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

Byron Lykiardopoulos, Hannes Hagström, Mats Fredrikson, Simone Ignatova, Per Stål, Rolf Hultcrantz, Mattias Ekstedt and Stergios Kechagias

PLOS ONE, 2016, vol. 11, issue 12, 1-17

Abstract: Background and Aim: Detection 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. Methods: We 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). Results: Of 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. Conclusion: The 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.

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0167776

DOI: 10.1371/journal.pone.0167776

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