Mixed graphical models for the prediction of neurological morbidity in the PICU

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In the modern pediatric intensive care unit (PICU), as mortality rates continue to decline, focus has shifted towards measures to decrease neurological morbidity. Neurological complications can be difficult to detect in the PICU as children oftentimes receive sedation and/or neuromuscular blockade due to the severity of their illnesses. Early identification and implementation of evidence-based treatment strategies is paramount to the reduction of neurological morbidity. Traditional methods of neuro-monitoring (computed tomography [CT], magnetic resonance imaging [MRI], electroencephalography [EEG]) cannot be practically utilized for routine screening purposes. We believe that biomathematical models integrating biomarkers and clinical data may represent an important tool for the detection of neurological complications in the PICU. This strategy may allow for rapid identification of neurologic complications and earlier intervention to ultimately reduce morbidity and mortality. In this mentored patient-oriented research career development award we will attempt to develop mixed graphical models using a novel algorithm developed by the co-sponsor, MGM-Learn (Mixed Graphical Model Learning), which has the unique capability of processing continuous and discrete variables. Two hundred and twenty-eight diagnostically diverse children admitted to the PICU at Children's Hospital of Pittsburgh of UPMC will be enrolled. Serum biomarkers (myelin basic protein [MBP], S100B, brain derived neurotrophic factor [BDNF], and glial fibrillary acidic protein [GFAP]) that have shown promise in prognostication of outcome after neurological injuries such as traumatic brain injury or cardiac arrest will be used in conjunction with clinical and laboratory variables obtained from the electronic health record, through integrative analysis in mixed graphical models to predict acute development of neurological complications that were not present at the time of admission (e.g. seizure, stroke, hemorrhage, encephalopathy) and morbidity (e.g. Functional Status Scale (FSS), Pediatric Quality of Life Inventory (PedsQL)) at discharge and 6 months following critical illness. This K23 award will provide me with in-depth training in mixed graphical modeling, greatly enhance my skills in the clinical application of neuro-biomarkers and effective leadership and management to transition to a successful independent investigator. It will provide preliminary data for my R01, the implementation of an early warning neuro-biosensor system, through the use of mixed graphical models that continually populates with the most up-to-date biomarker and clinical data variables, into clinical practice to detect neurological complications at a moment to moment basis; and the assessment of its ability to reduce neurologic morbidity through early recognition of neurological complications and timely execution of treatment strategies to prevent irreversible brain damage.