Affiliations 

  • 1 Center of Health Outcomes Research and Therapeutic Safety (Cohorts), School of Pharmaceutical Sciences, University of Phayao, Phayao, Thailand
  • 2 Clinical and Administrative Pharmacy, The University of Georgia College of Pharmacy, Athens, GA, USA
  • 3 Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
  • 4 Division of Gastroenterology, Department of Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
  • 5 Division of Ambulatory Medicine, Department of Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
  • 6 School of Pharmacy, Monash University Malaysia, Selangor, Malaysia
Liver Int, 2017 Oct;37(10):1535-1543.
PMID: 28294515 DOI: 10.1111/liv.13413

Abstract

BACKGROUND & AIMS: Non-alcoholic fatty liver disease (NAFLD) can progress from simple steatosis to hepatocellular carcinoma. None of tools have been developed specifically for high-risk patients. This study aimed to develop a simple risk scoring to predict NAFLD in patients with metabolic syndrome (MetS).

METHODS: A total of 509 patients with MetS were recruited. All were diagnosed by clinicians with ultrasonography-confirmed whether they were patients with NAFLD. Patients were randomly divided into derivation (n=400) and validation (n=109) cohort. To develop the risk score, clinical risk indicators measured at the time of recruitment were built by logistic regression. Regression coefficients were transformed into item scores and added up to a total score. A risk scoring scheme was developed from clinical predictors: BMI ≥25, AST/ALT ≥1, ALT ≥40, type 2 diabetes mellitus and central obesity. The scoring scheme was applied in validation cohort to test the performance.

RESULTS: The scheme explained, by area under the receiver operating characteristic curve (AuROC), 76.8% of being NAFLD with good calibration (Hosmer-Lemeshow χ2 =4.35; P=.629). The positive likelihood ratio of NAFLD in patients with low risk (scores below 3) and high risk (scores 5 and over) were 2.32 (95% CI: 1.90-2.82) and 7.77 (95% CI: 2.47-24.47) respectively. When applied in validation cohort, the score showed good performance with AuROC 76.7%, and illustrated 84%, and 100% certainty in low- and high-risk groups respectively.

CONCLUSIONS: A simple and non-invasive scoring scheme of five predictors provides good prediction indices for NAFLD in MetS patients. This scheme may help clinicians in order to take further appropriate action.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.