Smart phone application using transient elastography measured liver stiffness, spleenic stiffness and liver transaminases, to exclude the presence esophageal/cardio-fundal varices in compensated cirrhosis of non-viral aetiology.

Introduction and objectiveWe used AST to ALT ratio (AAR) and, liver stiffness measurement (LSM), splenic stiffness measurement (SSM) by transient elastography to develop a statistical model and present it as a user-friendly smartphone application to exclude the presence of oesophageal and cardio-fundal varices to avoid upper gastrointestinal endoscopy in selected patients. MethodsA prospective study was carried out among patients with Child-Pugh Class A cirrhosis (non-viral and BMI<30kg/m2). LSM and SSM were obtained using Fibroscan (EchoSens) by a single operator, blinded to the presence or absence of varices. The predictors used to develop the formula were AAR, LSM and SSM. Multiple logistic regression was used to create the algorithms in 70% of the sample and validated using 30% of the sample with Bootstrapping of 1000. Best algorithms with the highest area under the curve (AUC) were selected and identified as different cut-off levels to exclude or predict the presence of varices. Those values were included in a smartphone application on android and iOS web-based platforms. ResultsOne hundred and nine out of 211 had varices. After modelling different combinations, logistic regression formula (LRF)=5.577+(LSM*0.035)+(SSM*0.08)+(AAR*1.48) resulted AUCs 0.93. Cut-off value <-1.26 of LRF predicted the exclusion of varices with a negative predictive value of 90%. Cut-off value >0.829 of LRF predicted the presence of varices with a positive predictive value of 91%. Multiple values were used to develop a smartphone app on the Angular 2+ platform. (It can be downloaded for use @https://mediformula-65ef0.web.app/). ConclusionThe new formula using AAR, LSM and SSM can be used to predict exclusion of varices with high accuracy in non-obese patients with compensated cirrhosis of non-viral aetiology based on the patients biochemical or fibroscan values. The smartphone application derived from this model is easy to use. It is the first mobile application to be used to exclude or predict the presence of varices utilizing SSM.