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Development of non-alcoholic fatty liver disease scoring system among adult medical check-up patients: a large cross-sectional and prospective validation study

Authors Lesmana C, Pakasi L, Inggriani S, Aidawati M, Lesmana L

Received 5 January 2015

Accepted for publication 10 March 2015

Published 23 April 2015 Volume 2015:8 Pages 213—218


Checked for plagiarism Yes

Review by Single-blind

Peer reviewer comments 4

Editor who approved publication: Professor Ming-Hui Zou

Cosmas Rinaldi A Lesmana,1,2 Levina S Pakasi,1 Sri Inggriani,3 Maria L Aidawati,3 Laurentius A Lesmana1

1Digestive Disease and GI Oncology Centre, Medistra Hospital, 2Department of Internal Medicine, Hepatobiliary Division, Cipto Mangunkusumo Hospital, University of Indonesia, 3Radiology Department, Medistra Hospital, Jakarta, Indonesia

Background: Non-alcoholic fatty liver disease (NAFLD) is the most common liver disease in the community. However, NAFLD remains undiagnosed in most people with limited access to imaging facilities in most developing countries.
Objective: To examine the prevalence of NAFLD and to develop the risk scoring model for predicting the presence of NAFLD among adult medical check-up patients.
Method: A large prospective cross-sectional study was conducted among medical check-up patients who underwent transabdominal ultrasound examination between January and December 2013 in Medistra Hospital, Jakarta. Data were obtained from the patients' medical records. Logistic regression analyses were undertaken to identify the best combination of risk factors for predicting fatty liver using the backward (likelihood ratio) approach. The adjusted odds ratio and 95% confidence interval were estimated using the logistic regression coefficient. The prediction model was assessed using the receiver operating characteristic curve and the Hosmer–Lemeshow goodness-of-fit test and was validated on a new, prospective cohort. Statistical analysis was done using SPSS version 17.
Results: A total of 1,054 cases was included in this study. Fatty liver was present in 538 (51.0%) patients. Bivariate analyses found associations among fatty liver and several risk factors. Six risk factors were incorporated to build the final prediction model. All scores were summed up to obtain the total score. A probability equation was developed by applying linear regression analysis on the total score. The prediction model had good diagnostic performance with an area under the receiver operating characteristic curve =0.833 (95% confidence interval =0.809–0.857). The Hosmer–Lemeshow goodness-of-fit P-value was 0.232, which indicated the appropriateness of the logistic regression model to predict fatty liver. On the validation set, the scoring system proved to be moderately accurate and can potentially be applied to larger population setting.
Conclusion: The presence of fatty liver in NAFLD patients can be predicted using our proposed fatty liver scoring system.

Keywords: fatty liver, scoring model, ultrasound, community, developing countries, diagnostic performance

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