METHODS: We obtained viral hepatitis mortality data from the WHO Mortality Database for six East and Southeast Asian countries between 1987 and 2015. We produced choropleth maps of viral hepatitis mortality rates in 1987 and 2015 in East and Southeast Asia to illustrate geographic variations. We made predictions of mortality rates for each included country until the year 2030 using a series of joinpoint models.
RESULTS: Viral hepatitis mortality rates declined in China (the average annual percent change (AAPC) = -5.1%, 95% CI: -7.5, -2.6), Singapore (AAPC = -5.4%, 95% CI: -7.5, -3.2), and the Philippines (AAPC = -3.4%, 95% CI: -4.9, -1.8). In contrast, Japan, the Republic of Korea, and Malaysia have experienced increasing trends in mortality rates, followed by decreasing trends. Our predictions indicate that all countries will experience slight to moderate downward trends until 2030.
CONCLUSION: Favourable decreasing trends have been noted in East and Southeast Asian countries, which may not only inform the control and management of viral hepatitis in this region but also guide the prevention of viral hepatitis deaths in another region with a similar viral hepatitis epidemic.
METHODS: We evaluated 88 breast cancer risk variants that were identified previously by GWAS in 11,760 cases and 11,612 controls of Asian ancestry. SNPs confirmed to be associated with breast cancer risk in Asian women were used to construct a polygenic risk score (PRS). The relative and absolute risks of breast cancer by the PRS percentiles were estimated based on the PRS distribution, and were used to stratify women into different levels of breast cancer risk.
RESULTS: We confirmed significant associations with breast cancer risk for SNPs in 44 of the 78 previously reported loci at P
OBJECTIVE: The aim of this proof-of-concept study was to evaluate whether combining population pharmacokinetic and machine learning approaches could provide a more accurate prediction of the clearance of renally eliminated drugs in individual neonates.
METHODS: Six drugs that are primarily eliminated by the kidneys were selected (vancomycin, latamoxef, cefepime, azlocillin, ceftazidime, and amoxicillin) as 'proof of concept' compounds. Individual estimates of clearance obtained from population pharmacokinetic models were used as reference clearances, and diverse machine learning methods and nested cross-validation were adopted and evaluated against these reference clearances. The predictive performance of these combined methods was compared with the performance of two other predictive methods: a covariate-based maturation model and a postmenstrual age and body weight scaling model. Relative error was used to evaluate the different methods.
RESULTS: The extra tree regressor was selected as the best-fit machine learning method. Using the combined method, more than 95% of predictions for all six drugs had a relative error of < 50% and the mean relative error was reduced by an average of 44.3% and 71.3% compared with the other two predictive methods.
CONCLUSION: A combined population pharmacokinetic and machine learning approach provided improved predictions of individual clearances of renally cleared drugs in neonates. For a new patient treated in clinical practice, individual clearance can be predicted a priori using our model code combined with demographic data.