Identifying Risk Factors for Complicated Post-operative Course in Tetralogy of Fallot Using a Machine Learning Approach - Faerber, Jennifer A.; Huang, Jing; Zhang, Xuemei; Song, Lihai; DeCost, Grace; Mascio, Christopher E.; Ravishankar, Chitra; O'Byrne, Michael L.; Naim, Maryam Y.; Kawut, Steven M.; Goldmuntz, Elizabeth; Mercer-Rosa, Laura (Frontiers in Cardiovascular Medicine, 2021)
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 nonparametric 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 03/2012 and 05/2018, 43 (26.5%) had at least one post-operative cardiac complication. The most frequent complications were arrhythmia requiring treatment (N=22, 13.6%), cardiac catherization (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 global right ventricular (RV) 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 peri-operative 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 towards optimizing pre-operative ventricular remodeling might mitigate post-operative complications and help reduce future morbidity.