A systems biology study to tailored treatment in chronic heart failure

This thesis was part of the The BIOlogy STudy to TAilored Treatment in Chronic Heart Failure (BIOSTAT-CHF) project. BIOSTAT-CHF was a European, multicenter, multinational, prospective, observational study that was especially designed to find biological mechanisms involved with response to ESC guideline recommended pharmacological therapy.
The aim and focus of this thesis was twofold. In the first part, we focused on individualized treatment of heart failure. We looked at what heart failure prediction models were published and which factors resulted in more accurate predictions. We tried to identify heart failure patients with low or high risk of mortality and/or heart failure related hospitalization. We also looked at which patients achieved recommended treatment doses for guideline recommended pharmacological therapy and which did not. In addition to this, we tried to estimate if a patient would benefit from receiving recommended treatment doses or not based on prediction models consisting solely on biomarkers.
The second part, was of a more methodological nature. We looked at the consistency and robustness of different clustering methods, and we clustered patients based not on clinical, but pathophysiological parameters. We also developed two methods to analyze the enormous amounts of data collected in systems biology. The first method estimates possible existence of splice variants in gene expression data. The second method is a penalized canonical correlation analysis, which enables to analyze multiple high-dimensional datasets in such a way that a set of variables in one dataset correlates maximally with the sets of variables in all other datasets.