AUTHOR=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 

TITLE=Identifying Risk Factors for Complicated Post-operative Course in Tetralogy of Fallot Using a Machine Learning Approach

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=8

YEAR=2021

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2021.685855

DOI=10.3389/fcvm.2021.685855

ISSN=2297-055X

ABSTRACT=<p><bold>Introduction:</bold> 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.</p><p><bold>Methods:</bold> We conducted a single-center prospective cohort study of children &lt;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.</p><p><bold>Results:</bold> 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 (<italic>N</italic> = 22, 13.6%), cardiac catheterization (<italic>N</italic> = 17, 10.5%), and extracorporeal membrane oxygenation (ECMO) (<italic>N</italic> = 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.</p><p><bold>Conclusions:</bold> 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.</p>