Application of Principal Component Analysis to Heterogenous Fontan Registry Data Identifies Independent Contributing Factors to Decline

Abstract

Single ventricle heart disease is a severe and life-threatening illness, and improvements in clinical outcomes of those with Fontan circulation have not yet yielded acceptable survival over the past two decades. Patients are at risk of developing a diverse variety of Fontan-associated comorbidities that ultimately requires heart transplant. Our study goal was to determine if principal component analysis (PCA) applied to data collected from a substantial Fontan cohort can predict functional decline (N=140). Heterogeneous data broadly consisting of measures of cardiac and vascular function, exercise (VO2max), lymphatic biomarkers, and blood biomarkers were collected over 11 years at a single site; in that time, 16 events occurred that are considered here in a composite outcome measure. After standardization and PCA, principal components (PCs) representing >5% of total variance were thematically labeled based on their constituents and tested for association with the composite outcome. Our main findings suggest that the 6th PC (PC6), representing 7.1% percent of the total variance in the set, is greatly influenced by blood serum biomarkers and superior vena cava flow, is a superior measure of proportional hazard compared to EF, and displayed the greatest accuracy for classifying Fontan patients as determined by AUC. In bivariate hazard analysis, we found that models combining systolic function (EF or PC5) and lymphatic dysfunction (PC6) were most predictive, with the former having the greatest AIC, and the latter having the highest c-statistic. Our findings support our hypothesis that a multifactorial model must be considered to improve prognosis in the Fontan population.