scholarly journals Modeling Brain–Heart Crosstalk Information in Patients with Traumatic Brain Injury

2021 ◽  
Author(s):  
Giovanna Maria Dimitri ◽  
Erta Beqiri ◽  
Michal M. Placek ◽  
Marek Czosnyka ◽  
Nino Stocchetti ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Prediction Models ◽  
Age Groups ◽  
Negative Relationship ◽  
Inverse Correlation ◽  
Simultaneous Increase ◽  
Correlation Technique ◽  
Original Cohort ◽  
Data Set

Abstract Background Traumatic brain injury (TBI) is an extremely heterogeneous and complex pathology that requires the integration of different physiological measurements for the optimal understanding and clinical management of patients. Information derived from intracranial pressure (ICP) monitoring can be coupled with information obtained from heart rate (HR) monitoring to assess the interplay between brain and heart. The goal of our study is to investigate events of simultaneous increases in HR and ICP and their relationship with patient mortality.. Methods In our previous work, we introduced a novel measure of brain–heart interaction termed brain–heart crosstalks (ctnp), as well as two additional brain–heart crosstalks indicators [mutual information ($$mi_{ct}$$ m i ct ) and average edge overlap (ωct)] obtained through a complex network modeling of the brain–heart system. These measures are based on identification of simultaneous increase of HR and ICP. In this article, we investigated the relationship of these novel indicators with respect to mortality in a multicenter TBI cohort, as part of the Collaborative European Neurotrauma Effectiveness Research in TBI high-resolution work package. Results A total of 226 patients with TBI were included in this cohort. The data set included monitored parameters (ICP and HR), as well as laboratory, demographics, and clinical information. The number of detected brain–heart crosstalks varied (mean 58, standard deviation 57). The Kruskal–Wallis test comparing brain–heart crosstalks measures of survivors and nonsurvivors showed statistically significant differences between the two distributions (p values: 0.02 for $$mi_{ct}$$ m i ct , 0.005 for ctnp and 0.006 for ωct). An inverse correlation was found, computed using the point biserial correlation technique, between the three new measures and mortality: − 0.13 for ctnp (p value 0.04), − 0.19 for ωct (p value 0.002969) and − 0.09 for $$mi_{ct}$$ m i ct (p value 0.1396). The measures were then introduced into the logistic regression framework, along with a set of input predictors made of clinical, demographic, computed tomography (CT), and lab variables. The prediction models were obtained by dividing the original cohort into four age groups (16–29, 30–49, 50–65, and 65–85 years of age) to properly treat with the age confounding factor. The best performing models were for age groups 16–29, 50–65, and 65–85, with the deviance of ratio explaining more than 80% in all the three cases. The presence of an inverse relationship between brain–heart crosstalks and mortality was also confirmed. Conclusions The presence of a negative relationship between mortality and brain–heart crosstalks indicators suggests that a healthy brain–cardiovascular interaction plays a role in TBI.

Selection of children with ultra-severe traumatic brain injury for neurosurgical intervention

2019 ◽  
Vol 23 (6) ◽  
pp. 670-679
Author(s):  
Krista Greenan ◽  
Sandra L. Taylor ◽  
Daniel Fulkerson ◽  
Kiarash Shahlaie ◽  
Clayton Gerndt ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Decision Tree ◽  
Prediction Model ◽  
Severe Traumatic Brain Injury ◽  
Pupillary Response ◽  
Data Set ◽  
Robust Model ◽  
Gcs Score ◽  
Combined Data

OBJECTIVEA recent retrospective study of severe traumatic brain injury (TBI) in pediatric patients showed similar outcomes in those with a Glasgow Coma Scale (GCS) score of 3 and those with a score of 4 and reported a favorable long-term outcome in 11.9% of patients. Using decision tree analysis, authors of that study provided criteria to identify patients with a potentially favorable outcome. The authors of the present study sought to validate the previously described decision tree and further inform understanding of the outcomes of children with a GCS score 3 or 4 by using data from multiple institutions and machine learning methods to identify important predictors of outcome.METHODSClinical, radiographic, and outcome data on pediatric TBI patients (age < 18 years) were prospectively collected as part of an institutional TBI registry. Patients with a GCS score of 3 or 4 were selected, and the previously published prediction model was evaluated using this data set. Next, a combined data set that included data from two institutions was used to create a new, more statistically robust model using binomial recursive partitioning to create a decision tree.RESULTSForty-five patients from the institutional TBI registry were included in the present study, as were 67 patients from the previously published data set, for a total of 112 patients in the combined analysis. The previously published prediction model for survival was externally validated and performed only modestly (AUC 0.68, 95% CI 0.47, 0.89). In the combined data set, pupillary response and age were the only predictors retained in the decision tree. Ninety-six percent of patients with bilaterally nonreactive pupils had a poor outcome. If the pupillary response was normal in at least one eye, the outcome subsequently depended on age: 72% of children between 5 months and 6 years old had a favorable outcome, whereas 100% of children younger than 5 months old and 77% of those older than 6 years had poor outcomes. The overall accuracy of the combined prediction model was 90.2% with a sensitivity of 68.4% and specificity of 93.6%.CONCLUSIONSA previously published survival model for severe TBI in children with a low GCS score was externally validated. With a larger data set, however, a simplified and more robust model was developed, and the variables most predictive of outcome were age and pupillary response.

Intracranial Pressure Waveform Morphology and Intracranial Adaptive Capacity

2008 ◽  
Vol 17 (6) ◽  
pp. 545-554 ◽  
Author(s):  
Jun-Yu Fan ◽  
Catherine Kirkness ◽  
Paolo Vicini ◽  
Robert Burr ◽  
Pamela Mitchell
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Intracranial Pressure ◽  
Adaptive Capacity ◽  
Severe Traumatic Brain Injury ◽  
Controlled Trial ◽  
Pressure Monitor ◽  
Data Set ◽  
Prime Concern ◽  
Intracranial Pressure Waveform

Background Intracranial hypertension due to primary and secondary injuries is a prime concern when providing care to patients with severe traumatic brain injury. Increases in intracranial pressure vary depending on compensatory processes within the craniospinal space, also referred to as intracranial adaptive capacity. In patients with traumatic brain injury and decreased intracranial adaptive capacity, intracranial pressure increases disproportionately in response to a variety of stimuli. However, no well-validated measures are available in clinical practice to predict the development of such an increase. Objectives To examine whether P2 elevation, quantified by determining the P2:P1 ratio (=0.8) of the intracranial pressure pulse waveform, is a unique predictor of disproportionate increases in intracranial pressure on a beat-by-beat basis in the 30 minutes preceding the elevation in patients with severe traumatic brain injury, within 48 hours after deployment of an intracranial pressure monitor. Methods A total of 38 patients with severe traumatic brain injury were sampled from a randomized controlled trial of cerebral perfusion pressure management in patients with traumatic brain injury or subarachnoid hemorrhage. Results The P2 elevation was not only present before the disproportionate increase in pressure, but also appeared in the comparison data set (within-subject without such a pressure increase). Conclusions P2 elevation is not a reliable clinical indicator to predict an impending disproportionate increase in intracranial pressure.

scholarly journals Traumatic brain injury—the effects of patient age on treatment intensity and mortality

BMC Neurology ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Ola Skaansar ◽  
Cathrine Tverdal ◽  
Pål Andre Rønning ◽  
Karoline Skogen ◽  
Tor Brommeland ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Head Injury ◽  
Injury Severity ◽  
Age Groups ◽  
Composite Score ◽  
Increased Risk ◽  
Management Intensity ◽  
Treatment Bias ◽  
Head Injury Severity

Abstract Background Ageing is associated with worse treatment outcome after traumatic brain injury (TBI). This association may lead to a self-fulfilling prophecy that affects treatment efficacy. The aim of the current study was to evaluate the role of treatment bias in patient outcomes by studying the intensity of diagnostic procedures, treatment, and overall 30-day mortality in different age groups of patients with TBI. Methods Included in this study was consecutively admitted patients with TBI, aged ≥ 15 years, with a cerebral CT showing intracranial signs of trauma, during the time-period between 2015–2018. Data were extracted from our prospective quality control registry for admitted TBI patients. As a measure of management intensity in different age groups, we made a composite score, where placement of intracranial pressure monitor, ventilator treatment, and evacuation of intracranial mass lesion each gave one point. Uni- and multivariate survival analyses were performed using logistic multinomial regression. Results A total of 1,571 patients with TBI fulfilled the inclusion criteria. The median age was 58 years (range 15–98), 70% were men, and 39% were ≥ 65 years. Head injury severity was mild in 706 patients (45%), moderate in 437 (28%), and severe in 428 (27%). Increasing age was associated with less management intensity, as measured using the composite score, irrespective of head injury severity. Multivariate analyses showed that the following parameters had a significant association with an increased risk of death within 30 days of trauma: increasing age, severe comorbidities, severe TBI, Rotterdam CT-score ≥ 3, and low management intensity. Conclusion The present study indicates that the management intensity of hospitalised patients with TBI decreased with advanced age and that low management intensity was associated with an increased risk of 30-day mortality. This suggests that the high mortality among elderly TBI patients may have an element of treatment bias and could in the future be limited with a more aggressive management regime.

scholarly journals Simulation of Blast-Induced Early-Time Intracranial Wave Physics leading to Traumatic Brain Injury

2009 ◽  
Vol 131 (6) ◽  
Author(s):  
Paul A. Taylor ◽  
Corey C. Ford
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Time Course ◽  
Early Time ◽  
Brain Regions ◽  
Head Model ◽  
Maximum Pressure ◽  
Wave Interactions ◽  
Data Set ◽  
Blast Exposure

The objective of this modeling and simulation study was to establish the role of stress wave interactions in the genesis of traumatic brain injury (TBI) from exposure to explosive blast. A high resolution (1 mm3 voxels) five material model of the human head was created by segmentation of color cryosections from the Visible Human Female data set. Tissue material properties were assigned from literature values. The model was inserted into the shock physics wave code, CTH, and subjected to a simulated blast wave of 1.3 MPa (13 bars) peak pressure from anterior, posterior, and lateral directions. Three-dimensional plots of maximum pressure, volumetric tension, and deviatoric (shear) stress demonstrated significant differences related to the incident blast geometry. In particular, the calculations revealed focal brain regions of elevated pressure and deviatoric stress within the first 2 ms of blast exposure. Calculated maximum levels of 15 KPa deviatoric, 3.3 MPa pressure, and 0.8 MPa volumetric tension were observed before the onset of significant head accelerations. Over a 2 ms time course, the head model moved only 1 mm in response to the blast loading. Doubling the blast strength changed the resulting intracranial stress magnitudes but not their distribution. We conclude that stress localization, due to early-time wave interactions, may contribute to the development of multifocal axonal injury underlying TBI. We propose that a contribution to traumatic brain injury from blast exposure, and most likely blunt impact, can occur on a time scale shorter than previous model predictions and before the onset of linear or rotational accelerations traditionally associated with the development of TBI.

scholarly journals Hospitalizations and Emergency Department Visits for TBI in Ontario

2010 ◽  
Vol 37 (6) ◽  
pp. 783-790 ◽  
Author(s):  
Angela Colantonio ◽  
Cristina Saverino ◽  
Brandon Zagorski ◽  
Bonnie Swaine ◽  
John Lewko ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Emergency Department ◽  
Brain Injury ◽  
Age Groups ◽  
National Registry ◽  
Emergency Department Visits ◽  
Elderly Persons ◽  
Discharge Abstract Database ◽  
Episodes Of Care ◽  
Ed Visits

AbstractObjective:The aim of this study was to determine the number of annual hospitalizations and overall episodes of care that involve a traumatic brain injury (TBI) by age and gender in the province of Ontario. To provide a more accurate assessment of the prevalence of TBI, episodes of care included visits to the emergency department (ED), as well as admissions to hospital. Mechanisms of injury for overall episodes were also investigated.Methods:Traumatic brain injury cases from fiscal years 2002/03-2006/07 were identified by means of ICD-10 codes. Data were collected from the National Ambulatory Care Reporting System and the Discharge Abstract Database.Results:The rate of hospitalization was highest for elderly persons over 75 years-of-age. Males generally had higher rates for both hospitalizations and episodes of care than did females. The inclusion of ED visits to hospitalizations had the greatest impact on the rates of TBI in the youngest age groups. Episodes of care for TBI were greatest in youth under the age of 14 and elderly over the age of 85. Falls (41.6%) and being struck by or against an object (31.1%) were the most frequent causes for a TBI.Conclusions:The study provides estimates for TBI from the only Canadian province that has systematically captured ED visits in a national registry. It shows the importance of tracking ED visits, in addition to hospitalizations, to capture the burden of TBI on the health care system. Prevention strategies should include information on ED visits, particularly for those at younger ages.

scholarly journals Traumatic Brain Injury Related Hospitalization and Mortality in California

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Clint Lagbas ◽  
Shahrzad Bazargan-Hejazi ◽  
Magda Shaheen ◽  
Dulcie Kermah ◽  
Deyu Pan
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Motor Vehicle ◽  
Motor Vehicle Accident ◽  
Age Groups ◽  
Motor Vehicle Accidents ◽  
Elderly Male ◽  
Cross Sectional ◽  
Discharge Data ◽  
Vehicle Accidents

Objective. The aim of this study is to describe the traumatic brain injury (TBI) population and causes and identify factors associated with TBI hospitalizations and mortality in California.Methods. This is a cross-sectional study of 61,188 patients with TBI from the California Hospital Discharge Data 2001 to 2009. We used descriptive, bivariate, and multivariate analyses in SAS version 9.3.Results. TBI-related hospitalizations decreased by 14% and mortality increased by 19% from 2001 to 2009. The highest percentages of TBI hospitalizations were due to other causes (38.4%), falls (31.2%), being of age≥75years old (37.2%), being a males (58.9%), and being of Medicare patients (44%). TBIs due to falls were found in those age≤4years old (53.5%),≥75years old (44.0%), and females (37.2%). TBIs due to assaults were more frequent in Blacks (29.0%). TBIs due to motor vehicle accidents were more frequent in 15–19 and 20–24 age groups (48.7% and 48.6%, resp.) and among Hispanics (27.8%). Higher odds of mortality were found among motor vehicle accident category (adjusted odds ratio (AOR): 1.27, 95% CI: 1.14–1.41); males (AOR: 1.36, 95% CI: 1.27–1.46); and the≥75-year-old group (AOR: 6.4, 95% CI: 4.9–8.4).Conclusions. Our findings suggest a decrease in TBI-related hospitalizations but an increase in TBI-related mortality during the study period. The majority of TBI-related hospitalizations was due to other causes and falls and was more frequent in the older, male, and Medicare populations. The higher likelihood of TBI-related mortalities was found among elderly male≥75years old who had motor vehicle accidents. Our data can inform practitioners, prevention planners, educators, service sectors, and policy makers who aim to reduce the burden of TBI in the community. Implications for interventions are discussed.

scholarly journals Subspace Clustering of Physiological Data From Acute Traumatic Brain Injury Patients: Retrospective Analysis Based on the PROTECT III Trial

10.2196/24698 ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. e24698
Author(s):  
Sina Ehsani ◽  
Chandan K Reddy ◽  
Brandon Foreman ◽  
Jonathan Ratcliff ◽  
Vignesh Subbian
Keyword(s):  
Machine Learning ◽  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Subspace Clustering ◽  
Machine Learning Techniques ◽  
Phase Iii ◽  
Physiological Data ◽  
Clustering Methods ◽  
Data Set ◽  
Health Technologies

Background With advances in digital health technologies and proliferation of biomedical data in recent years, applications of machine learning in health care and medicine have gained considerable attention. While inpatient settings are equipped to generate rich clinical data from patients, there is a dearth of actionable information that can be used for pursuing secondary research for specific clinical conditions. Objective This study focused on applying unsupervised machine learning techniques for traumatic brain injury (TBI), which is the leading cause of death and disability among children and adults aged less than 44 years. Specifically, we present a case study to demonstrate the feasibility and applicability of subspace clustering techniques for extracting patterns from data collected from TBI patients. Methods Data for this study were obtained from the Progesterone for Traumatic Brain Injury, Experimental Clinical Treatment–Phase III (PROTECT III) trial, which included a cohort of 882 TBI patients. We applied subspace-clustering methods (density-based, cell-based, and clustering-oriented methods) to this data set and compared the performance of the different clustering methods. Results The analyses showed the following three clusters of laboratory physiological data: (1) international normalized ratio (INR), (2) INR, chloride, and creatinine, and (3) hemoglobin and hematocrit. While all subclustering algorithms had a reasonable accuracy in classifying patients by mortality status, the density-based algorithm had a higher F1 score and coverage. Conclusions Clustering approaches serve as an important step for phenotype definition and validation in clinical domains such as TBI, where patient and injury heterogeneity are among the major reasons for failure of clinical trials. The results from this study provide a foundation to develop scalable clustering algorithms for further research and validation.

scholarly journals Defining severe traumatic brain injury readmission rates and reasons in a rural state

2018 ◽  
Vol 3 (1) ◽  
pp. e000186 ◽  
Author(s):  
James Gardner ◽  
Kevin W Sexton ◽  
John Taylor ◽  
William Beck ◽  
Mary Katherine Kimbrough ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Chronic Diseases ◽  
Head Trauma ◽  
Motor Vehicle ◽  
Level Of Evidence ◽  
Data Set ◽  
Discharge Data ◽  
Long Term Treatment

BackgroundReadmissions after a traumatic brain injury (TBI) have significant impact on long-term patient outcomes through interruption of rehabilitation. This study examined readmissions in a rural population, hypothesizing that readmitted patients after TBI will be older and have more comorbidities than those not readmitted.MethodsDischarge data on all patients 15 years and older who were admitted to an Arkansas-based hospital for TBI were obtained from the Arkansas Hospital Discharge Data System from 2010 to 2014. This data set includes diagnoses (principal discharge diagnosis, up to 3 external cause of injury codes, 18 diagnosis codes using the International Classification of Disease, 9th Edition, Clinical Modifications), age, gender, and inpatient costs. Hospital Cost and Utilization Project Clinical Classification and Chronic Condition Indicator were used to identify chronic disease and body systems affected in principal diagnosis.ResultsOf the 3114 cases of significant head trauma, more than two-thirds were attributed to fall injuries, with motor vehicle crashes accounting for 20% of the remainder. The mean length of stay was 6.5 days. 691 of these patients were admitted to an Arkansas hospital in the following year, totaling 1368 readmissions. Of the readmissions, 16.4% of patients were admitted for altered mental status, 12.9% with shortness of breath (SOB), and 9.4% with chest pain. Mental disorders (psychosis, dementia, and depression) and organic nervous symptoms (Alzheimer’s disease, encephalopathy, and epilepsy) were the primary source of readmissions. More than one-third of the patients were admitted in the following year for chronic diseases such as heart failure (8.6%), psychosis (5.2%), and cerebral artery occlusion (4.1%).DiscussionThis study showed that there is a significant rate of readmissions in the year after a diagnosis of TBI. Complications with existing chronic diseases are among the most reported reasons for admission in this time period, demonstrating the effect severe head trauma has on long-term treatment.Level of evidenceLevel IV, Retrospective epidemiological study.

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