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Metabolic Profiling of Children Undergoing Surgery for Congenital Heart Disease.
OBJECTIVE: Inflammation and metabolism are closely interlinked. Both undergo significant dysregulation following surgery for congenital heart disease, contributing to organ failure and morbidity. In this study, we combined cytokine and metabolic profiling to examine the effect of postoperative tight glycemic control compared with conventional blood glucose management on metabolic and inflammatory outcomes in children undergoing congenital heart surgery. The aim was to evaluate changes in key metabolites following congenital heart surgery and to examine the potential of metabolic profiling for stratifying patients in terms of expected clinical outcomes. DESIGN: Laboratory and clinical study. SETTING: University Hospital and Laboratory. PATIENTS: Of 28 children undergoing surgery for congenital heart disease, 15 underwent tight glycemic control postoperatively and 13 were treated conventionally. INTERVENTIONS: Metabolic profiling of blood plasma was undertaken using proton nuclear magnetic resonance spectroscopy. A panel of metabolites was measured using a curve-fitting algorithm. Inflammatory cytokines were measured by enzyme-linked immunosorbent assay. The data were assessed with respect to clinical markers of disease severity (Risk Adjusted Congenital heart surgery score-1, Pediatric Logistic Organ Dysfunction, inotrope score, duration of ventilation and pediatric ICU-free days). MEASUREMENTS AND MAIN RESULTS: Changes in metabolic and inflammatory profiles were seen over the time course from surgery to recovery, compared with the preoperative state. Tight glycemic control did not significantly alter the response profile. We identified eight metabolites (3-D-hydroxybutyrate, acetone, acetoacetate, citrate, lactate, creatine, creatinine, and alanine) associated with surgical and disease severity. The strength of proinflammatory response, particularly interleukin-8 and interleukin-6 concentrations, inversely correlated with PICU-free days at 28 days. The interleukin-6/interleukin-10 ratio directly correlated with plasma lactate. CONCLUSIONS: This is the first report on the metabolic response to cardiac surgery in children. Using nuclear magnetic resonance to monitor the patient journey, we identified metabolites whose concentrations and trajectory appeared to be associated with clinical outcome. Metabolic profiling could be useful for patient stratification and directing investigations of clinical interventions.Mr. Correia is supported by the Imperial College Stratified Medicine Graduate Training Programme in Systems Medicine and Spectroscopic Profiling (STRATiGRAD). Dr. Pathan’s institution received grant support from the British Heart Foundation (research grant) and a Higher Education Funding Council for England clinical senior lecturer award. Dr. Ng received grant support from the British Heart Foundation (Researcher salary) and received support for article research from the British Heart Foundation. Dr. Jimenez consulted for Metabometrix is employed by the Imperial College London, and received support for article research from the National Institutes of Health (NIH). Her institution received grant support from the Cardiovascular Biomedical Research Institute. Dr. Macrae is employed by Royal Brompton and Harefield NHS Foundation Trust. Dr. Holmes is employed by the Imperial College London and received support for article research. Her institution received grant support from the Imperial College London (unrelated research grants in the field of metabolic medicine). The remaining authors have disclosed that they do not have any potential conflicts of interest.This is the final published version. It first appeared at http://journals.lww.com/ccmjournal/pages/articleviewer.aspx?year=9000&issue=00000&article=97295&type=abstract
NEUROlogical Prognosis After Cardiac Arrest in Kids (NEUROPACK) study: protocol for a prospective multicentre clinical prediction model derivation and validation study in children after cardiac arrest
Introduction Currently, we are unable to accurately predict mortality or neurological morbidity following resuscitation after paediatric out of hospital (OHCA) or in-hospital (IHCA) cardiac arrest. A clinical prediction model may improve communication with parents and families and risk stratification of patients for appropriate postcardiac arrest care. This study aims to the derive and validate a clinical prediction model to predict, within 1 hour of admission to the paediatric intensive care unit (PICU), neurodevelopmental outcome at 3 months after paediatric cardiac arrest.
Methods and analysis A prospective study of children (age: >24 hours and <16 years), admitted to 1 of the 24 participating PICUs in the UK and Ireland, following an OHCA or IHCA. Patients are included if requiring more than 1 min of cardiopulmonary resuscitation and mechanical ventilation at PICU admission Children who had cardiac arrests in PICU or neonatal intensive care unit will be excluded. Candidate variables will be identified from data submitted to the Paediatric Intensive Care Audit Network registry. Primary outcome is neurodevelopmental status, assessed at 3 months by telephone interview using the Vineland Adaptive Behavioural Score II questionnaire. A clinical prediction model will be derived using logistic regression with model performance and accuracy assessment. External validation will be performed using the Therapeutic Hypothermia After Paediatric Cardiac Arrest trial dataset. We aim to identify 370 patients, with successful consent and follow-up of 150 patients. Patient inclusion started 1 January 2018 and inclusion will continue over 18 months.
Ethics and dissemination Ethical review of this protocol was completed by 27 September 2017 at the Wales Research Ethics Committee 5, 17/WA/0306. The results of this study will be published in peer-reviewed journals and presented in conferences.
Trial registration number NCT03574025