Background Liver fibrosis is a stage of non-alcoholic fatty liver disease (NAFLD) which is responsible for liver-related morbidity and mortality in adults. Accordingly, the search for non-invasive markers of liver fibrosis has been the subject of intensive efforts in adults with NAFLD. Here, we developed a simple algorithm for the prediction of liver fibrosis in children with NAFLD followed at a tertiary care center. Methods The study included 136 male and 67 female children with NAFLD aged 3.3 to 18.0 years; 141 (69%) of them had fibrosis at liver biopsy. On the basis of biological plausibility, readily availability and evidence from adult studies, we evaluated the following potential predictors of liver fibrosis at bootstrapped stepwise logistic regression: gender, age, body mass index, waist circumference, alanine transaminase, aspartate transaminase, gamma-glutamyl-transferase, albumin, prothrombin time, glucose, insulin, triglycerides and cholesterol. A final model was developed using bootstrapped logistic regression with bias-correction. We used this model to develop the 'pediatric NAFLD fibrosis index' (PNFI), which varies between 0 and 10. Results The final model was based on age, waist circumference and triglycerides and had a area under the receiver operating characteristic curve of 0.85 (95% bootstrapped confidence interval (CI) with bias correction 0.80 to 0.90) for the prediction of liver fibrosis. A PNFI ≥ 9 (positive likelihood ratio = 28.6, 95% CI 4.0 to 201.0; positive predictive value = 98.5, 95% CI 91.8 to 100.0) could be used to rule in liver fibrosis without performing liver biopsy. Conclusion PNFI may help clinicians to predict liver fibrosis in children with NAFLD, but external validation is needed before it can be employed for this purpose.

The pediatric NAFLD fibrosis index: a predictor of liver fibrosis in children with non-alcoholic fatty liver disease.

TIRIBELLI, CLAUDIO;
2009

Abstract

Background Liver fibrosis is a stage of non-alcoholic fatty liver disease (NAFLD) which is responsible for liver-related morbidity and mortality in adults. Accordingly, the search for non-invasive markers of liver fibrosis has been the subject of intensive efforts in adults with NAFLD. Here, we developed a simple algorithm for the prediction of liver fibrosis in children with NAFLD followed at a tertiary care center. Methods The study included 136 male and 67 female children with NAFLD aged 3.3 to 18.0 years; 141 (69%) of them had fibrosis at liver biopsy. On the basis of biological plausibility, readily availability and evidence from adult studies, we evaluated the following potential predictors of liver fibrosis at bootstrapped stepwise logistic regression: gender, age, body mass index, waist circumference, alanine transaminase, aspartate transaminase, gamma-glutamyl-transferase, albumin, prothrombin time, glucose, insulin, triglycerides and cholesterol. A final model was developed using bootstrapped logistic regression with bias-correction. We used this model to develop the 'pediatric NAFLD fibrosis index' (PNFI), which varies between 0 and 10. Results The final model was based on age, waist circumference and triglycerides and had a area under the receiver operating characteristic curve of 0.85 (95% bootstrapped confidence interval (CI) with bias correction 0.80 to 0.90) for the prediction of liver fibrosis. A PNFI ≥ 9 (positive likelihood ratio = 28.6, 95% CI 4.0 to 201.0; positive predictive value = 98.5, 95% CI 91.8 to 100.0) could be used to rule in liver fibrosis without performing liver biopsy. Conclusion PNFI may help clinicians to predict liver fibrosis in children with NAFLD, but external validation is needed before it can be employed for this purpose.
http://bmcmedicine.biomedcentral.com/articles/10.1186/1741-7015-7-21
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11368/2853487
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