{"doi":"10.3389/fcvm.2021.685855","title":"Identifying Risk Factors for Complicated Post-operative Course in Tetralogy of Fallot Using a Machine Learning Approach","abstract":"Introduction: Tetralogy of Fallot (TOF) repair is associated with excellent operative survival. However, a subset of patients experiences post-operative complications, which can significantly alter the early and late post-operative course. We utilized a machine learning approach to identify risk factors for post-operative complications after TOF repair. Methods: We conducted a single-center prospective cohort study of children <2 years of age with TOF undergoing surgical repair. The outcome was occurrence of post-operative cardiac complications, measured between TOF repair and hospital discharge or death. Predictors included patient, operative, and echocardiographic variables, including pre-operative right ventricular strain and fractional area change as measures of right ventricular function. Gradient-boosted quantile regression models (GBM) determined predictors of post-operative complications. Cross-validated GBMs were implemented with and without a filtering stage non-parametric regression model to select a subset of clinically meaningful predictors. Sensitivity analysis with gradient-boosted Poisson regression models was used to examine if the same predictors were identified in the subset of patients with at least one complication. Results: Of the 162 subjects enrolled between March 2012 and May 2018, 43 (26.5%) had at least one post-operative cardiac complication. The most frequent complications were arrhythmia requiring treatment ( N = 22, 13.6%), cardiac catheterization ( N = 17, 10.5%), and extracorporeal membrane oxygenation (ECMO) ( N = 11, 6.8%). Fifty-six variables were used in the machine learning analysis, of which there were 21 predictors that were already identified from the first-stage regression. Duration of cardiopulmonary bypass (CPB) was the highest ranked predictor in all models. Other predictors included gestational age, pre-operative right ventricular (RV) global longitudinal strain, pulmonary valve Z-score, and immediate post-operative arterial oxygen level. Sensitivity analysis identified similar predictors, confirming the robustness of these findings across models. Conclusions: Cardiac complications after TOF repair are prevalent in a quarter of patients. A prolonged surgery remains an important predictor of post-operative complications; however, other perioperative factors are likewise important, including pre-operative right ventricular remodeling. This study identifies potential opportunities to optimize the surgical repair for TOF to diminish post-operative complications and secure improved clinical outcomes. Efforts toward optimizing pre-operative ventricular remodeling might mitigate post-operative complications and help reduce future morbidity.","journal":"Frontiers in Cardiovascular Medicine","year":2021,"id":187915,"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":17,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.953,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":390519,"name":"Jing Huang","orcid":"0000-0002-6133-6988","position":1,"is_corresponding":false},{"id":738695,"name":"Xuemei Zhang","orcid":"0000-0003-0705-4231","position":2,"is_corresponding":false},{"id":504847,"name":"Lihai Song","orcid":"0000-0002-2296-5570","position":3,"is_corresponding":false},{"id":695428,"name":"Grace DeCost","orcid":"0000-0001-8535-5254","position":4,"is_corresponding":false},{"id":365861,"name":"Christopher E. Mascio","orcid":null,"position":5,"is_corresponding":false},{"id":695429,"name":"Chitra Ravishankar","orcid":"0000-0002-5825-3778","position":6,"is_corresponding":false},{"id":166591,"name":"Michael L. O'Byrne","orcid":"0000-0001-6023-1634","position":7,"is_corresponding":false},{"id":541169,"name":"Maryam Y. Naim","orcid":"0000-0002-9127-0043","position":8,"is_corresponding":false},{"id":329835,"name":"Steven M. Kawut","orcid":"0000-0001-7896-0608","position":9,"is_corresponding":false},{"id":54918,"name":"Elizabeth Goldmuntz","orcid":"0000-0003-2936-4396","position":10,"is_corresponding":false},{"id":403439,"name":"Laura Mercer‐Rosa","orcid":null,"position":11,"is_corresponding":false},{"id":365860,"name":"Jennifer Faerber","orcid":null,"position":0,"is_corresponding":true}],"reference_count":40,"raw_metadata":null,"created_at":"2026-07-18T23:49:05.976005Z","pmid":"34368247","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":[]}