BIOSTAT-CHF

We use several artificial intelligence applications (such as network/pathway analyses, cluster analyses, machine learning en system biology) using large scale -omics data (genomics, proteomics, transcriptomics, and biomarkers) to get a better insight in the pathophysiology of specific groups of patients with heart failure, leading to better and more personalized therapies of patients with heart failure.
From 2010-2015 data from 2 large cohorts of patients with heart failure ( n=2516 and n=1738) were collected and information on clinical outcome was captured. During a median of 21 months, 45% of patients died or had to be hospitalized for worsening of heart failure, indicating the severity of this disease.

From 2015 – now, a large group of researchers have performed various analyses and in the meantime more than 50 papers are published from this project, all of which were published in Q1 journals.

We are currently looking for PhD students and post-docs, both medical doctors, (medical) biologists and statisticians and bio-informaticians to support future studies to learn more about pathways related to disease progression, with specific attention to sex-differences, atrial fibrillation, diabetes and the differences between patients with heart failure with preserved and reduced ejection fraction (HFpEF versus HFrEF)

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Women benefit from lower doses of heart failure medications