Precision Medicine in the CICU: Identifying Proteomic Biomarkers

Plain-language summary

Congenital Heart disease (CHD) is a leading cause of childhood death. Substantial morbidity and mortality relates to the postoperative course. For example, only 70% of neonates survive to hospital discharge after their first complex surgery for single ventricle heart disease. Adverse systemic inflammatory responses are highly exaggerated in some children postoperatively. This inflammation is pathological, results in leaky blood vessels and fluid overload, toxin release as well as cell damage contributing to lung, heart and kidney injury. Reasons why some children develop this amplified systemic inflammatory response after heart surgery while others do not are poorly understood. Mechanisms for how cardiopulmonary bypass and surgery drive this inflammation are also inadequately characterized. Currently, there are no existing methods to predict patients at high-risk for acute adverse postoperative complications, let alone adjust our management to mitigate these effects. Instead, our postoperative care approach is a one-size fits all, reactive process 'after' patients become inflamed or adverse events occur. Proteins in a patient's blood participate in and reflect acute inflammatory responses. In other pediatric conditions, protein biomarkers have been shown to both predict and monitor inflammation and adverse outcomes, and importantly predict responsiveness to anti-inflammatory drug therapies. This is the premise of precision medicine. Personalizing treatment to each individual patient. New technologies now allow the levels of tens of thousands of proteins to be measured from a few drops of blood. In this proposal the investigators will identify predictors of adverse events after heart surgery by quantifying protein levels and their changes after surgery. It is now possible to detect those proteins with the greatest variability in the postoperative course over time, and between patients, as well as those that are associated with adverse outcomes. The most informative proteins will yield insights into the causes of the inflammatory response. The investigators anticipate identifying protein plasma biomarkers in pathways associated with inflammation, metabolism, blood vessel function and the immune system as these may be key mechanisms involved. Advanced understanding of these mechanisms is critical to deriving targeted therapies to prevent or mitigate inflammatory responses. The investigators will also collect patient clinical data, such as age, cardiac anatomy, and duration of surgery. By combining this clinical information with blood protein profiles, the investigators will be able to develop a model predicting patients at highest risk for adverse postoperative events using machine learning approaches. The overarching goal of this research integrating clinical and bench research is ultimately to translate precision medicine approaches to the Cardiac ICU. Guiding personalized care of high-risk patients by enabling clinicians to anticipate outcomes and tailor decision-making at the bedside will undoubtably improve outcomes in CHD.

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