Discontinuation of nucleos(t)ide analogues is not associated with a higher risk of HBsAg seroreversion after antiviral-induced HBsAg seroclearance: a nationwide multicentre study.

OBJECTIVE: Direct comparison of the clinical outcomes between nucleos(t)ide analogue (NA) discontinuation versus NA continuation has not been performed in patients with chronic hepatitis B who achieved HBsAg-seroclearance. Whether NA discontinuation was as safe as NA continuation after NA-induced surface antigen of HBV (HBsAg) seroclearance was investigated in the present study. DESIGNS: This multicentre study included 276 patients from 16 hospitals in Korea who achieved NA-induced HBsAg seroclearance: 131 (47.5%) discontinued NA treatment within 6 months after HBsAg seroclearance (NA discontinuation group) and 145 (52.5%) continued NA treatment (NA continuation group). Primary endpoint was HBsAg reversion and secondary endpoints included serum HBV DNA redetection and development of hepatocellular carcinoma (HCC). RESULTS: During follow-up (median=26.9 months, IQR=12.2-49.2 months), 10 patients (3.6%) experienced HBsAg reversion, 6 (2.2%) showed HBV DNA redetection and 8 (2.9%) developed HCC. Compared with NA continuation, NA discontinuation was not associated with HBsAg reversion in both univariable (HR=0.45, 95% CI=0.12 to 1.76, log-rank p=0.24) and multivariable analyses (adjusted HR=0.65, 95% CI=0.16 to 2.59, p=0.54). The cumulative probabilities of HBsAg reversion at 1, 3 and 5 years were 0.8%, 2.3% and 5.0% in the NA discontinuation group, and 1.5%, 6.3% and 8.4% in the NA continuation group, respectively. NA discontinuation was not associated with higher risk of either HBV redetection (HR=0.83, 95% CI=0.16 to 4.16, log-rank p=0.82) or HCC development (HR=0.53, 95% CI=0.12 to 2.23, log-rank p=0.38). CONCLUSION: The discontinuation of NA was not associated with a higher risk of either HBsAg reversion, serum HBV DNA redetection or HCC development compared with NA continuation among patients who achieved HBsAg seroclearance with NA.

Predicting breast cancer survivability using fuzzy decision trees for personalized healthcare.

Data analysis systems, intended to assist a physician, are highly desirable to be accurate, human interpretable and balanced, with a degree of confidence associated with final decision. In cancer prognosis, such systems estimate recurrence of disease and predict survival of patient; hence resulting in improved patient management. To develop such a prognostic system, this paper proposes to investigate a hybrid scheme based on fuzzy decision trees, as an efficient alternative to crisp classifiers that are applied independently. Experiments were performed using different combinations of: number of decision tree rules, types of fuzzy membership functions and inference techniques. For this purpose, SEER breast cancer data set (1973-2003), the most comprehensible source of information on cancer incidence in United States, is considered. Performance comparisons suggest that, for cancer prognosis, hybrid fuzzy decision tree classification is more robust and balanced than independently applied crisp classification; moreover it has a potential to adapt for significant performance enhancement.