Improving the accuracy of outcome predictions for pediatric neurologic conditions
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Approximately 20% of pediatric intensive care unit (PICU) admissions are secondary to neurologic conditions. Children’s Wisconsin (CW) has the largest PICU in the state and serves this subset of children through the pediatric neurocritical care program (PNCC). CW is a major referral center for PNCC conditions such as traumatic brain injury, anoxic brain injury, neuroinflammatory disorders, and more. The PNCC service cares for approximately 400 patients yearly and follows them in CW's outpatient clinic. In the acute period of a PNCC illness, neurologic outcome prediction is often necessary, especially when the physical exam is obscured by sedatives. Because neurologic outcome prediction may help clinicians and families make life altering decisions, accuracy is of utmost importance. Despite this, there is often a high level of uncertainty and risk for overly pessimistic or optimistic predictions which may prompt Wisconsin families to make decisions based on limited data. The PNCC group published a study documenting prognostic accuracy for traumatic brain injury patients of only 75%.
There is a critical need for diagnostic tools that improve the accuracy of neurologic outcome prediction. A condition termed cognitive motor dissociation (CMD) has been recently recognized and reported almost solely in adult neuro-critically ill patients. CMD refers to patients who appear to be unconscious but have cortical activity present on neuromonitoring tools such as electroencephalogram, functional magnetic resonance, or functional near infrared spectroscopy (fNIRS). It is critical to diagnose CMD because patients with CMD may have better outcome trajectories. fNIRS is quickly becoming an attractive tool to diagnose CMD due to its portability, low risk, low cost, and non-invasiveness. fNIRS and CMD are understudied in pediatrics, particularly in PNCC. This project aims to improve neurologic outcome prediction accuracy for PNCC children of Wisconsin by detecting CMD through use of fNIRS
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