Portal Venous Pulsatility Index as a predictor of fibrosis in patients with non-alcoholic fatty liver disease.

INTRODUCTION: Non-alcoholic fatty liver disease (NAFLD) is a growing public health issue. Non-invasive methods to assess the fibrosis stage are limited, and biopsy remains the gold standard. The objective of our study was to assess whether the portal venous pulsatility index (VPI) can be used as a predictor of high-risk NAFLD at a tertiary referral centre for liver transplantation. METHODS: We retrospectively reviewed patients with biopsy-proved NAFLD who had undergone a liver ultrasound scan within 1 year of biopsy at our centre from 2011 to 2019. RESULTS: We did not find a significant correlation between the VPI and the NAFLD risk category or correlation between the VPI and degree of steatosis (P > 0.05 for both). CONCLUSION: Since VPI can be easily obtained on routine liver ultrasound and since other studies do report a positive association with significant fibrosis, more studies are needed before it can be recommended or not in risk-stratifying NAFLD patients into high- vs. low-risk NAFLD.

Combined prediction model of death toll for road traffic accidents based on independent and dependent variables.

In order to build a combined model which can meet the variation rule of death toll data for road traffic accidents and can reflect the influence of multiple factors on traffic accidents and improve prediction accuracy for accidents, the Verhulst model was built based on the number of death tolls for road traffic accidents in China from 2002 to 2011; and car ownership, population, GDP, highway freight volume, highway passenger transportation volume, and highway mileage were chosen as the factors to build the death toll multivariate linear regression model. Then the two models were combined to be a combined prediction model which has weight coefficient. Shapley value method was applied to calculate the weight coefficient by assessing contributions. Finally, the combined model was used to recalculate the number of death tolls from 2002 to 2011, and the combined model was compared with the Verhulst and multivariate linear regression models. The results showed that the new model could not only characterize the death toll data characteristics but also quantify the degree of influence to the death toll by each influencing factor and had high accuracy as well as strong practicability.