Abstract Body (Do not enter title and authors here): Introduction. 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 may ultimately require heart transplant.
Goal. Our study goal was to determine if principal component analysis (PCA), which determines unique directions of data variance, applied to data collected from a pediatric Fontan cohort can predict functional decline (N=140).
Methods. Heterogeneous data broadly consisting of measures of cardiac and vascular function, exercise (VO_2max), lymphatic biomarkers, and blood biomarkers were collected over 9 years at a single site as part of a prospectively applied clinical care pathway; in that time, 16 events occurred that are considered here in a composite adverse 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.
Results. Our main findings suggest that the 6^th PC (PC6), representing 7.1% percent of the total variance in the set, is greatly influenced by albumin, alkaline phosphatase, total protein, BUN and BNP max and is a superior measure of proportional hazard compared to EF; it 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 blood biomarkers (PC6) were most predictive, with the former having the greatest AIC, and the latter having the highest c-statistic.
Conclusion. Our findings support the hypothesis that a broad, prospectively collected, multiorgan system model analyzed by way of unsupervised machine learning methods will improve prognosis of adverse outcomes in a Fontan cohort.