Intracranial pressure in traumatic brain injury is dynamic and influenced by factors like injury patterns, treatments, and genetics. Existing studies use time invariant summary intracranial pressure measures thus potentially losing critical information about temporal trends. We identified longitudinal intracranial pressure trajectories in severe traumatic brain injury and evaluated whether they predicted outcome. We further interrogated the model to explore whether ABCC8 polymorphisms (a known cerebraledema regulator) differed across trajectory groups.
Prospective observational cohort.
Single-center academic medical center.
Four-hundred four severe traumatic brain injury patients.
We used group-based trajectory modeling to identify hourly intracranial pressure trajectories in days 0–5 post traumatic brain injury incorporating risk factor adjustment (age, sex, Glasgow Coma Scale 6score, craniectomy, primary hemorrhage pattern). We compared 6-month outcomes (Glasgow Outcome Scale, Disability Rating Scale, mortality) and ABCC8 tag-single-nucleotide polymorphisms associated with cerebral edema (rs2237982, rs7105832) across groups. Regression models determined whether trajectory groups predicted outcome. A six trajectory group model best fit the data, identifying cohorts differing in initial intracranial pressure, evolution, and number/proportion of spikes greater than 20 mm Hg. There were pattern differences in age, hemorrhage type, and craniectomy rates. ABCC8 polymorphisms differed across groups. GOS (p = 0.006), Disability Rating Scale (p = 0.001), mortality (p < 0.0001), and rs2237982 (p = 0.035) differed across groups. Unfavorable outcomes were surprisingly predicted by both low intracranial pressure trajectories and sustained intracranial hypertension. Intracranial pressure variability differed across groups (p < 0.001) and may reflect preserved/impaired intracranial elastance/compliance.
We employed a novel approach investigating longitudinal/dynamic intracranial pressure patterns in traumatic brain injury. In a risk adjusted model, six groups were identified and predicted outcomes. If validated, trajectory modeling may be a first step toward developing a new, granular approach for intracranial pressure phenotyping in conjunction with other phenotyping tools like biomarkers and neuroimaging. This may be particularly relevant in light of changing traumatic brain injury demographics toward the elderly.
1Department of Critical Care Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA.
2Department of Neurology, School of Medicine, University of Pittsburgh, Pittsburgh, PA.
3Department of Neurosurgery, School of Medicine, University of Pittsburgh, Pittsburgh, PA.
4Safar Center for Resuscitation Research, School of Medicine, University of Pittsburgh, Pittsburgh, PA.
5Clinical and Translational Science Institute, School of Medicine, University of Pittsburgh, Pittsburgh, PA.
6Department of Emergency Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA.
7Department of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA.
8Department of Biostatistics, School of Public Health, University of Pittsburgh, Pittsburgh, PA.
9School of Nursing, University of Pittsburgh, Pittsburgh, PA.
10Department of Human Genetics, School of Medicine, University of Pittsburgh, Pittsburgh, PA.
11Department of Anesthesia, School of Medicine, University of Pittsburgh, Pittsburgh, PA.
*See also p. 1876.
Drs. Zusman and Desai are joint third authors since they contributed equally to the final article.
Dr. Jha was involved in study concept, design, data analysis, and interpretation and article generation. Dr. Elmer was involved in data and statistical analysis, group-based trajectory modeling and interpretation, and critical revision of the article. Drs. Zusman, Desai, Puccio, and Okonkwo were involved in acquisition of data and patient samples. Dr. Park was involved in statistical analysis and review. Drs. Shutter and Wallisch were involved in content expertise and critical revision of the article. Dr. Conley was involved in genetics content expertise, supervision, and critical review of the article. Drs. Kochanek was involved in study concept, design, data interpretation, supervision, and critical revision of the article.
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Supported, in part, by the following grants: The National Institutes of Health (NIH) (NINDS) K23NS101036 (to Dr. Jha), NIH (NINDS) 1K23NS097629 (to Dr. Elmer) NINR R01NR013342 (to Dr. Conley), NINR R00 NR013176 (to Dr. Puccio), NCATS KL2-TR000146 (to Dr. Jha), NCATS KL2-TR001856 (to Dr. Jha), UPP Foundation award (to Dr. Jha), University of Pittsburgh School of Medicine Dean’s Faculty Advancement Award (to Dr. Jha).
Dr. Jha’s institution received funding from the National Institutes of Health (NIH) grants K23NS101036 (National Institute of Neurological Disorders and Stroke [NINDS]), KL2-TR001856 (National Center for Advancing Translational Science [NCATS]), KL2-TR000146 (NCATS). Drs. Jha, Elmer, Park, Wallisch, Conley, and Kochanek received support for article research from the NIH. Dr. Elmer’s institution received funding from NIH 1K23NS097629 (NINDS). Dr. Puccio received funding from NIH grant R00 NR013176 (National Institute of Nursing Research [NINR]). Dr. Conley’s institution received funding from NIH grant R01NR013342 (NINR). Dr. Kochanek’s institution received funding from the NIH and the Department of Defense, and he received funding from the Society of Critical Care Medicine/World Federation of Pediatric Intensive Critical Care Societies as Editor-in-Chief of Pediatric Critical Care Medicine, numerous lectures as a guest professor at a number of academic institutions, from serving as an expert witness on several cases relevant to pediatric neurocritical care, and as one of the editors of the Textbook of Critical Care Medicine (honorarium). The remaining authors have disclosed that they do not have any potential conflicts of interest.
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