is a limitation of this work.
Controversial results according to the literature might be the result of using different histopathological scoring systems (the METAVIR vs. the Ishak system), differences in patient populations or the prevalence of significant fibrosis.
Although HBV genotypes of the patients were not evaluated in this study, the HBV genotype D was the most common HBV genotype in Turkey. Hence, the researchers think that the results of the patients with HBV genotype D could be extended to other HBV genotypes (49). Additionally, since limited number of cases were tested in this study, the validation of SHFI-1 or SHFI-2 is required in large populations.
The selection of a test depends on individual patient factors, as well as the cost, accuracy, reliability, and availability of the test. The major advantage of the SHFI-2 is being an easily calculated model with low cost due to parameters being routinely analyzed in every laboratory. Especially for chronic HBV infection, treatment decision is based on the presence of necroinflammation rather than fibrosis, therefore, in such cases liver biopsy is still irreplaceable. The challenge now is to decide on how to apply validated noninvasive tests in HBV management. It is likely that a convenient combination approach (i.e., blood and imaging test as screening) will give the highest diagnostic accuracy, obviate the need for the greatest number of liver biopsies, and inform the clinician and patient regarding prognosis and the need for therapy.
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