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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
Linköping University, Department of Medical and Health Sciences. Linköping University, Faculty of Medicine and Health Sciences.
Karolinska Institute, Sweden.
Linköping University, Department of Clinical and Experimental Medicine, Division of Neuro and Inflammation Science. Linköping University, Faculty of Medicine and Health Sciences.
Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
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2016 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 11, no 12, article id e0167776Article in journal (Refereed) Published
Abstract [en]

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, Kings 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 Kings 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.

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PUBLIC LIBRARY SCIENCE , 2016. Vol. 11, no 12, article id e0167776
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Bioinformatics (Computational Biology)
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URN: urn:nbn:se:liu:diva-133732DOI: 10.1371/journal.pone.0167776ISI: 000389587100185PubMedID: 27936091OAI: oai:DiVA.org:liu-133732DiVA, id: diva2:1063882
Note

Funding Agencies|Royal Swedish Academy of Sciences Foundations [ME2015-0011]; Medical Research Council of Southeast Sweden [F2004-303]

Available from: 2017-01-11 Created: 2017-01-09 Last updated: 2018-03-19

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Fredrikson, MatsIgnatova, SimoneEkstedt, MattiasKechagias, Stergios
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Department of Medical and Health SciencesFaculty of Medicine and Health SciencesDivision of Neuro and Inflammation ScienceDivision of Cell BiologyDivision of Cardiovascular MedicineDepartment of Gastroentorology
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