Metabolomics and random forests in patients with complex congenital heart disease - Michel, Miriam; Laser, Kai Thorsten; Dubowy, Karl-Otto; Scholl-Bürgi, Sabine; Michel, Erik (Frontiers in Cardiovascular Medicine, 2022)
Introduction. It becomes increasingly common to simultaneously determine a cornucopia of metabolites in order to assess the metabolic state of, or clarify biochemical pathways in, an organism (‚metabolomics‘). This approach is increasingly used in the investigation of the development of heart failure. Recently there appeared first reports with respect to a metabolomics approach for the assessment of patients with complex congenital heart disease. Classical statistical analysis of such data is challenging. Objective. To present an alternative to classical statistics with respect to identifying relevant metabolites in a classification task and numerically estimating their relative impact. Methods. Data from two metabolomics studies on 20 patients with complex congenital heart disease and Fontan circulation and 20 controls were reanalysed using Random Forest (RF) methodology. Results were compared to those of classical statistics. Results. RF analysis required no elaborate data preprocessing. The ranking of the variables with respect to classification impact (subject diseased, or not) was remarkably similar irrespective of the evaluation method used, leading to identical clinical interpretation. Conclusion. In metabolomics classification in adult patients with complex congenital heart disease, RF analysis as a one-step method delivers most adequate results with minimum effort.