Development of Serum Marker Models to Increase Diagnostic Accuracy of Advanced Fibrosis in Nonalcoholic Fatty Liver Disease: The New LINKI Algorithm Compared with Established Algorithms

Abstract

Background and AimDetection of advanced fibrosis (F3-F4) in nonalcoholic fatty liver disease (NAFLD) is important for ascertaining prognosis. Serum markers have been proposed as alternatives to biopsy. We attempted to develop a novel algorithm for detection of advanced fibrosis based on a more efficient combination of serological markers and to compare this with established algorithms.MethodsWe included 158 patients with biopsy-proven NAFLD. Of these, 38 had advanced fibrosis. The following fibrosis algorithms were calculated: NAFLD fibrosis score, BARD, NIKEI, NASH-CRN regression score, APRI, FIB-4, King´s score, GUCI, Lok index, Forns score, and ELF. Study population was randomly divided in a training and a validation group. A multiple logistic regression analysis using bootstrapping methods was applied to the training group. Among many variables analyzed age, fasting glucose, hyaluronic acid and AST were included, and a model (LINKI-1) for predicting advanced fibrosis was created. Moreover, these variables were combined with platelet count in a mathematical way exaggerating the opposing effects, and alternative models (LINKI-2) were also created. Models were compared using area under the receiver operator characteristic curves (AUROC).ResultsOf established algorithms FIB-4 and King´s score had the best diagnostic accuracy with AUROCs 0.84 and 0.83, respectively. Higher accuracy was achieved with the novel LINKI algorithms. AUROCs in the total cohort for LINKI-1 was 0.91 and for LINKI-2 models 0.89.ConclusionThe LINKI algorithms for detection of advanced fibrosis in NAFLD showed better accuracy than established algorithms and should be validated in further studies including larger cohorts.

The Non-Invasive Liver Biopsy : Determining Hepatic Function in Diffuse and Focal LiverDisease

“…In contrast, even though 38% of our cohort had NAFLD, NAFLD fibrosis score, NIKEI and NASH-CRN regression score did not generate the same precision. These findings are in line with a recent study which showed that FIB-4 and King's score were superior in diagnosing advanced fibrosis in a pure NAFLD cohort (156). This recent study and ours indicates that King's score or similar algorithms (GUCI and APRI) might be suitable in cohorts with liver disease of various aetiologies for the detection of advanced fibrosis.…”

A Predictive model for liver disease progression based on logistic regression algorithm

“…Prediction of liver disease Categories Occurrence Probability Vijayarani, Dhayanand [7] Yes NO Dhamodharan [8] Yes NO Karthik, Priyadarishini [9] Yes NO Nahar, Ara [10] Yes NO Kumar, Sahoo [11] Yes NO Wu, Yeh [12] Yes NO Hashem and Mabrouk [13] Yes NO Nagaraj and Sridhar [14] Yes NO Vijayarani, Dhayanand [7] have compared the performance of classification algorithms to predict liver diseases. The compared algorithms in this work were Naïve Bayes (NB) and support vector machine (SVM).…”

“…The experiment's result showed that Naïve Bayes has outstanding the FT Tree algorithm in the accuracy measure. Karthik, Priyadarishini [9] applied a soft computing method to identify the liver disease. The authors have applied the method in three phases.…”

“…Despite the excellent performance of the proposed models in the literature [7][8][9][10][11][12]14], these models, however, did not handle the problem of predicting the probability of LIVER DISEASE occurrence. Also, the models are more general, where they have proposed to predict the categories of the disease (e.g., healthy, or patient).…”

“…Moreover, the efficacy of antiviral treatment to prevent the progression of disease in patients with chronic hepatitis B and cirrhosis or advanced fibrosis is obscure [6]. Many studies have been carried out to diagnose liver disease using blood tests combined with various risk factors [7][8][9][10][11][12][13][14]. Although there have been some excellent results from these models, the disease's progression remains obscure.…”