Group-Based Trajectory Modeling Identifies Distinct Patterns of Sympathetic Hyperactivity Following Traumatic Brain Injury.

backgroundParoxysmal sympathetic hyperactivity (PSH) occurs with high prevalence among critically ill patients with traumatic brain injury (TBI) and is associated with worse outcomes. The PSH-Assessment Measure (PSH-AM) consists of a Clinical Features Scale and a diagnosis likelihood tool (DLT) intended to quantify the severity of sympathetically mediated symptoms and the likelihood that they are due to PSH, respectively, on a daily basis. Here, we aim to identify and explore the value of dynamic trends in the evolution of sympathetic hyperactivity following acute TBI using elements of the PSH-AM.

methodsWe performed an observational cohort study of 221 acute critically ill patients with TBI for whom PSH-AM scores were calculated over the first 14 days of hospitalization. A principled group-based trajectory modeling approach using unsupervised K-means clustering was used to identify distinct patterns of Clinical Features Scale evolution within the cohort. We also evaluated the relationships between trajectory group membership and PSH diagnosis, as well as PSH DLT score, hospital discharge Glasgow Coma Scale, intensive care unit and hospital length of stay, duration of mechanical ventilation, and mortality. Baseline clinical and demographic features predictive of trajectory group membership were analyzed using univariate screening and multivariate multinomial logistic regression.

resultsWe identified four distinct trajectory groups. Trajectory group membership was significantly associated with clinical outcomes including PSH diagnosis and DLT score, intensive care unit length of stay, and duration of mechanical ventilation. Baseline features independently predictive of trajectory group membership included age and postresuscitation motor Glasgow Coma Scale.

conclusionsThis study adds to the sparse research characterizing the heterogeneous temporal trends of sympathetic nervous system activation during the acute phase following TBI. This may open avenues for early identification of at-risk patients to receive tailored interventions to limit secondary brain injury associated with autonomic dysfunction and thereby improve outcomes in patients with TBI.