Rapid prediction of secondary neurologic decline after traumatic brain injury: a data analytic approach.

Secondary neurologic decline (ND) after traumatic brain injury (TBI) is independently associated with outcome, but robust predictors of ND are lacking. In this retrospective analysis of consecutive isolated TBI admissions to the R. Adams Cowley Shock Trauma Center between November 2015 and June 2018, we aimed to develop a triage decision support tool to quantify risk for early ND. Three machine learning models based on clinical, physiologic, or combined characteristics from the first hour of hospital resuscitation were created. Among 905 TBI cases, 165 (18%) experienced one or more ND events (130 clinical, 51 neurosurgical, and 54 radiographic) within 48 h of presentation. In the prediction of ND, the clinical plus physiologic data model performed similarly to the physiologic only model, with concordance indices of 0.85 (0.824-0.877) and 0.84 (0.812-0.868), respectively. Both outperformed the clinical only model, which had a concordance index of 0.72 (0.688-0.759). This preliminary work suggests that a data-driven approach utilizing physiologic and basic clinical data from the first hour of resuscitation after TBI has the potential to serve as a decision support tool for clinicians seeking to identify patients at high or low risk for ND.

Dynamic Intracranial Pressure Waveform Morphology Predicts Ventriculitis.

BACKGROUND: Intracranial pressure waveform morphology reflects compliance, which can be decreased by ventriculitis. We investigated whether morphologic analysis of intracranial pressure dynamics predicts the onset of ventriculitis. METHODS: Ventriculitis was defined as culture or Gram stain positive cerebrospinal fluid, warranting treatment. We developed a pipeline to automatically isolate segments of intracranial pressure waveforms from extraventricular catheters, extract dominant pulses, and obtain morphologically similar groupings. We used a previously validated clinician-supervised active learning paradigm to identify metaclusters of triphasic, single-peak, or artifactual peaks. Metacluster distributions were concatenated with temperature and routine blood laboratory values to create feature vectors. A L2-regularized logistic regression classifier was trained to distinguish patients with ventriculitis from matched controls, and the discriminative performance using area under receiver operating characteristic curve with bootstrapping cross-validation was reported. RESULTS: Fifty-eight patients were included for analysis. Twenty-seven patients with ventriculitis from two centers were identified. Thirty-one patients with catheters but without ventriculitis were selected as matched controls based on age, sex, and primary diagnosis. There were 1590 h of segmented data, including 396,130 dominant pulses in patients with ventriculitis and 557,435 pulses in patients without ventriculitis. There were significant differences in metacluster distribution comparing before culture-positivity versus during culture-positivity (p < 0.001) and after culture-positivity (p < 0.001). The classifier demonstrated good discrimination with median area under receiver operating characteristic 0.70 (interquartile range 0.55-0.80). There were 1.5 true alerts (ventriculitis detected) for every false alert. CONCLUSIONS: Intracranial pressure waveform morphology analysis can classify ventriculitis without cerebrospinal fluid sampling.

Admission Features Associated With Paroxysmal Sympathetic Hyperactivity After Traumatic Brain Injury: A Case-Control Study.

OBJECTIVES: Paroxysmal sympathetic hyperactivity occurs in a subset of critically ill traumatic brain injury patients and has been associated with worse outcomes after traumatic brain injury. The goal of this study was to identify admission risk factors for the development of paroxysmal sympathetic hyperactivity in traumatic brain injury patients. DESIGN: Retrospective case-control study of age- and Glasgow Coma Scale-matched traumatic brain injury patients. SETTING: Neurotrauma ICU at the R. Adams Cowley Shock Trauma Center of the University of Maryland Medical System, January 2016 to July 2018. PATIENTS: Critically ill adult traumatic brain injury patients who underwent inpatient monitoring for at least 14 days were included. Cases were identified based on treatment for paroxysmal sympathetic hyperactivity with institutional first-line therapies and were confirmed by retrospective tabulation of established paroxysmal sympathetic hyperactivity diagnostic and severity criteria. Cases were matched 1:1 by age and Glasgow Coma Scale to nonparoxysmal sympathetic hyperactivity traumatic brain injury controls, yielding 77 patients in each group. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Admission characteristics independently predictive of paroxysmal sympathetic hyperactivity included male sex, higher admission systolic blood pressure, and initial CT evidence of diffuse axonal injury, intraventricular hemorrhage/subarachnoid hemorrhage, complete cisternal effacement, and absence of contusion. Paroxysmal sympathetic hyperactivity cases demonstrated significantly worse neurologic outcomes upon hospital discharge despite being matched for injury severity at admission. CONCLUSIONS: Several anatomical, epidemiologic, and physiologic risk factors for clinically relevant paroxysmal sympathetic hyperactivity can be identified on ICU admission. These features help characterize paroxysmal sympathetic hyperactivity as a clinical-pathophysiologic phenotype associated with worse outcomes after traumatic brain injury.