Clinical Risk Scoring Scheme for Predicting Non-Alcoholic Fatty Liver Disease in Metabolic Syndrome Patients (NAFLD-MS score) Surasak Saokaew1-3, Shada Kanchanasuwan4, Piyaporn Apisarnthanarak5, Aphinya Charoensak5, Phunchai Charatcharoenwitthaya6, Pochamana Phisalprapa7*, Nathorn Chaiyakunapruk2,3,8,9 1
Center of Health Outcomes Research and Therapeutic Safety (Cohorts), School of Pharmaceutical Sciences, University of Phayao, Thailand; 2 School of Pharmacy, Monash University Malaysia, Selangor, Malaysia; 3 Center of Pharmaceutical Outcomes Research (CPOR), Department of Pharmacy Practice, Faculty of Pharmaceutical Sciences, Naresuan University, Phitsanulok, Thailand; 4 Clinical and Administrative Pharmacy, The University of Georgia College of Pharmacy, Georgia, USA; 5 Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand; 6 Division of Gastroenterology, Department of Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand; 7 Division of Ambulatory Medicine, Department of Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand; 8 School of Pharmacy, University of Wisconsin, Madison, USA; 9 School of Population Health, University of Queensland, Brisbane, Australia; *Corresponding author:
[email protected]
Background and Objective • Nonalcoholic fatty liver disease (NAFLD) is an increasing prevalent liver disease due to obesity epidemic (1, 2). • Prevalence of NAFLD is 6-35% worldwide and 15-20% in Asia (1-3) and very high In obese patients (57-98%) and metabolic syndrome patients (69-87%) (2). • Gold standard of NAFLD diagnosis is liver biopsy. However, due to its invasiveness, diagnostic methods have shifted to non-invasive techniques i.e. ultrasonography (pooled sensitivity of 84.8% and specificity of 93.6%) but it is not used for screening of NAFLD due to its high cost and inconvenient procedure. • In developing country such as Thailand, with limited human resources and equipment in conducting liver ultrasound, a simple NAFLD screening tool will help select patients in need of liver ultrasound. • This study aimed to develop a simple risk scoring system to predict NAFLD in metabolic syndrome patients to serve as a primary screening tool for NAFLD. On top of that, by using this new developed NAFLD screening tool, the country can save money from unnecessary liver ultrasound.
Results (cont’) Predictors
Coefficient
BMI >25 AST/ALT≥ 1 ALT ≥ 40 DM type 2 Central obese
Adjusted Odds ratio 2.63 2.32 4.49 2.31 1.93
95% CI of adjusted odds ratio 1.61, 4.29 1.41, 3.80 1.48, 13.55 1.43, 13.55 0.98, 3.79
0.967157 0.841081 1.502436 0.835602 0.658639 Table 2 Multivariable analysis and risk score for NAFLD
P-value Assigned Score < 0.001 1.5 0.001 1 0.008 2 0.001 1 0.056 1
Methods • The study was conducted at Siriraj Hospital, Faculty of Medicine, Mahidol University. • The study was approved by Siriraj Institutional Review Board, Faculty of Medicine Siriraj Hospital, Mahidol University, Thailand. • Eligible subjects were patients diagnosed with metabolic syndrome according to the standard definition (4) • Number of patients, model development and validation was showed in fig 1.
Fig. 2 Risk score calculator
Fig. 3 Derivation cohort
Derivation cohort (n=400)
Fig. 1 Number of patients, model development and validation
Results Derivation cohorts Variable (n=400) Odds ratio 95% CI of odds ratio P-value Age (per year) 0.95 0.94, 0.98 < 0.0001 Female 1.20 0.79, 1.83 0.3898 Smoker 0.46 0.23, 0.92 0.0253 Impaired fasting blood sugar 0.67 0.43, 1.06 0.0888 Diabetes Mellitus 2.34 1.51, 3.66 0.0001 Hypertension 1.10 0.54, 2.24 0.7861 Dyslipidemia 1.05 0.39, 2.87 0.9200 BMI > 25 3.72 2.34, 5.93 < 0.001 Central obese 2.85 1.54, 5.26 0.0004 AST/ALT ≥ 1 3.78 2.34, 6.08 < 0.0001 ALT > 40 U/L 8.32 2.86, 24.22 < 0.0001 Table 1 Univariable analysis of risk factors associated with NAFLD in derivation cohort
Total No NAFLD NAFLD Diagnostic performance Sensitivity Specificity Positive predictive value Negative predictive value Likelihood ratio (+) Likelihood ratio (-) Interpretation
Fig. 4 Validation cohort
Low (score
