{"doi":"10.70322/cvs.2025.10005","title":"Application of Principal Component Analysis to Heterogenous Fontan Registry Data Identifies Independent Contributing Factors to Decline","abstract":"Single ventricle disease is a serious and deadly illness, and advances in clinical management of individuals with Fontan circulation over the past two decades have yet to yield acceptable survival. Patients remain at risk of developing a diverse assortment of Fontan-associated comorbidities that ultimately require a heart transplant. Our goal in this observational cohort study was to determine if application of principal component analysis (PCA) to heterogeneous data collected from a sizable Fontan cohort (n = 140) would predict functional decline. The data, broadly comprised of blood biomarkers, lymphatic biomarkers, measures of cardiac and vascular function, and exercise (VO2max), were collected at a single site over 11 years; 16 events occurred over that time that we consider here as a single composite outcome measure. The standardized data was transformed via PCA, and principal components (PCs) characterizing >5% of total variance were thematically labeled based on their constituents and tested for association with the composite outcome. We found that the 6th PC (PC6), which represents 7.1% of the total variance, is superior to ejection fraction (EF) as a measure of proportional hazard, is greatly influenced by blood serum biomarkers and superior vena cava flow, and displays the greatest accuracy (according to area under the curve analysis) for classifying Fontan patients. In bivariate hazard analysis, we determined that models combining lymphatic dysfunction (PC6) and systolic function (EF or PC5) were most accurate, with the former having the highest c-statistic, and the latter having the greatest AIC. Our findings support our hypothesis that improved prognostication in a Fontan population should utilize a multifactorial model.","journal":"Cardiovascular Science","year":2025,"id":552815,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9344,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1449572,"name":"Michal Sch鋐er","orcid":null,"position":1,"is_corresponding":false},{"id":1149134,"name":"Michael V. Di Maria","orcid":"0000-0003-0346-8517","position":2,"is_corresponding":false},{"id":350878,"name":"Kendall S. Hunter","orcid":"0000-0002-5162-8100","position":3,"is_corresponding":false},{"id":790865,"name":"Margaret R. Ferrari","orcid":"0000-0003-0062-4334","position":0,"is_corresponding":true}],"reference_count":22,"raw_metadata":null,"created_at":"2026-07-19T02:54:37.527189Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}