Lower intrafamilial transmission rate of hepatitis B in patients with hepatitis d coinfection: a data-mining approach.
BACKGROUND
The presence of an infected family member significantly increases the risk of HBV transmission, but many socio-demographic and viral characteristics of family members affect the transmission rate.


OBJECTIVE
In this study, we have used data mining techniques to investigate the impact of different variables in intrafamilial transmission of HBV infection.


METHODS
demographic information, viral markers, and medical history of 330 patients with chronic hepatitis B and their offspring attending a referral center in Tehran were collected. Data-mining techniques were administered to detect patterns.


RESULTS
The overall transmission rate was 15.7% (5.4% and 27.3% for male and female index cases respectively). In female patients, HBe Ag positively affected the transmission rate (49% vs. 23.4%). There was a dominant change in transmission rate of female patients with negative results for Hbe Ag with HDV coinfection, where the transmission rate changed from 25% in patients with negative results for HDV Ab to 5% in those with positive results. In Hbe Ag negative male index cases, the transmission rate was 1.3% in cases with positive results for HDV Ab compared to 7% in those with negative findings. The overall transmission rate was statistically different between patients with positive and negative results for HDV Ab (P = 0.016).


CONCLUSIONS
There is a minor but consistent pattern change in the presence of HDV infection which reduces familial transmission of HBV, especially in female patients with negative results for HBe Ag.
Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, IR Iran ; Medical Informatics Research Center, Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, IR Iran.
Omid
O
Pournik
Seyed Moayed
SM
Alavian
Leila
L
Ghalichi
Bashir
B
Hajibeigi
Amir Reza
AR
Razavi
Saeid
S
Eslami
Data Mining; Hepatitis B Virus; Hepatitis D; Machine Learning