Systemic-to-Pulmonary Artery (SPA) shunt surgery, one of the most common cardiac surgical procedures in the newborn period, provides a means to palliate children with limited pulmonary blood flow, such as in Tetralogy of Fallot. Despite the simplicity of the procedure, it is associated with significant morbidity (such as need for extracorporeal membrane oxygenation (ECMO), and long post-operative length of stay (PLOS) in the hospital following surgery) and mortality. These outcomes are known to be impacted by a number of complex factors (including patient specific and procedure specific factors, perioperative related factors, etc.), whose relative importance in clinical decision making remains the domain of clinical judgment. The increasing availability of multi-modal data on patient care and outcomes opens up the opportunity to assess clinical practices from a more data-driven perspective. In this paper, we report results from a study of 1036 patients (from 44 children's hospitals across the US) during 2009-2014 that applies a machine learning approach to predicting post-operative outcomes for patients in the Pediatric Health Information System (PHIS) database. We demonstrate that it is feasible to achieve significant prediction benefits using a standard machine learning approach (random forests) on a carefully constructed dataset, showing the value of applying machine learning even with noisy administrative databases. The methods we describe can be used to identify potential important variables that lead to good clinical judgment as defined by desirable clinical outcomes.