Send to

Choose Destination
See comment in PubMed Commons below
Obes Surg. 2010 Jun;20(6):685-91. doi: 10.1007/s11695-010-0118-y.

A noninvasive clinical scoring model predicts risk of nonalcoholic steatohepatitis in morbidly obese patients.

Author information

Division of Gastroenterology and Hepatology, Department of Medicine, Medical College of Wisconsin, 9200 W Wisconsin Avenue, Milwaukee, WI 53226, USA.



A simple model to predict nonalcoholic steatohepatitis (NASH) in patients with nonalcoholic fatty liver disease is desirable to optimize the selection of patients for liver biopsy. We investigated a large group of morbidly obese patients to derive a scoring system based on simple clinical and laboratory variables.


Consecutive subjects undergoing bariatric surgery and without evidence of other liver disease or significant alcohol use underwent intraoperative liver biopsy. Demographic, clinical, and biochemical variables were collected. A scoring model was derived using variables found to be independent predictors of NASH. The scores were divided into four risk categories (low, intermediate, high, and very high). Positive and negative predictive values (PPV/NPV) were derived for each category and the area under the receiver operator curve (AUROC) was calculated.


A total of 253 subjects were included: 52 (20.6%) had NASH, 116 (45.8%) had simple steatosis, and 85 (33.6%) had normal liver histology. Only ten subjects (19% of NASH group) had significant (>or= stage 2) fibrosis. Multivariate analysis identified diabetes, abnormal ALT, and hypertriglyceridemia as independent predictors of NASH. Sleep apnea showed a strong trend toward significance and was also included in the model. This model showed a NPV of 89.7% in the low risk category and a PPV of 75% in the very high risk category, with AUROC of 0.76.


A simple scoring system performs well in predicting NASH and can be used in the clinic to optimize the selection of morbidly obese patients for liver biopsy.

[Indexed for MEDLINE]
PubMed Commons home

PubMed Commons


    Supplemental Content

    Full text links

    Icon for Springer
    Loading ...
    Support Center