Abstract
Background Clinical risk factors in neonatal cardiac surgery do not fully capture discrepancies in outcomes. Targeted metabolomic analysis of plasma from neonates undergoing heart surgery with cardiopulmonary bypass was performed to determine associations with clinical outcomes. Methods and Result Samples and clinical variables from 149 neonates enrolled in the Corticosteroid Therapy in Neonates Undergoing Cardiopulmonary Bypass trial with surgical treatment for congenital heart disease between 2012 and 2016 were included. Blood samples were collected before skin incision, immediately after cardiopulmonary bypass, and 12 hours after surgery. Outcomes include composite morbidity/mortality (death, extracorporeal membrane oxygenation, cardiac arrest, acute kidney injury, and/or hepatic injury) and a cardiac composite (extracorporeal membrane oxygenation, cardiac arrest, or increase in lactate level), hepatic injury, and acute kidney injury. Targeted metabolite levels were determined by high-resolution tandem liquid chromatography and mass spectrometry. Principal component and regression analyses were used to assess associations between metabolic profiles and outcomes, with 2 models created: a base clinical model and a base model+metabolites. Of the 193 metabolites examined, 40 were detected and quantified. The first principal component, principal component 1, was composed mostly of preoperative metabolites and was significantly associated with the composite morbidity/mortality, cardiac composite, and hepatic injury outcomes. In regression models, individual metabolites also improved model performance for the composite morbidity/mortality, cardiac composite, and hepatic injury outcomes. Significant disease pathways included myocardial injury (false discovery rate, 0.00091) and heart failure (false discovery rate, 0.041). Conclusions In neonatal cardiac surgery, perioperative metabolites were associated with postoperative outcomes and improved clinical model outcome associations. Preoperative metabolite levels alone may improve risk models and provide a basis for optimizing perioperative care.