Risk factors and machine learning prediction models for intrahepatic cholestasis of pregnancy.
Journal:
BMC pregnancy and childbirth
PMID:
39885442
Abstract
BACKGROUND: Intrahepatic cholestasis of pregnancy (ICP) is a liver disorder that occurs in the second and third trimesters of pregnancy and is associated with a significant risk of fetal complications, including premature birth and fetal death. In clinical practice, the diagnosis of ICP is predominantly based on the presence of pruritus in pregnant women and elevated serum total bile acid. However, this approach may result in missed or delayed diagnoses. Therefore, it is essential to explore the risk factors associated with ICP and to accurately identify affected individuals to enable timely prophylactic interventions. The existing literature exhibits a paucity of studies employing artificial intelligence to predict ICP. Therefore, developing machine learning-based diagnostic and severity classification models for ICP holds significant importance.