Machine learning to identify a composite indicator to predict cardiac death in ischemic heart disease.

Background: Machine learning (ML) employs algorithms that learn from data, building models with the potential to predict events by aggregating a large number of variables and assessing their complex interactions. The aim of this study is to assess ML potential in identifying patients with ischemic heart disease (IHD) at high risk of cardiac death (CD).

Methods: 3987 (mean age 68 ± 11) hospitalized IHD patients were enrolled.

HOXD8 hypermethylation as a fully sensitive and specific biomarker for biliary tract cancer detectable in tissue and bile samples.

Background: Biliary tract cancers (BTC) are rare but highly aggressive tumours with poor prognosis, usually detected at advanced stages. Herein, we aimed at identifying BTC-specific DNA methylation alterations.

Methods: Study design included statistical power and sample size estimation.