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Zoghi S, Ansari A, Niakan A, Taheri R, Khalili H. Post-discharge 6-Month Functional Recovery of Traumatic Brain Injury Survivors with Unfavorable Functional Status at Discharge: A Registry-Based Cohort Study. World Neurosurg 2024; 189:e580-e590. [PMID: 38936616 DOI: 10.1016/j.wneu.2024.06.116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 06/20/2024] [Accepted: 06/21/2024] [Indexed: 06/29/2024]
Abstract
BACKGROUND Traumatic brain injury (TBI) is a major cause of physical disabilities worldwide. Herein, we aimed to investigate the factors contributing to post-discharge recovery in patients who were discharged with an unfavorable outcome. METHODS We collected data on the characteristics of patients, with a focus on those who survived TBI but had an unfavorable outcome at discharge as measured by Glasgow Outcome Scale Extended (GOSE) categories 2, 3, and 4. Post-discharge recovery was defined as achieving a favorable functional status at 6 months (GOSE of 5 or more) with a minimum 2-point increase in GOSE. RESULTS Of 4011 TBI patients in our registry, 797 had an unfavorable discharge functional status. In severe TBI, 51% achieved recovery, while in mild to moderate TBI, 57% achieved recovery after 6 months. Older patients and those with shorter intensive care unit length of stay were more likely to experience post-discharge recovery in both mild to moderate and severe TBI groups. The presence of skull base fracture was also associated with post-discharge recovery in severe TBI patients. Lastly, we show that, after adjustment for potential confounders, GOSE at discharge is associated with post-discharge recovery in both mild to moderate and severe TBI patients. CONCLUSIONS This study found that the majority of patients discharged with an unfavorable functional status were able to achieve a favorable outcome within 6 months. The novel post-discharge recovery in TBI patients might be a useful tool for illuminating the factors associated with a significant improvement after discharge.
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Affiliation(s)
- Sina Zoghi
- Department of Neurosurgery, Shiraz University of Medical Sciences, Shiraz, Iran; Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Ali Ansari
- Department of Neurosurgery, Shiraz University of Medical Sciences, Shiraz, Iran; Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Amin Niakan
- Department of Neurosurgery, Shiraz University of Medical Sciences, Shiraz, Iran; Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Reza Taheri
- Shiraz Neuroscience Research Center, Shiraz University of Medical Sciences, Shiraz, Iran; School of Medicine, Fasa University of Medical Sciences, Fasa, Iran.
| | - Hosseinali Khalili
- Department of Neurosurgery, Shiraz University of Medical Sciences, Shiraz, Iran; Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Shiraz University of Medical Sciences, Shiraz, Iran
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Tenorio A, Brandel MG, McCann CP, Real M, Doucet JJ, Costantini TW, Santiago-Dieppa DR, Levy M, Ciacci JD. Increased Traumatic Brain Injury Severity and Mortality in Undocumented Immigrants. Neurosurgery 2024:00006123-990000000-01327. [PMID: 39212417 DOI: 10.1227/neu.0000000000003158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 07/23/2024] [Indexed: 09/04/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Health disparities related to traumatic brain injury (TBI) have focused on socioeconomic status, race, and ethnicity. We sought to characterize TBI patterns and outcomes based on undocumented status. METHODS Patients who presented to University of California, San Diego Health Trauma Center with a TBI between 2019 and 2022 were identified and stratified based on undocumented status. Undocumented immigrants were identified using validated methods of absent or invalid social security number and key terms through chart review. Demographic information, injury characteristics, and neurosurgical interventions were recorded. Univariable and multivariable analyses were performed to determine the impact of patient factors on outcomes. RESULTS Of 1654 patients with TBI, 76 (4.6%) were undocumented. Undocumented immigrants were younger (50 vs 60 years; P < .001) and had higher Injury Severity Score (17 vs 13; P < .001). They presented from farther distances (12.8 vs 5.3 miles, P < .001) with greater midline shift (1.49 vs 0.91 mm; P = .003). A greater proportion had basal cistern compression/effacement (14% vs 4.6%; P = .001) and required neurosurgical intervention (18% vs 9.6%; P = .012). Undocumented immigrants had higher hospital charges ($208 403 vs $128 948; P < .001), length of stay (5 vs 4 days; P = .002), and were discharged to a health facility at a lower rate (18% vs 32%; P = .012). They had nearly double the mortality rate (14% vs 7.3%; P = .021), with undocumented status trending as a predictor on multivariable regression (odds ratio = 2.87; P = .052). CONCLUSION Undocumented immigrants presented from farther distances with increased TBI severity, likely from both more severe trauma and delayed presentation, requiring more neurosurgical intervention. They also had greater length of stay, charges, and nearly double the mortality rate. Importantly, undocumented status was a strong predictor for mortality. Despite worse outcomes, they were discharged to a health care facility at a lower rate. Advocacy efforts should be directed at increasing health care coverage and migrant community engagement and education.
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Affiliation(s)
- Alexander Tenorio
- Department of Neurosurgery, University of California, San Diego, La Jolla, California, USA
| | - Michael G Brandel
- Department of Neurosurgery, University of California, San Diego, La Jolla, California, USA
| | - Carson P McCann
- School of Medicine, University of California, San Diego, La Jolla, California, USA
| | - Marcos Real
- School of Medicine, University of California, San Diego, La Jolla, California, USA
| | - Jay J Doucet
- Division of Trauma, Surgical Critical Care, Burns and Acute Care Surgery, Department of Surgery, University of California, San Diego, La Jolla, California, USA
| | - Todd W Costantini
- Division of Trauma, Surgical Critical Care, Burns and Acute Care Surgery, Department of Surgery, University of California, San Diego, La Jolla, California, USA
| | | | - Michael Levy
- Department of Neurosurgery, University of California, San Diego, La Jolla, California, USA
| | - Joseph D Ciacci
- Department of Neurosurgery, University of California, San Diego, La Jolla, California, USA
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Chen X, Józsa TI, Cardim D, Robba C, Czosnyka M, Payne SJ. Modelling midline shift and ventricle collapse in cerebral oedema following acute ischaemic stroke. PLoS Comput Biol 2024; 20:e1012145. [PMID: 38805558 PMCID: PMC11161059 DOI: 10.1371/journal.pcbi.1012145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Revised: 06/07/2024] [Accepted: 05/08/2024] [Indexed: 05/30/2024] Open
Abstract
In ischaemic stroke, a large reduction in blood supply can lead to the breakdown of the blood-brain barrier and to cerebral oedema after reperfusion therapy. The resulting fluid accumulation in the brain may contribute to a significant rise in intracranial pressure (ICP) and tissue deformation. Changes in the level of ICP are essential for clinical decision-making and therapeutic strategies. However, the measurement of ICP is constrained by clinical techniques and obtaining the exact values of the ICP has proven challenging. In this study, we propose the first computational model for the simulation of cerebral oedema following acute ischaemic stroke for the investigation of ICP and midline shift (MLS) relationship. The model consists of three components for the simulation of healthy blood flow, occluded blood flow and oedema, respectively. The healthy and occluded blood flow components are utilized to obtain oedema core geometry and then imported into the oedema model for the simulation of oedema growth. The simulation results of the model are compared with clinical data from 97 traumatic brain injury patients for the validation of major model parameters. Midline shift has been widely used for the diagnosis, clinical decision-making, and prognosis of oedema patients. Therefore, we focus on quantifying the relationship between ICP and midline shift (MLS) and identify the factors that can affect the ICP-MLS relationship. Three major factors are investigated, including the brain geometry, blood-brain barrier damage severity and the types of oedema (including rare types of oedema). Meanwhile, the two major types (stress and tension/compression) of mechanical brain damage are also presented and the differences in the stress, tension, and compression between the intraparenchymal and periventricular regions are discussed. This work helps to predict ICP precisely and therefore provides improved clinical guidance for the treatment of brain oedema.
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Affiliation(s)
- Xi Chen
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Tamás I. Józsa
- School of Aerospace, Transport and Manufacturing Cranfield University, Cranfield, United Kingdom
| | - Danilo Cardim
- Department of Neurology, University of Texas Southwestern Medical Centre, Dallas, Texas, United States of America
- Institute for Exercise and Environmental Medicine, Texas Health Presbyterian Hospital, Dallas, Texas, United States of America
| | - Chiara Robba
- Department of Anesthesia and Intensive Care, IRCCS Ospedale Policlinico San Martino, Genova, Italy
| | - Marek Czosnyka
- Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
- Institute of Electronic Systems, Warsaw University of Technology, Warsaw, Poland
| | - Stephen J. Payne
- Institute of Applied Mechanics, National Taiwan University, Taiwan
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Khademolhosseini S, Habibzadeh A, Zoghi S, Taheri R, Niakan A, Khalili H. Precision and Speed at Your Fingertips: An Automated Intracranial Hematoma Volume Calculation. World Neurosurg 2024; 185:e827-e834. [PMID: 38453009 DOI: 10.1016/j.wneu.2024.02.135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Accepted: 02/24/2024] [Indexed: 03/09/2024]
Abstract
BACKGROUND Intracranial hemorrhage (ICH) is a severe condition that requires rapid diagnosis and treatment. Automated methods for calculating ICH volumes can reduce human error and improve clinical decisioPlease provide professional degrees (e.g., PhD, MD) for the corresponding author.n-making. A novel automated method has been developed that is comparable to the ABC/2 method in terms of speed and accuracy while providing more accurate volumetric data. METHODS We developed a novel automated algorithm for calculating intracranial blood volume from computed tomography (CT) scans. The algorithm consists of a Python script that processes Digital Imaging and Communications in Medicine images and determines the blood volume and ratio. The algorithm was validated against manual calculations performed by neurosurgeons. RESULTS Our novel automated algorithm for calculating intracranial blood volume from CT scans demonstrated excellent agreement with the ABC/2 method, with a median overall difference of just 1.46 mL. The algorithm was also validated in patient groups with ICH, epidural hematoma (EDH), and SDH, with agreement coefficients of 0.992, 0.983, and 0.997, respectively. CONCLUSIONS The study introduces a novel automated algorithm for calculating the volumes of various ICHs (EDH, and SDH) within CT scans. The algorithm showed excellent agreement with manual calculations and outperformed the commonly used ABC/2 method, which tends to overestimate ICH volume. The automated algorithm offers a more accurate, efficient, and time-saving approach to quantifying ICH, EDH, and SDH volumes, making it a valuable tool for clinical evaluation and decision-making.
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Affiliation(s)
| | - Adrina Habibzadeh
- Shiraz Trauma Research Center, Shiraz, Iran; Student Research Committee, Fasa University of Medical Sciences, Fasa, Iran; USERN Office, Fasa University of Medical Sciences, Fasa, Iran
| | - Sina Zoghi
- Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Reza Taheri
- Shiraz Neurosurgery Department, Shiraz University of Medical Sciences, Shiraz, Iran; Clinical Research Development Unit, Valiasr Hospital, Fasa University of Medical Sciences, Fasa, Iran; Shiraz Neuroscience Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
| | - Amin Niakan
- Shiraz Trauma Research Center, Shiraz, Iran; Shiraz Neurosurgery Department, Shiraz University of Medical Sciences, Shiraz, Iran
| | - HosseinAli Khalili
- Shiraz Trauma Research Center, Shiraz, Iran; Shiraz Neurosurgery Department, Shiraz University of Medical Sciences, Shiraz, Iran
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Sengupta SK, Aggarwal R, Singh MK. Correlation Between Volume and Pressure of Intracranial Space With Craniectomy Surface Area and Brain Herniation: A Phantom-Based Study. Neurotrauma Rep 2024; 5:293-303. [PMID: 38560491 PMCID: PMC10979661 DOI: 10.1089/neur.2024.0006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2024] Open
Abstract
There are proponents of decompressive craniectomy (DC) and its various modifications who claim reasonable clinical outcomes for each of them. Clinical outcome in cases of traumatic brain injury, managed conservatively or aided by different surgical techniques, depends on multiple factors, which vary widely among patients and have complex interplay, making it difficult to compare one case with another in absolute terms. This forms the basis of the perceived necessity to have a standard model to study, compare, and strategize in this field. We designed a phantom-based model and present the findings of the study aimed at establishing a correlation of the volume of intracranial space and changes in intracranial pressure (ICP) with surface area of the craniectomy defect created during DC and brain herniation volume. A roughly hemispherical radio-opaque container was scanned on a 128-slice computed tomography scanner. Craniectomies of different sizes and shapes were marked on the walls of the phantom. Two spherical sacs of stretchable materials were subsequently placed inside the phantom, fixed to three-way connectors, filled with water, and connected with transducers. The terminals of the transducer cables were coupled with the display monitor through a signal amplifier and processor module. Parts of the wall of the phantom were removed to let portions of the sac herniate through the defect, simulating a DC. Volume measurements using AW volume share 7® software were done. Resection of a 12.7 × 11.5 cm part of the wall resulted in a 10-cm-diameter defect in the wall. Volume differential of 35 mL created a midline shift of 5 mm to the side with lesser volume. When measuring pressure in two stretchable sacs contained inside the phantom, there always remained a pressure differential ranging from 1 to 2 mm Hg in different recordings, even with sacs on both sides containing an equal volume of fluids. Creating a circular wall defect of 10 cm in diameter with an intracavitary pressure of 35 mm Hg on the ipsilateral sac and 33 mm on the contralateral sac recorded with intact walls, resulted in a true volume expansion of 48.411 cm3. The herniation resulted in a reduction of pressure in both sacs, with the pressure recorded as 25 mm in the ipsilateral sac and 24 mm in the contralateral sac. The findings closely matched those of the other model-based studies. Refinement of the materials used is likely to provide a valid platform to study cranial volume, ICP, craniectomy size, and brain prolapse volume in real time. The model will help in pre-operatively choosing the most appropriate technique between a classical DC, a hinge craniotomy, and an expansive cranioplasty technique in cases of refractory raised ICP.
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Affiliation(s)
| | - Rohit Aggarwal
- Department of Radiology, Command Hospital Southern Command (Pune), India
| | - Manish Kumar Singh
- Department of Anaesthesia, Command Hospital Southern Command (Pune), India
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Dubinski D, Won SY, Meyer-Wilmes J, Trnovec S, Rafaelian A, Behmanesh B, Cantré D, Baumgarten P, Dinc N, Konczalla J, Wittstock M, Bernstock JD, Freiman TM, Gessler F. Frailty in Traumatic Brain Injury-The Significance of Temporal Muscle Thickness. J Clin Med 2023; 12:7625. [PMID: 38137693 PMCID: PMC10743381 DOI: 10.3390/jcm12247625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 11/23/2023] [Accepted: 12/08/2023] [Indexed: 12/24/2023] Open
Abstract
BACKGROUND Temporal muscle thickness (TMT) on cranial CT scans has recently been identified as a prognostic imaging parameter for assessing a patient's baseline frailty. Here, we analyzed whether TMT correlates with Traumatic brain injury (TBI) severity and whether it can be used to predict outcome(s) after TBI. METHODS We analyzed the radiological and clinical data sets of 193 patients with TBI who were admitted to our institution and correlated the radiological data with clinical outcomes after stratification for TMT. RESULTS Our analyses showed a significant association between high TMT and increased risk for intracranial hemorrhage (p = 0.0135) but improved mRS at 6 months (p = 0.001) as compared to patients with low TMT. Congruent with such findings, a lower TMT was associated with falls and reduced outcomes at 6 months (p < 0.0001 and p < 0.0001). CONCLUSION High TMT was robustly associated with head trauma sequelae but was also associated with good clinical outcomes in TBI patients. These findings consolidate the significance of TMT as an objective marker of frailty in TBI patients; such measurements may ultimately be leveraged as prognostic indicators.
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Affiliation(s)
- Daniel Dubinski
- Department of Neurosurgery, University Medicine Rostock, 18057 Rostock, Germany; (S.-Y.W.); (J.M.-W.); (S.T.); (A.R.); (B.B.); (T.M.F.); (F.G.)
| | - Sae-Yeon Won
- Department of Neurosurgery, University Medicine Rostock, 18057 Rostock, Germany; (S.-Y.W.); (J.M.-W.); (S.T.); (A.R.); (B.B.); (T.M.F.); (F.G.)
| | - Jonas Meyer-Wilmes
- Department of Neurosurgery, University Medicine Rostock, 18057 Rostock, Germany; (S.-Y.W.); (J.M.-W.); (S.T.); (A.R.); (B.B.); (T.M.F.); (F.G.)
| | - Svorad Trnovec
- Department of Neurosurgery, University Medicine Rostock, 18057 Rostock, Germany; (S.-Y.W.); (J.M.-W.); (S.T.); (A.R.); (B.B.); (T.M.F.); (F.G.)
| | - Artem Rafaelian
- Department of Neurosurgery, University Medicine Rostock, 18057 Rostock, Germany; (S.-Y.W.); (J.M.-W.); (S.T.); (A.R.); (B.B.); (T.M.F.); (F.G.)
| | - Bedjan Behmanesh
- Department of Neurosurgery, University Medicine Rostock, 18057 Rostock, Germany; (S.-Y.W.); (J.M.-W.); (S.T.); (A.R.); (B.B.); (T.M.F.); (F.G.)
| | - Daniel Cantré
- Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, University Medicine Rostock, 18057 Rostock, Germany;
| | - Peter Baumgarten
- Department of Neurosurgery, University Hospital, Schiller University Jena, 07747 Jena, Germany; (P.B.); (N.D.)
| | - Nazife Dinc
- Department of Neurosurgery, University Hospital, Schiller University Jena, 07747 Jena, Germany; (P.B.); (N.D.)
| | - Juergen Konczalla
- Department of Neurosurgery, Goethe-University Hospital, 60596 Frankfurt am Main, Germany;
| | - Matthias Wittstock
- Department of Neurology, University Medicine Rostock, 18057 Rostock, Germany;
| | - Joshua D. Bernstock
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA;
| | - Thomas M. Freiman
- Department of Neurosurgery, University Medicine Rostock, 18057 Rostock, Germany; (S.-Y.W.); (J.M.-W.); (S.T.); (A.R.); (B.B.); (T.M.F.); (F.G.)
| | - Florian Gessler
- Department of Neurosurgery, University Medicine Rostock, 18057 Rostock, Germany; (S.-Y.W.); (J.M.-W.); (S.T.); (A.R.); (B.B.); (T.M.F.); (F.G.)
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Kazimierska A, Uryga A, Mataczyński C, Czosnyka M, Lang EW, Kasprowicz M. Relationship between the shape of intracranial pressure pulse waveform and computed tomography characteristics in patients after traumatic brain injury. Crit Care 2023; 27:447. [PMID: 37978548 PMCID: PMC10656987 DOI: 10.1186/s13054-023-04731-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 11/09/2023] [Indexed: 11/19/2023] Open
Abstract
BACKGROUND Midline shift and mass lesions may occur with traumatic brain injury (TBI) and are associated with higher mortality and morbidity. The shape of intracranial pressure (ICP) pulse waveform reflects the state of cerebrospinal pressure-volume compensation which may be disturbed by brain injury. We aimed to investigate the link between ICP pulse shape and pathological computed tomography (CT) features. METHODS ICP recordings and CT scans from 130 TBI patients from the CENTER-TBI high-resolution sub-study were analyzed retrospectively. Midline shift, lesion volume, Marshall and Rotterdam scores were assessed in the first CT scan after admission and compared with indices derived from the first 24 h of ICP recording: mean ICP, pulse amplitude of ICP (AmpICP) and pulse shape index (PSI). A neural network model was applied to automatically group ICP pulses into four classes ranging from 1 (normal) to 4 (pathological), with PSI calculated as the weighted sum of class numbers. The relationship between each metric and CT measures was assessed using Mann-Whitney U test (groups with midline shift > 5 mm or lesions > 25 cm3 present/absent) and the Spearman correlation coefficient. Performance of ICP-derived metrics in identifying patients with pathological CT findings was assessed using the area under the receiver operating characteristic curve (AUC). RESULTS PSI was significantly higher in patients with mass lesions (with lesions: 2.4 [1.9-3.1] vs. 1.8 [1.1-2.3] in those without; p << 0.001) and those with midline shift (2.5 [1.9-3.4] vs. 1.8 [1.2-2.4]; p < 0.001), whereas mean ICP and AmpICP were comparable. PSI was significantly correlated with the extent of midline shift, total lesion volume and the Marshall and Rotterdam scores. PSI showed AUCs > 0.7 in classification of patients as presenting pathological CT features compared to AUCs ≤ 0.6 for mean ICP and AmpICP. CONCLUSIONS ICP pulse shape reflects the reduction in cerebrospinal compensatory reserve related to space-occupying lesions despite comparable mean ICP and AmpICP levels. Future validation of PSI is necessary to explore its association with volume imbalance in the intracranial space and a potential complementary role to the existing monitoring strategies.
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Affiliation(s)
- Agnieszka Kazimierska
- Department of Biomedical Engineering, Faculty of Fundamental Problems of Technology, Wroclaw University of Science and Technology, 27 Wybrzeze Wyspianskiego Street, 50-370, Wroclaw, Poland.
| | - Agnieszka Uryga
- Department of Biomedical Engineering, Faculty of Fundamental Problems of Technology, Wroclaw University of Science and Technology, 27 Wybrzeze Wyspianskiego Street, 50-370, Wroclaw, Poland
| | - Cyprian Mataczyński
- Department of Computer Engineering, Faculty of Electronics, Wroclaw University of Science and Technology, Wroclaw, Poland
| | - Marek Czosnyka
- Brain Physics Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Institute of Electronic Systems, Faculty of Electronics and Information Technology, Warsaw University of Technology, Warsaw, Poland
| | - Erhard W Lang
- Neurosurgical Associates, Red Cross Hospital, Kassel, Germany
- Department of Neurosurgery, Faculty of Medicine, Georg-August-Universität, Göttingen, Germany
| | - Magdalena Kasprowicz
- Department of Biomedical Engineering, Faculty of Fundamental Problems of Technology, Wroclaw University of Science and Technology, 27 Wybrzeze Wyspianskiego Street, 50-370, Wroclaw, Poland.
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Bah MG, Chen AY, Hart K, Vahidy Z, Coles J, Mahas R, Eden SV. Racial Disparities in Employment Status After Moderate/Severe Traumatic Brain Injuries in Southeast Michigan. Arch Phys Med Rehabil 2023; 104:1173-1179. [PMID: 37178951 PMCID: PMC10524608 DOI: 10.1016/j.apmr.2023.04.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 03/26/2023] [Accepted: 04/17/2023] [Indexed: 05/15/2023]
Abstract
OBJECTIVE To examine the progress made in recent decades by assessing the employment rates of Black and non-Hispanic White (NHW) patients after traumatic brain injury (TBI), controlling for pre-TBI employment status and education status. DESIGN Retrospective analysis in a cohort of patients treated in Southeast Michigan at major trauma centers in more recent years (February 2010 to December 2019). SETTING Southeastern Michigan Traumatic Brain Injury Model System (TBIMS): 1 of 16 TBIMSs across the United States. PARTICIPANTS NHW (n=81) and Black (n=188) patients with moderate/severe TBI (N=269). INTERVENTION Not applicable. MAIN OUTCOME MEASURES Employment status, which is separated into 2 categories: student plus competitive employment and noncompetitive employment. RESULTS In 269 patients, NHW patients had more severe initial TBI, measured by percentage brain computed tomography with compression causing >5-mm midline shift (P<.001). Controlling for pre-TBI employment status, we found NHW participants who were students or had competitive employment prior to TBI had higher rates of competitive employment at 2-year (P=.03) follow-up. Controlling for pre-TBI education status, we found no difference in competitive and noncompetitive employment rates between NHW and Black participants at all follow-up years. CONCLUSIONS Black patients who were students or had competitive employment before TBI experience worse employment outcomes than their NHW counterparts after TBI at 2 years post TBI. Further research is needed to understand better the factors driving these disparities and how social determinants of health affect these racial differences after TBI.
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Affiliation(s)
- Momodou G Bah
- Michigan State University College of Human Medicine, East Lansing, MI
| | - Alex Y Chen
- Department of Neurology, Case Western Reserve University, University Hospital Cleveland Medical Center, Cleveland, OH
| | - Kristina Hart
- Wayne State University School of Medicine, Detroit, MI
| | - Zara Vahidy
- Wayne State University School of Medicine, Detroit, MI
| | - Jasmine Coles
- Wayne State University School of Medicine, Detroit, MI
| | - Rachel Mahas
- Department of Family Medicine and Public Health Sciences, Wayne State University, Detroit, MI
| | - Sonia V Eden
- Department of Neurosurgery, Semmes Murphey Clinic, Memphis, TN; University of Tennessee Health Sciences Center, Memphis, TN.
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Rajaei F, Cheng S, Williamson CA, Wittrup E, Najarian K. AI-Based Decision Support System for Traumatic Brain Injury: A Survey. Diagnostics (Basel) 2023; 13:diagnostics13091640. [PMID: 37175031 PMCID: PMC10177859 DOI: 10.3390/diagnostics13091640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 04/22/2023] [Accepted: 04/29/2023] [Indexed: 05/15/2023] Open
Abstract
Traumatic brain injury (TBI) is one of the major causes of disability and mortality worldwide. Rapid and precise clinical assessment and decision-making are essential to improve the outcome and the resulting complications. Due to the size and complexity of the data analyzed in TBI cases, computer-aided data processing, analysis, and decision support systems could play an important role. However, developing such systems is challenging due to the heterogeneity of symptoms, varying data quality caused by different spatio-temporal resolutions, and the inherent noise associated with image and signal acquisition. The purpose of this article is to review current advances in developing artificial intelligence-based decision support systems for the diagnosis, severity assessment, and long-term prognosis of TBI complications.
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Affiliation(s)
- Flora Rajaei
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Shuyang Cheng
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Craig A Williamson
- Department of Neurosurgery, University of Michigan, Ann Arbor, MI 48109, USA
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor, MI 48109, USA
| | - Emily Wittrup
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Kayvan Najarian
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor, MI 48109, USA
- Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Emergency Medicine, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA
- Center for Data-Driven Drug Development and Treatment Assessment (DATA), University of Michigan, Ann Arbor, MI 48109, USA
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Is there a weekend effect on mortality rate and outcome for moderate and severe traumatic brain injury? A population-based, observational cohort study. BRAIN & SPINE 2022; 2:101699. [PMID: 36506297 PMCID: PMC9729811 DOI: 10.1016/j.bas.2022.101699] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 11/17/2022] [Accepted: 11/28/2022] [Indexed: 12/03/2022]
Abstract
Purpose The aim of the study was to analyse patient and injury characteristics and the effects of weekend admissions on mortality rate and outcome after moderate and severe traumatic brain injuries. Methods This is an observational cohort study based on data from a prospectively maintained regional trauma registry in South Western Norway. Patients with moderate and severe traumatic brain injury admitted between January 1st, 2004 and December 31st, 2019 were included in this study. Results During the study period 688 patients were included in the study with similar distribution between moderate (n = 318) and severe (n = 370) traumatic brain injury. Mortality rate was 46% in severe and 13% in moderate traumatic brain injury. Two hundred and thirty-one (34%) patients were admitted during weekends. Patients admitted during weekends were significantly younger (median age (IQR) 32.0 (25.5-67.0) vs 47.0 (20.0-55.0), p < 0.001). Pre-injury ASA 1 was significantly more common in patients admitted during weekends (n = 146, 64%, p = 0.001) while ASA 3 showed significance during weekdays compared to weekends (n = 101, 22%, p = 0.013). On binominal logistic regression analysis mortality rate was significantly higher with older age (OR 1.03, 95% CI for OR 1.02-1.04, p < 0.001) and increasing TBI severity (OR 7.08, 95% CI for OR 4.67-10.73, p < 0.001). Conclusions Mortality rate and poor clinical outcome remain high in severe traumatic brain injury. While a higher number of patients are admitted during the weekend, mortality rate does not differ from weekday admissions.
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11
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Hibi A, Jaberipour M, Cusimano MD, Bilbily A, Krishnan RG, Aviv RI, Tyrrell PN. Automated identification and quantification of traumatic brain injury from CT scans: Are we there yet? Medicine (Baltimore) 2022; 101:e31848. [PMID: 36451512 PMCID: PMC9704869 DOI: 10.1097/md.0000000000031848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 10/26/2022] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND The purpose of this study was to conduct a systematic review for understanding the availability and limitations of artificial intelligence (AI) approaches that could automatically identify and quantify computed tomography (CT) findings in traumatic brain injury (TBI). METHODS Systematic review, in accordance with PRISMA 2020 and SPIRIT-AI extension guidelines, with a search of 4 databases (Medline, Embase, IEEE Xplore, and Web of Science) was performed to find AI studies that automated the clinical tasks for identifying and quantifying CT findings of TBI-related abnormalities. RESULTS A total of 531 unique publications were reviewed, which resulted in 66 articles that met our inclusion criteria. The following components for identification and quantification regarding TBI were covered and automated by existing AI studies: identification of TBI-related abnormalities; classification of intracranial hemorrhage types; slice-, pixel-, and voxel-level localization of hemorrhage; measurement of midline shift; and measurement of hematoma volume. Automated identification of obliterated basal cisterns was not investigated in the existing AI studies. Most of the AI algorithms were based on deep neural networks that were trained on 2- or 3-dimensional CT imaging datasets. CONCLUSION We identified several important TBI-related CT findings that can be automatically identified and quantified with AI. A combination of these techniques may provide useful tools to enhance reproducibility of TBI identification and quantification by supporting radiologists and clinicians in their TBI assessments and reducing subjective human factors.
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Affiliation(s)
- Atsuhiro Hibi
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
| | - Majid Jaberipour
- Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
| | - Michael D. Cusimano
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
- Division of Neurosurgery, St Michael’s Hospital, University of Toronto, Toronto, Canada
| | - Alexander Bilbily
- Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
- Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Rahul G. Krishnan
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Department of Laboratory Medicine & Pathobiology, University of Toronto, Toronto, Ontario, Canada
| | - Richard I. Aviv
- Department of Radiology, Radiation Oncology and Medical Physics, University of Ottawa, Ottawa, Ontario, Canada
| | - Pascal N. Tyrrell
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
- Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
- Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada
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12
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Martínez‐Molina N, Siponkoski S, Särkämö T. Cognitive efficacy and neural mechanisms of music-based neurological rehabilitation for traumatic brain injury. Ann N Y Acad Sci 2022; 1515:20-32. [PMID: 35676218 PMCID: PMC9796942 DOI: 10.1111/nyas.14800] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Traumatic brain injury (TBI) causes lifelong cognitive deficits, most often in executive function (EF). Both musical training and music-based rehabilitation have been shown to enhance EF and neuroplasticity. Thus far, however, there is little evidence for the potential rehabilitative effects of music for TBI. Here, we review the core findings from our recent cross-over randomized controlled trial in which a 10-week music-based neurological rehabilitation (MBNR) protocol was administered to 40 patients with moderate-to-severe TBI. Neuropsychological testing and structural/functional magnetic resonance imaging were collected at three time points (baseline, 3 months, and 6 months); one group received the MBNR between time points 1 and 2, while a second group received it between time points 2 and 3. We found that both general EF and set shifting improved after the intervention, and this effect was maintained long term. Morphometric analyses revealed therapy-induced gray matter volume changes most consistently in the right inferior frontal gyrus, changes that correlated with better outcomes in set shifting. Finally, we found changes in the between- and within-network functional connectivity of large-scale resting-state networks after MBNR, which also correlated with measures of EF. Taken together, the data provide evidence for concluding that MBNR improves EF in TBI; also, the data show that morphometric and resting-state functional connectivity are sensitive markers with which to monitor the neuroplasticity induced by the MBNR intervention.
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Affiliation(s)
- Noelia Martínez‐Molina
- Music, Ageing and Rehabilitation Team, Cognitive Brain Research Unit, Department of Psychology and LogopedicsUniversity of HelsinkiHelsinki FI‐00014Finland,Centre of Excellence in Music, Mind, Body and BrainUniversity of Jyväskylä & University of HelsinkiHelsinkiFinland
| | - Sini‐Tuuli Siponkoski
- Music, Ageing and Rehabilitation Team, Cognitive Brain Research Unit, Department of Psychology and LogopedicsUniversity of HelsinkiHelsinki FI‐00014Finland,Centre of Excellence in Music, Mind, Body and BrainUniversity of Jyväskylä & University of HelsinkiHelsinkiFinland
| | - Teppo Särkämö
- Music, Ageing and Rehabilitation Team, Cognitive Brain Research Unit, Department of Psychology and LogopedicsUniversity of HelsinkiHelsinki FI‐00014Finland,Centre of Excellence in Music, Mind, Body and BrainUniversity of Jyväskylä & University of HelsinkiHelsinkiFinland
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13
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Kobata H, Kuroda Y, Suehiro E, Kaneko T, Fujita M, Bunya N, Miyata K, Inoue A, Hifumi T, Oda Y, Dohi K, Yamashita S, Maekawa T. Benefits of Hypothermia for Young Patients with Acute Subdural Hematoma: A Computed Tomography Analysis of the Brain Hypothermia Study. Neurotrauma Rep 2022; 3:250-260. [PMID: 35982984 PMCID: PMC9380885 DOI: 10.1089/neur.2021.0080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Therapeutic hypothermia for severe traumatic brain injury (TBI) has been repeatedly studied, but no past studies have assessed the detailed head computed tomography (CT) findings. We sought to investigate individual CT findings of severe TBI patients treated with targeted temperature management utilizing the head CT database obtained from the Brain Hypothermia study. Enrolled patients underwent either mild therapeutic hypothermia (32.0°C-34.0°C) or fever control (35.5°C-37.0°C). We assessed individual head CT images on arrival and after rewarming and investigated the correlations with outcomes. The initial CT data were available for 125 patients (hypothermia group = 80, fever control group = 45). Baseline characteristics and CT findings, such as hematoma thickness and midline shift, were similar in all aspects between the two groups. The favorable outcomes in the hypothermia and fever control groups were 38 (47.5%) and 24 (53.3%; p = 0.53) for all 125 patients, respectively; 21 (46.7%) vs. 10 (38.5%; p = 0.50) for 71 patients with acute subdural hematoma (SDH), respectively; and 12 (75.0%) vs. 4 (36.4%; p = 0.045) in 27 young adults (≤50 years) with acute SDH, respectively. There was a trend toward favorable outcomes for earlier time to reach 35.5°C (190 vs. 377 min, p = 0.052) and surgery (155 vs. 180 min, p = 0.096) in young patients with acute SDH. The second CT image revealed progression of the brain injury. This study demonstrated the potential benefits of early hypothermia in young patients with acute SDH, despite no difference in CT findings between the two groups. However, the small number of cases involved hindered the drawing of definitive conclusions. Future studies are warranted to validate the results.
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Affiliation(s)
- Hitoshi Kobata
- Department of Neurosurgery, Osaka Mishima Emergency Critical Care Center, Takatsuki, Japan
| | - Yasuhiro Kuroda
- Department of Emergency, Disaster, and Critical Care Medicine, Kagawa University School of Medicine, Takamatsu, Japan
| | - Eiichi Suehiro
- Department of Neurosurgery, International University of Health and Welfare, School of Medicine, Narita, Japan
| | - Tadashi Kaneko
- Emergency and Critical Care Center, Mie University Hospital, Tsu, Japan
| | - Motoki Fujita
- Advanced Medical Emergency and Critical Care Center, Yamaguchi University Hospital, Ube, Japan
| | - Naofumi Bunya
- Department of Emergency Medicine and Sapporo Medical University, Sapporo, Japan
| | - Kei Miyata
- Neurosurgery, Sapporo Medical University, Sapporo, Japan
| | - Akihiko Inoue
- Department of Emergency and Critical Care Medicine, Hyogo Emergency Medical Center, Kobe, Japan
| | - Toru Hifumi
- Department of Emergency and Critical Care Medicine, St. Luke's International Hospital, Tokyo, Japan
| | - Yasutaka Oda
- Advanced Medical Emergency and Critical Care Center, Yamaguchi University Hospital, Ube, Japan
| | - Kenji Dohi
- Department of Emergency, Disaster and Critical Care Medicine, Showa University, Tokyo, Japan
| | - Susumu Yamashita
- Emergency and Critical Care Center, Tokuyama Central Hospital, Tokuyama, Japan
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14
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A Robust, Fully Automatic Detection Method and Calculation Technique of Midline Shift in Intracranial Hemorrhage and Its Clinical Application. Diagnostics (Basel) 2022; 12:diagnostics12030693. [PMID: 35328245 PMCID: PMC8947005 DOI: 10.3390/diagnostics12030693] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 03/09/2022] [Accepted: 03/10/2022] [Indexed: 02/04/2023] Open
Abstract
A midline shift (MLS) is an important clinical indicator for intracranial hemorrhage. In this study, we proposed a robust, fully automatic neural network-based model for the detection of MLS and compared it with MLSs drawn by clinicians; we also evaluated the clinical applications of the fully automatic model. We recruited 300 consecutive non-contrast CT scans consisting of 7269 slices in this study. Six different types of hemorrhage were included. The automatic detection of MLS was based on modified Keypoint R-CNN with keypoint detection followed by training on the ResNet-FPN-50 backbone. The results were further compared with manually drawn outcomes and manually defined keypoint calculations. Clinical parameters, including Glasgow coma scale (GCS), Glasgow outcome scale (GOS), and 30-day mortality, were also analyzed. The mean absolute error for the automatic detection of an MLS was 0.936 mm compared with the ground truth. The interclass correlation was 0.9899 between the automatic method and MLS drawn by different clinicians. There was high sensitivity and specificity in the detection of MLS at 2 mm (91.7%, 80%) and 5 mm (87.5%, 96.7%) and MLSs greater than 10 mm (85.7%, 97.7%). MLS showed a significant association with initial poor GCS and GCS on day 7 and was inversely correlated with poor 30-day GOS (p < 0.001). In conclusion, automatic detection and calculation of MLS can provide an accurate, robust method for MLS measurement that is clinically comparable to the manually drawn method.
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15
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Magnusson BM, Isaksson E, Koskinen LOD. A prospective observational cohort study of traumatic brain injury in the northern region of Sweden. Brain Inj 2022; 36:191-198. [DOI: 10.1080/02699052.2022.2034952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Affiliation(s)
- Beatrice M. Magnusson
- Department of Surgery and Perioperative Sciences, Anesthesiology and Intensive Care Medicine, Umeå University, Sweden
| | - Emil Isaksson
- Department of Surgery and Perioperative Sciences, Anesthesiology and Intensive Care Medicine, Umeå University, Sweden
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16
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Jing X, Wang X, Zhuang H, Fang X, Xu H. Multiple Machine Learning Approaches Based on Postoperative Prediction of Pulmonary Complications in Patients With Emergency Cerebral Hemorrhage Surgery. Front Surg 2022; 8:797872. [PMID: 35127804 PMCID: PMC8812295 DOI: 10.3389/fsurg.2021.797872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 12/01/2021] [Indexed: 11/26/2022] Open
Abstract
Objective This study aimed to create a prediction model of postoperative pulmonary complications for the patients with emergency cerebral hemorrhage surgery. Methods Patients with hemorrhage surgery who underwent cerebral hemorrhage surgery were included and divided into two groups: patients with or without pulmonary complications. Patient characteristics, previous history, laboratory tests, and interventions were collected. Univariate and multivariate logistic regressions were used to predict postoperative pulmonary infection. Multiple machine learning approaches have been used to compare their importance in predicting factors, namely K-nearest neighbor (KNN), stochastic gradient descent (SGD), support vector classification (SVC), random forest (RF), and logistics regression (LR), as they are the most successful and widely used models for clinical data. Results Three hundred and fifty four patients with emergency cerebral hemorrhage surgery between January 1, 2017 and December 31, 2020 were included in the study. 53.7% (190/354) of the patients developed postoperative pulmonary complications (PPC). Stepwise logistic regression analysis revealed four independent predictive factors associated with pulmonary complications, including current smoker, lymphocyte count, clotting time, and ASA score. In addition, the RF model had an ideal predictive performance. Conclusions According to our result, current smoker, lymphocyte count, clotting time, and ASA score were independent risks of pulmonary complications. Machine learning approaches can also provide more evidence in the prediction of pulmonary complications.
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Affiliation(s)
- Xiaolei Jing
- Division of Life Sciences and Medicine, Department of Neurosurgery, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, China
| | - Xueqi Wang
- Division of Life Sciences and Medicine, Department of Neurosurgery, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, China
| | - Hongxia Zhuang
- Division of Life Sciences and Medicine, Department of Neurosurgery, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, China
| | - Xiang Fang
- Division of Life Sciences and Medicine, Department of Neurology, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, China
| | - Hao Xu
- Division of Life Sciences and Medicine, Department of Neurosurgery, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, China
- *Correspondence: Hao Xu
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17
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Sheth KN, Yuen MM, Mazurek MH, Cahn BA, Prabhat AM, Salehi S, Shah JT, By S, Welch EB, Sofka M, Sacolick LI, Kim JA, Payabvash S, Falcone GJ, Gilmore EJ, Hwang DY, Matouk C, Gordon-Kundu B, Rn AW, Petersen N, Schindler J, Gobeske KT, Sansing LH, Sze G, Rosen MS, Kimberly WT, Kundu P. Bedside detection of intracranial midline shift using portable magnetic resonance imaging. Sci Rep 2022; 12:67. [PMID: 34996970 PMCID: PMC8742125 DOI: 10.1038/s41598-021-03892-7] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 12/02/2021] [Indexed: 12/17/2022] Open
Abstract
Neuroimaging is crucial for assessing mass effect in brain-injured patients. Transport to an imaging suite, however, is challenging for critically ill patients. We evaluated the use of a low magnetic field, portable MRI (pMRI) for assessing midline shift (MLS). In this observational study, 0.064 T pMRI exams were performed on stroke patients admitted to the neuroscience intensive care unit at Yale New Haven Hospital. Dichotomous (present or absent) and continuous MLS measurements were obtained on pMRI exams and locally available and accessible standard-of-care imaging exams (CT or MRI). We evaluated the agreement between pMRI and standard-of-care measurements. Additionally, we assessed the relationship between pMRI-based MLS and functional outcome (modified Rankin Scale). A total of 102 patients were included in the final study (48 ischemic stroke; 54 intracranial hemorrhage). There was significant concordance between pMRI and standard-of-care measurements (dichotomous, κ = 0.87; continuous, ICC = 0.94). Low-field pMRI identified MLS with a sensitivity of 0.93 and specificity of 0.96. Moreover, pMRI MLS assessments predicted poor clinical outcome at discharge (dichotomous: adjusted OR 7.98, 95% CI 2.07–40.04, p = 0.005; continuous: adjusted OR 1.59, 95% CI 1.11–2.49, p = 0.021). Low-field pMRI may serve as a valuable bedside tool for detecting mass effect.
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Affiliation(s)
- Kevin N Sheth
- Department of Neurology, Yale School of Medicine, 15 York Street, LLCI Room 1003C, P.O. Box 208018, New Haven, CT, 06520, USA.
| | - Matthew M Yuen
- Department of Neurology, Yale School of Medicine, 15 York Street, LLCI Room 1003C, P.O. Box 208018, New Haven, CT, 06520, USA
| | - Mercy H Mazurek
- Department of Neurology, Yale School of Medicine, 15 York Street, LLCI Room 1003C, P.O. Box 208018, New Haven, CT, 06520, USA
| | - Bradley A Cahn
- Department of Neurology, Yale School of Medicine, 15 York Street, LLCI Room 1003C, P.O. Box 208018, New Haven, CT, 06520, USA
| | - Anjali M Prabhat
- Department of Neurology, Yale School of Medicine, 15 York Street, LLCI Room 1003C, P.O. Box 208018, New Haven, CT, 06520, USA
| | | | - Jill T Shah
- Department of Neurology, Yale School of Medicine, 15 York Street, LLCI Room 1003C, P.O. Box 208018, New Haven, CT, 06520, USA
| | | | | | | | | | - Jennifer A Kim
- Department of Neurology, Yale School of Medicine, 15 York Street, LLCI Room 1003C, P.O. Box 208018, New Haven, CT, 06520, USA
| | | | - Guido J Falcone
- Department of Neurology, Yale School of Medicine, 15 York Street, LLCI Room 1003C, P.O. Box 208018, New Haven, CT, 06520, USA
| | - Emily J Gilmore
- Department of Neurology, Yale School of Medicine, 15 York Street, LLCI Room 1003C, P.O. Box 208018, New Haven, CT, 06520, USA
| | - David Y Hwang
- Department of Neurology, Yale School of Medicine, 15 York Street, LLCI Room 1003C, P.O. Box 208018, New Haven, CT, 06520, USA
| | - Charles Matouk
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | - Barbara Gordon-Kundu
- Department of Neurology, Yale School of Medicine, 15 York Street, LLCI Room 1003C, P.O. Box 208018, New Haven, CT, 06520, USA
| | - Adrienne Ward Rn
- Neuroscience Intensive Care Unit, Yale New Haven Hospital, New Haven, CT, USA
| | - Nils Petersen
- Department of Neurology, Yale School of Medicine, 15 York Street, LLCI Room 1003C, P.O. Box 208018, New Haven, CT, 06520, USA
| | - Joseph Schindler
- Department of Neurology, Yale School of Medicine, 15 York Street, LLCI Room 1003C, P.O. Box 208018, New Haven, CT, 06520, USA
| | - Kevin T Gobeske
- Department of Neurology, Yale School of Medicine, 15 York Street, LLCI Room 1003C, P.O. Box 208018, New Haven, CT, 06520, USA
| | - Lauren H Sansing
- Department of Neurology, Yale School of Medicine, 15 York Street, LLCI Room 1003C, P.O. Box 208018, New Haven, CT, 06520, USA
| | - Gordon Sze
- Department of Neuroradiology, Yale School of Medicine, New Haven, CT, USA
| | - Matthew S Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - W Taylor Kimberly
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
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Palekar SG, Jaiswal M, Patil M, Malpathak V. Outcome Prediction in Patients of Traumatic Brain Injury Based on Midline Shift on CT Scan of Brain. INDIAN JOURNAL OF NEUROSURGERY 2021. [DOI: 10.1055/s-0040-1716990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022] Open
Abstract
Abstract
Background Clinicians treating patients with head injury often take decisions based on their assessment of prognosis. Assessment of prognosis could help communication with a patient and the family. One of the most widely used clinical tools for such prediction is the Glasgow coma scale (GCS); however, the tool has a limitation with regard to its use in patients who are under sedation, are intubated, or under the influence of alcohol or psychoactive drugs. CT scan findings such as status of basal cistern, midline shift, associated traumatic subarachnoid hemorrhage (SAH), and intraventricular hemorrhage are useful indicators in predicting outcome and also considered as valid options for prognostication of the patients with traumatic brain injury (TBI), especially in emergency setting.
Materials and Methods 108 patients of head injury were assessed at admission with clinical examination, history, and CT scan of brain. CT findings were classified according to type of lesion and midline shift correlated to GCS score at admission. All the subjects in this study were managed with an identical treatment protocol. Outcome of these patients were assessed on GCS score at discharge.
Results Among patients with severe GCS, 51% had midline shift. The degree of midline shift in CT head was a statistically significant determinant of outcome (p = 0.023). Seventeen out of 48 patients (35.4%) with midline shift had poor outcome as compared with 8 out of 60 patients (13.3%) with no midline shift.
Conclusion In patients with TBI, the degree of midline shift on CT scan was significantly related to the severity of head injury and resulted in poor clinical outcome.
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Affiliation(s)
- Shrikant Govindrao Palekar
- Department of General Surgery, Dr. Vasantrao Pawar Medical College, Hospital & research center, Adgaon, Nasik, India
| | - Manish Jaiswal
- Department of Neurosurgery, King George’s Medical University, Lucknow, Uttar Pradesh, India
| | - Mandar Patil
- Department of Neurosurgery, Tirunelveli Government Medical College, Tamil Nadu, India
| | - Vijay Malpathak
- Department of General Surgery, Dr. Vasantrao Pawar Medical College, Hospital & research center, Adgaon, Nasik, India
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Fiani B, Pasko KBD, Sarhadi K, Covarrubias C. Current uses, emerging applications, and clinical integration of artificial intelligence in neuroradiology. Rev Neurosci 2021; 33:383-395. [PMID: 34506699 DOI: 10.1515/revneuro-2021-0101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 08/18/2021] [Indexed: 11/15/2022]
Abstract
Artificial intelligence (AI) is a branch of computer science with a variety of subfields and techniques, exploited to serve as a deductive tool that performs tasks originally requiring human cognition. AI tools and its subdomains are being incorporated into healthcare delivery for the improvement of medical data interpretation encompassing clinical management, diagnostics, and prognostic outcomes. In the field of neuroradiology, AI manifested through deep machine learning and connected neural networks (CNNs) has demonstrated incredible accuracy in identifying pathology and aiding in diagnosis and prognostication in several areas of neurology and neurosurgery. In this literature review, we survey the available clinical data highlighting the utilization of AI in the field of neuroradiology across multiple neurological and neurosurgical subspecialties. In addition, we discuss the emerging role of AI in neuroradiology, its strengths and limitations, as well as future needs in strengthening its role in clinical practice. Our review evaluated data across several subspecialties of neurology and neurosurgery including vascular neurology, spinal pathology, traumatic brain injury (TBI), neuro-oncology, multiple sclerosis, Alzheimer's disease, and epilepsy. AI has established a strong presence within the realm of neuroradiology as a successful and largely supportive technology aiding in the interpretation, diagnosis, and even prognostication of various pathologies. More research is warranted to establish its full scientific validity and determine its maximum potential to aid in optimizing and providing the most accurate imaging interpretation.
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Affiliation(s)
- Brian Fiani
- Department of Neurosurgery, Desert Regional Medical Center, 1150 N Indian Canyon Dr, Palm Springs, CA, 92262, USA
| | - Kory B Dylan Pasko
- School of Medicine, Georgetown University, 3900 Reservoir Rd NW, Washington, DC, 20007, USA
| | - Kasra Sarhadi
- Department of Neurology, University of Washington, Main Hospital, 325 9th Ave, Seattle, WA, 98104, USA
| | - Claudia Covarrubias
- School of Medicine, Universidad Anáhuac Querétaro, Cto. Universidades I, Fracción 2, 76246 Qro., Querétaro, Mexico
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V. V, Gudigar A, Raghavendra U, Hegde A, Menon GR, Molinari F, Ciaccio EJ, Acharya UR. Automated Detection and Screening of Traumatic Brain Injury (TBI) Using Computed Tomography Images: A Comprehensive Review and Future Perspectives. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:6499. [PMID: 34208596 PMCID: PMC8296416 DOI: 10.3390/ijerph18126499] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 06/07/2021] [Accepted: 06/09/2021] [Indexed: 12/17/2022]
Abstract
Traumatic brain injury (TBI) occurs due to the disruption in the normal functioning of the brain by sudden external forces. The primary and secondary injuries due to TBI include intracranial hematoma (ICH), raised intracranial pressure (ICP), and midline shift (MLS), which can result in significant lifetime disabilities and death. Hence, early diagnosis of TBI is crucial to improve patient outcome. Computed tomography (CT) is the preferred modality of choice to assess the severity of TBI. However, manual visualization and inspection of hematoma and its complications from CT scans is a highly operator-dependent and time-consuming task, which can lead to an inappropriate or delayed prognosis. The development of computer aided diagnosis (CAD) systems could be helpful for accurate, early management of TBI. In this paper, a systematic review of prevailing CAD systems for the detection of hematoma, raised ICP, and MLS in non-contrast axial CT brain images is presented. We also suggest future research to enhance the performance of CAD for early and accurate TBI diagnosis.
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Affiliation(s)
- Vidhya V.
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India;
| | - Anjan Gudigar
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India;
| | - U. Raghavendra
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India;
| | - Ajay Hegde
- Institute of Neurological Sciences, Glasgow G51 4LB, UK;
- Department of Neurosurgery, Kasturba Medical College, Manipal Academy of Higher Education, Manipal 576104, India;
| | - Girish R. Menon
- Department of Neurosurgery, Kasturba Medical College, Manipal Academy of Higher Education, Manipal 576104, India;
| | - Filippo Molinari
- Department of Electronics, Politecnico di Torino, 24 Corso Duca degli Abruzzi, 10129 Torino, Italy;
| | - Edward J. Ciaccio
- Department of Medicine, Columbia University, New York, NY 10032, USA;
| | - U. Rajendra Acharya
- School of Engineering, Ngee Ann Polytechnic, 535 Clementi Road, Singapore 599489, Singapore;
- Department of Biomedical Engineering, School of Science and Technology, SUSS University, 463 Clementi Road, Singapore 599491, Singapore
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
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21
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Hanko M, Grendár M, Snopko P, Opšenák R, Šutovský J, Benčo M, Soršák J, Zeleňák K, Kolarovszki B. Random Forest-Based Prediction of Outcome and Mortality in Patients with Traumatic Brain Injury Undergoing Primary Decompressive Craniectomy. World Neurosurg 2021; 148:e450-e458. [PMID: 33444843 DOI: 10.1016/j.wneu.2021.01.002] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Revised: 01/01/2021] [Accepted: 01/02/2021] [Indexed: 11/19/2022]
Abstract
BACKGROUND Various prognostic models are used to predict mortality and functional outcome in patients after traumatic brain injury with a trend to incorporate machine learning protocols. None of these models is focused exactly on the subgroup of patients indicated for decompressive craniectomy. Evidence regarding efficiency of this surgery is still incomplete, especially in patients undergoing primary decompressive craniectomy with evacuation of traumatic mass lesions. METHODS In a prospective study with a 6-month follow-up period, we assessed postoperative outcome and mortality of 40 patients who underwent primary decompressive craniectomy for traumatic brain injuries during 2018-2019. The results were analyzed in relation to a wide spectrum of preoperatively available demographic, clinical, radiographic, and laboratory data. Random forest algorithms were trained for prediction of both mortality and unfavorable outcome, with their accuracy quantified by area under the receiver operating curves (AUCs) for out-of-bag samples. RESULTS At the end of the follow-up period, we observed mortality of 57.5%. Favorable outcome (Glasgow Outcome Scale [GOS] score 4-5) was achieved by 30% of our patients. Random forest-based prediction models constructed for 6-month mortality and outcome reached a moderate predictive ability, with AUC = 0.811 and AUC = 0.873, respectively. Random forest models trained on handpicked variables showed slightly decreased AUC = 0.787 for 6-month mortality and AUC = 0.846 for 6-month outcome and increased out-of-bag error rates. CONCLUSIONS Random forest algorithms show promising results in prediction of postoperative outcome and mortality in patients undergoing primary decompressive craniectomy. The best performance was achieved by Classification Random forest for 6-month outcome.
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Affiliation(s)
- Martin Hanko
- Clinic of Neurosurgery, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava and University Hospital in Martin, Martin, Slovak Republic.
| | - Marián Grendár
- Bioinformatic Center, Biomedical Center Martin (BioMed), Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovak Republic
| | - Pavol Snopko
- Clinic of Neurosurgery, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava and University Hospital in Martin, Martin, Slovak Republic
| | - René Opšenák
- Clinic of Neurosurgery, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava and University Hospital in Martin, Martin, Slovak Republic
| | - Juraj Šutovský
- Clinic of Neurosurgery, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava and University Hospital in Martin, Martin, Slovak Republic
| | - Martin Benčo
- Clinic of Neurosurgery, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava and University Hospital in Martin, Martin, Slovak Republic
| | - Jakub Soršák
- Clinic of Radiology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava and University Hospital in Martin, Martin, Slovak Republic
| | - Kamil Zeleňák
- Clinic of Radiology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava and University Hospital in Martin, Martin, Slovak Republic
| | - Branislav Kolarovszki
- Clinic of Neurosurgery, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava and University Hospital in Martin, Martin, Slovak Republic
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22
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Wang H, Baker EW, Mandal A, Pidaparti RM, West FD, Kinder HA. Identification of predictive MRI and functional biomarkers in a pediatric piglet traumatic brain injury model. Neural Regen Res 2021; 16:338-344. [PMID: 32859794 PMCID: PMC7896230 DOI: 10.4103/1673-5374.290915] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Traumatic brain injury (TBI) at a young age can lead to the development of long-term functional impairments. Severity of injury is well demonstrated to have a strong influence on the extent of functional impairments; however, identification of specific magnetic resonance imaging (MRI) biomarkers that are most reflective of injury severity and functional prognosis remain elusive. Therefore, the objective of this study was to utilize advanced statistical approaches to identify clinically relevant MRI biomarkers and predict functional outcomes using MRI metrics in a translational large animal piglet TBI model. TBI was induced via controlled cortical impact and multiparametric MRI was performed at 24 hours and 12 weeks post-TBI using T1-weighted, T2-weighted, T2-weighted fluid attenuated inversion recovery, diffusion-weighted imaging, and diffusion tensor imaging. Changes in spatiotemporal gait parameters were also assessed using an automated gait mat at 24 hours and 12 weeks post-TBI. Principal component analysis was performed to determine the MRI metrics and spatiotemporal gait parameters that explain the largest sources of variation within the datasets. We found that linear combinations of lesion size and midline shift acquired using T2-weighted imaging explained most of the variability of the data at both 24 hours and 12 weeks post-TBI. In addition, linear combinations of velocity, cadence, and stride length were found to explain most of the gait data variability at 24 hours and 12 weeks post-TBI. Linear regression analysis was performed to determine if MRI metrics are predictive of changes in gait. We found that both lesion size and midline shift are significantly correlated with decreases in stride and step length. These results from this study provide an important first step at identifying relevant MRI and functional biomarkers that are predictive of functional outcomes in a clinically relevant piglet TBI model. This study was approved by the University of Georgia Institutional Animal Care and Use Committee (AUP: A2015 11-001) on December 22, 2015.
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Affiliation(s)
- Hongzhi Wang
- Department of Statistics, University of Georgia, Athens, GA, USA
| | - Emily W Baker
- Regenerative Bioscience Center; Department of Animal and Dairy Science, University of Georgia, Athens, GA, USA
| | - Abhyuday Mandal
- Department of Statistics, University of Georgia, Athens, GA, USA
| | | | - Franklin D West
- Regenerative Bioscience Center; Department of Animal and Dairy Science, University of Georgia, Athens, GA, USA
| | - Holly A Kinder
- Regenerative Bioscience Center; Department of Animal and Dairy Science, University of Georgia, Athens, GA, USA
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23
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Dobran M, Mancini F, Nasi D, Lattanzi S, Fogante M, Tagliati C, Gesuita R, Polonara G, Iacoangeli M. Relationship between Burr-Hole position and pneumocephalus in patients operated for chronic subdural hematoma. INTERDISCIPLINARY NEUROSURGERY 2020. [DOI: 10.1016/j.inat.2020.100759] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
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24
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Using trauma registry data to predict prolonged mechanical ventilation in patients with traumatic brain injury: Machine learning approach. PLoS One 2020; 15:e0235231. [PMID: 32639971 PMCID: PMC7343348 DOI: 10.1371/journal.pone.0235231] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Accepted: 06/10/2020] [Indexed: 12/16/2022] Open
Abstract
OBJECTIVES We aimed to build a machine learning predictive model to predict the risk of prolonged mechanical ventilation (PMV) for patients with Traumatic Brain Injury (TBI). METHODS This study included TBI patients who were hospitalized in a level 1 trauma center between January 2014 and February 2019. Data were analyzed for all adult patients who received mechanical ventilation following TBI with abbreviated injury severity (AIS) score for the head region of ≥ 3. This study designed three sets of machine learning models: set A defined PMV to be greater than 7 days, set B (PMV > 10 days) and set C (PMV >14 days) to determine the optimal model for deployment. Patients' demographics, injury characteristics and CT findings were used as predictors. Logistic regression (LR), Artificial neural networks (ANN) Support vector machines (SVM), Random Forest (RF) and C.5 Decision Tree (C.5 DT) were used to predict the PMV. RESULTS The number of eligible patients that were included in the study were 674, 643 and 622 patients in sets A, B and C respectively. In set A, LR achieved the optimal performance with accuracy 0.75 and Area under the curve (AUC) 0.83. SVM achieved the optimal performance among other models in sets B with accuracy/AUC of 0.79/0.84 respectively. ANNs achieved the optimal performance in set C with accuracy/AUC of 0.76/0.72 respectively. Machine learning models in set B demonstrated more stable performance with higher prediction success and discrimination power. CONCLUSION This study not only provides evidence that machine learning methods outperform the traditional multivariate analytical methods, but also provides a perspective to reach a consensual definition of PMV.
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25
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Tsitsopoulos PP, Marklund N, Rostami E, Enblad P, Hillered L. Association of the bleeding time test with aspects of traumatic brain injury in patients with alcohol use disorder. Acta Neurochir (Wien) 2020; 162:1597-1606. [PMID: 32424564 PMCID: PMC7232602 DOI: 10.1007/s00701-020-04373-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Accepted: 03/29/2020] [Indexed: 12/17/2022]
Abstract
BACKGROUND-AIM Traumatic brain injury (TBI) and alcohol use disorder (AUD) can occur concomitantly and be associated with coagulopathy that influences TBI outcome. The use of bleeding time tests in TBI management is controversial. We hypothesized that in TBI patients with AUD, a prolonged bleeding time is associated with more severe injury and poor outcome. MATERIAL AND METHODS Moderate and severe TBI patients with evidence of AUD were examined with bleeding time according to IVY bleeding time on admission during neurointensive care. Baseline clinical and radiological characteristics were recorded. A standardized IVY bleeding time test was determined by staff trained in the procedure. Bleeding time test results were divided into normal (≤ 600 s), prolonged (> 600 s), and markedly prolonged (≥ 900 s). Normal platelet count (PLT) was defined as > 150,000/μL. This cohort was compared with another group of TBI patients without evidence of AUD. RESULTS Fifty-two patients with TBI and AUD were identified, and 121 TBI patients without any history of AUD were used as controls. PLT was low in 44.2% and bleeding time was prolonged in 69.2% of patients. Bleeding time values negatively correlated with PLT (p < 0.05). TBI patients with markedly prolonged values (≥ 900 s) had significantly increased hematoma size, and more frequently required intracranial pressure measurement and mechanical ventilation compared with those with bleeding times < 900 s (p < 0.05). Most patients (88%) with low platelet count had prolonged bleeding time. No difference in 6-month outcome between the bleeding time groups was observed (p > 0.05). Subjects with TBI and no evidence for AUD had lower bleeding time values and higher platelet count compared with those with TBI and history of AUD (p < 0.05). CONCLUSIONS Although differences in the bleeding time values between TBI cohorts exist and prolonged values may be seen even in patients with normal platelet count, the bleeding test is a marker of primary hemostasis and platelet function with low specificity. However, it may provide an additional assessment in the interpretation of the overall status of TBI patients with AUD. Therefore, the bleeding time test should only be used in combination with the patient's bleeding history and careful assessment of other hematologic parameters.
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Affiliation(s)
- P P Tsitsopoulos
- Department of Neuroscience, Section of Neurosurgery, Uppsala University, Uppsala, Sweden.
| | - N Marklund
- Department of Neuroscience, Section of Neurosurgery, Uppsala University, Uppsala, Sweden
- Department of Clinical Sciences Lund, Neurosurgery, Skåne University Hospital, Lund University, Lund, Sweden
| | - E Rostami
- Department of Neuroscience, Section of Neurosurgery, Uppsala University, Uppsala, Sweden
| | - P Enblad
- Department of Neuroscience, Section of Neurosurgery, Uppsala University, Uppsala, Sweden
| | - L Hillered
- Department of Neuroscience, Section of Neurosurgery, Uppsala University, Uppsala, Sweden
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26
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Hiraizumi S, Shiomi N, Echigo T, Oka H, Hino A, Baba M, Hitosugi M. Factors Associated with Poor Outcomes in Patients with Mild or Moderate Acute Subdural Hematomas. Neurol Med Chir (Tokyo) 2020; 60:402-410. [PMID: 32565532 PMCID: PMC7431873 DOI: 10.2176/nmc.oa.2020-0030] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
The factors influencing the outcomes of mild/moderate acute subdural hematoma (ASDH) are still unclear. Retrospective analyses were performed to identify such factors. The medical records of all patients who were admitted to Saiseikai Shiga Hospital with mild (Glasgow Coma Scale [GCS] score of 14–15) or moderate (GCS score of 9–13) ASDH between April 2008 and March 2017 were reviewed. Comparisons between the patients who exhibited favorable and poor outcomes were performed. Then, independent factors that contributed to poor outcomes were identified via logistic regression analyses. A total of 266 patients with a mean age of 70.2 were included in this study. The most common concomitant injuries were subarachnoid hemorrhages (SAHs; 56.8%). The patients’ Injury Severity Scores (ISS) ranged from 16 to 75 (median: 21). The 66 moderate ASDH patients exhibited significantly higher frequencies of surgery and mortality (24.2% and 13.6%, respectively) than the 200 mild ASDH patients (8.0% and 4.5%, respectively). The factors associated with poor outcomes were age (odds ratio [OR]: 1.06) and the ISS (OR: 1.24) in the mild ASDH patients, and older age (OR: 1.09) and the higher ISS (OR: 1.15) in the moderate group, too.
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Affiliation(s)
- Shiho Hiraizumi
- Emergency and Critical Care Medicine, Saiseikai Shiga Hospital.,Department of Legal Medicine, Shiga University of Medical Science
| | - Naoto Shiomi
- Emergency and Critical Care Medicine, Saiseikai Shiga Hospital
| | - Tadashi Echigo
- Emergency and Critical Care Medicine, Saiseikai Shiga Hospital
| | - Hideki Oka
- Department of Neurosurgery, Saiseikai Shiga Hospital
| | - Akihiko Hino
- Department of Neurosurgery, Saiseikai Shiga Hospital
| | - Mineko Baba
- Center for Integrated Medical Research, Keio University School of Medicine
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27
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Yao H, Williamson C, Gryak J, Najarian K. Automated hematoma segmentation and outcome prediction for patients with traumatic brain injury. Artif Intell Med 2020; 107:101910. [PMID: 32828449 DOI: 10.1016/j.artmed.2020.101910] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Revised: 05/04/2020] [Accepted: 06/09/2020] [Indexed: 11/18/2022]
Abstract
Traumatic brain injury (TBI) is a major cause of death and disability worldwide. Automated brain hematoma segmentation and outcome prediction for patients with TBI can effectively facilitate patient management. In this study, we propose a novel Multi-view convolutional neural network with a mixed loss to segment total acute hematoma on head CT scans collected within 24 h after the injury. Based on the automated segmentation, the volumetric distribution and shape characteristics of the hematoma were extracted and combined with other clinical observations to predict 6-month mortality. The proposed hematoma segmentation network achieved an average Dice coefficient of 0.697 and an intraclass correlation coefficient of 0.966 between the volumes estimated from the predicted hematoma segmentation and volumes of the annotated hematoma segmentation on the test set. Compared with other published methods, the proposed method has the most accurate segmentation performance and volume estimation. For 6-month mortality prediction, the model achieved an average area under the precision-recall curve (AUCPR) of 0.559 and area under the receiver operating characteristic curve (AUC) of 0.853 using 10-fold cross-validation on a dataset consisting of 828 patients. The average AUCPR and AUC of the proposed model are respectively more than 10% and 5% higher than those of the widely used IMPACT model.
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Affiliation(s)
- Heming Yao
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
| | - Craig Williamson
- Department of Neurosurgery, University of Michigan, Ann Arbor, MI, USA.
| | - Jonathan Gryak
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
| | - Kayvan Najarian
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA; Michigan Center for Integrative Research in Critical Care University of Michigan, Ann Arbor, MI, USA; Department of Emergency Medicine, University of Michigan, Ann Arbor, MI, USA.
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28
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Bodanapally UK, Shanmuganathan K, Parikh GY, Schwartzbauer G, Kondaveti R, Feiter TR. Quantification of Iodine Leakage on Dual-Energy CT as a Marker of Blood-Brain Barrier Permeability in Traumatic Hemorrhagic Contusions: Prediction of Surgical Intervention for Intracranial Pressure Management. AJNR Am J Neuroradiol 2019; 40:2059-2065. [PMID: 31727752 PMCID: PMC6975368 DOI: 10.3174/ajnr.a6316] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Accepted: 09/30/2019] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Hemorrhagic contusions are associated with iodine leakage. We aimed to identify quantitative iodine-based dual-energy CT variables that correlate with the type of intracranial pressure management. MATERIALS AND METHODS Consecutive patients with contusions from May 2016 through January 2017 were retrospectively analyzed. Radiologists, blinded to the outcomes, evaluated CT variables from unenhanced admission and short-term follow-up head dual-energy CT scans obtained after contrast-enhanced whole-body CT. Treatment intensity of intracranial pressure was broadly divided into 2 groups: those managed medically and those managed surgically. Univariable analysis followed by logistic regression was used to develop a prediction model. RESULTS The study included 65 patients (50 men; median age, 48 years; Q1 to Q3, 25-65.5 years). Twenty-one patients were managed surgically (14 by CSF drainage, 7 by craniectomy). Iodine-based variables that correlated with surgical management were higher iodine concentration, pseudohematoma volume, iodine quantity in pseudohematoma, and iodine quantity in contusions. The regression model developed after inclusion of clinical variables identified 3 predictor variables: postresuscitation Glasgow Coma Scale (adjusted OR = 0.55; 95% CI, 0.38-0.79; P = .001), age (adjusted OR = 0.9; 95% CI, 0.85-0.97; P = .003), and pseudohematoma volume (adjusted OR = 2.05; 95% CI, 1.1-3.77; P = .02), which yielded an area under the curve of 0.96 in predicting surgical intracranial pressure management. The 2 predictors for craniectomy were age (adjusted OR = 0.89; 95% CI, 0.81-0.99; P = .03) and pseudohematoma volume (adjusted OR = 1.23; 95% CI, 1.03-1.45; P = .02), which yielded an area under the curve of 0.89. CONCLUSIONS Quantitative iodine-based parameters derived from follow-up dual-energy CT may predict the intensity of intracranial pressure management in patients with hemorrhagic contusions.
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Affiliation(s)
- U K Bodanapally
- From the Departments of Diagnostic Radiology and Nuclear Medicine (U.K.B., K.S., T.R.F.)
| | - K Shanmuganathan
- From the Departments of Diagnostic Radiology and Nuclear Medicine (U.K.B., K.S., T.R.F.)
| | | | - G Schwartzbauer
- Neurosurgery (G.S.), R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, Baltimore, Maryland
| | - R Kondaveti
- Kasturba Medical College (R.K.), Mangaluru, India
| | - T R Feiter
- From the Departments of Diagnostic Radiology and Nuclear Medicine (U.K.B., K.S., T.R.F.)
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29
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Jiang C, Cao J, Williamson C, Farzaneh N, Rajajee V, Gryak J, Najarian K, Soroushmehr SMR. Midline Shift vs. Mid-Surface Shift: Correlation with Outcome of Traumatic Brain Injuries. PROCEEDINGS. IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE 2019; 2019:1083-1086. [PMID: 33569243 PMCID: PMC7871460 DOI: 10.1109/bibm47256.2019.8983159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Traumatic brain injury (TBI) is a major health and socioeconomic problem globally that is associated with a high level of mortality. Early and accurate diagnosis and prognosis of TBI is important in patient management and preventing any secondary injuries. Computer tomography (CT) imaging assists physicians in diagnosing injury and guiding treatment. One of the clinical parameters extracted from CT images is midline shift, a measure of linear displacement in brain structure, which is correlated with TBI patient outcomes. However, only a tiny fraction of the overall tissue displacement is quantified through this parameter. In this paper, a novel measurement of overall mid-surface shift is proposed that quantifies the total volume of brain tissue shifted across the midline. When compared to traditional midline shift, mid-surface shift has a stronger correlation with TBI patient outcomes.
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Affiliation(s)
- Cheng Jiang
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI
| | - Jie Cao
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI
| | - Craig Williamson
- Department of Neurosurgery, University of Michigan, Ann Arbor, MI
- Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, MI
| | - Negar Farzaneh
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI
| | - Venkatakrishna Rajajee
- Department of Neurosurgery, University of Michigan, Ann Arbor, MI
- Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, MI
| | - Jonathan Gryak
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI
| | - Kayvan Najarian
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI
- Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, MI
- Department of Emergency Medicine, University of Michigan, Ann Arbor, MI
| | - S M Reza Soroushmehr
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI
- Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, MI
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30
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Yao H, Williamson C, Soroushmehr R, Gryak J, Najarian K. Hematoma Segmentation Using Dilated Convolutional Neural Network. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2018:5902-5905. [PMID: 30441679 DOI: 10.1109/embc.2018.8513648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Traumatic brain injury (TBI) is a global health challenge. Accurate and fast automatic detection of hematoma in the brain is essential for TBI diagnosis and treatment. In this study, we developed a fully automated system to detect and segment hematoma regions in head Computed Tomography (CT) images of patients with acute TBI. We adapted the structure of a fully convolutional network by introducing dilated convolution and removing down-sampling and up-sampling layers. Skip layers are also used to combine low-level features and high-level features. By integrating the information from different scales without losing spatial resolution, the network can perform more accurate segmentation. Our final hematoma segmentations achieved the Dice, sensitivity, and specificity of 0.62, 0.81, and 0.96, respectively, which outperformed the results from previous methods.
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31
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Chitinase-3-Like Protein 1, Serum Amyloid A1, C-Reactive Protein, and Procalcitonin Are Promising Biomarkers for Intracranial Severity Assessment of Traumatic Brain Injury: Relationship with Glasgow Coma Scale and Computed Tomography Volumetry. World Neurosurg 2019; 134:e120-e143. [PMID: 31606503 DOI: 10.1016/j.wneu.2019.09.143] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 09/24/2019] [Accepted: 09/26/2019] [Indexed: 11/23/2022]
Abstract
OBJECTIVE The volume and location of intracranial hematomas are well-known prognostic factors for traumatic brain injury. The aim of this study was to determine the relationship of serum biomarkers S100β, glial fibrillary acidic protein, neuron-specific enolase, total tau, phosphorylated neurofilament heavy chain, serum amyloid A1 (SAA1), C-reactive protein, procalcitonin (PCT), and chitinase-3-like protein 1 (YKL-40) with traumatic brain injury severity and the amount and location of hemorrhagic traumatic lesions. METHODS A prospective observational cohort of 115 patients with a Glasgow Coma Scale (GCS) score of 3-15 were evaluated. Intracranial lesion volume was measured from the semiautomatic segmentation of hematoma on computed tomography using Analyze software. The establishment of possible biomarker cutoff points for intracranial lesion detection was estimated using the Youden Index (J) obtained from the area under the receiver operating characteristic curve. RESULTS SAA1, YKL-40, PCT, and S100β showed the most robust association with level of consciousness, both with total GCS and motor score. Biomarkers significantly correlated with volumetric measurements of subdural hematoma, traumatic subarachnoid hemorrhage, intraparenchymal hemorrhage, intraventricular hemorrhage, and total amount of bleeding. The type of intracranial hemorrhage was associated with various release patterns of neurobiochemical markers. CONCLUSIONS YKL-40, SAA1, C-reactive protein, and PCT combined with S100β were the most promising biomarkers to determine the presence, location, and extent of traumatic intracranial lesions. Combination of biomarkers further increased the discriminatory capacity for the detection of intracranial bleeding.
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32
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Puffer RC, Yue JK, Mesley M, Billigen JB, Sharpless J, Fetzick AL, Puccio A, Diaz-Arrastia R, Okonkwo DO. Long-term outcome in traumatic brain injury patients with midline shift: a secondary analysis of the Phase 3 COBRIT clinical trial. J Neurosurg 2019; 131:596-603. [PMID: 30074459 DOI: 10.3171/2018.2.jns173138] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2017] [Accepted: 02/16/2018] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Following traumatic brain injury (TBI), midline shift of the brain at the level of the septum pellucidum is often caused by unilateral space-occupying lesions and is associated with increased intracranial pressure and worsened morbidity and mortality. While outcome has been studied in this population, the recovery trajectory has not been reported in a large cohort of patients with TBI. The authors sought to utilize the Citicoline Brain Injury Treatment (COBRIT) trial to analyze patient recovery over time depending on degree of midline shift at presentation. METHODS Patient data from the COBRIT trial were stratified into 4 groups of midline shift, and outcome measures were analyzed at 30, 90, and 180 days postinjury. A recovery trajectory analysis was performed identifying patients with outcome measures at all 3 time points to analyze the degree of recovery based on midline shift at presentation. RESULTS There were 892, 1169, and 895 patients with adequate outcome data at 30, 90, and 180 days, respectively. Rates of favorable outcome (Glasgow Outcome Scale-Extended [GOS-E] scores 4-8) at 6 months postinjury were 87% for patients with no midline shift, 79% for patients with 1-5 mm of shift, 64% for patients with 6-10 mm of shift, and 47% for patients with > 10 mm of shift. The mean improvement from unfavorable outcome (GOS-E scores 2 and 3) to favorable outcome (GOS-E scores 4-8) from 1 month to 6 months in all groups was 20% (range 4%-29%). The mean GOS-E score for patients in the 6- to 10-mm group crossed from unfavorable outcome (GOS-E scores 2 and 3) into favorable outcome (GOS-E scores 4-8) at 90 days, and the mean GOS-E of patients in the > 10-mm group nearly reached the threshold of favorable outcome by 180 days postinjury. CONCLUSIONS In this secondary analysis of the Phase 3 COBRIT trial, TBI patients with less than 10 mm of midline shift on admission head CT had significantly improved functional outcomes through 180 days after injury compared with those with greater than 10 mm of midline shift. Of note, nearly 50% of patients with > 10 mm of midline shift achieved a favorable outcome (GOS-E score 4-8) by 6 months postinjury.
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Affiliation(s)
- Ross C Puffer
- 1Department of Neurosurgery, Mayo Clinic, Rochester, Minnesota
| | - John K Yue
- 2Department of Neurosurgery, UPMC, Pittsburgh; and
| | | | | | | | | | - Ava Puccio
- 2Department of Neurosurgery, UPMC, Pittsburgh; and
| | - Ramon Diaz-Arrastia
- 3Department of Neurosurgery, University of Pennsylvania, Philadelphia, Pennsylvania
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Kinder HA, Baker EW, Wang S, Fleischer CC, Howerth EW, Duberstein KJ, Mao H, Platt SR, West FD. Traumatic Brain Injury Results in Dynamic Brain Structure Changes Leading to Acute and Chronic Motor Function Deficits in a Pediatric Piglet Model. J Neurotrauma 2019; 36:2930-2942. [PMID: 31084386 DOI: 10.1089/neu.2018.6303] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
Abstract
Traumatic brain injury (TBI) is a leading cause of death and disability in children. Pediatric TBI patients often suffer from crippling cognitive, emotional, and motor function deficits that have negative lifelong effects. The objective of this study was to longitudinally assess TBI pathophysiology using multi-parametric magnetic resonance imaging (MRI), gait analysis, and histological approaches in a pediatric piglet model. TBI was produced by controlled cortical impact in Landrace piglets. MRI data, including from proton magnetic resonance spectroscopy (MRS), were collected 24 hours and 12 weeks post-TBI, and gait analysis was performed at multiple time-points over 12 weeks post-TBI. A subset of animals was sacrificed 24 hours, 1 week, 4 weeks, and 12 weeks post-TBI for histological analysis. MRI results demonstrated that TBI led to a significant brain lesion and midline shift as well as microscopic tissue damage with altered brain diffusivity, decreased white matter integrity, and reduced cerebral blood flow. MRS showed a range of neurochemical changes after TBI. Histological analysis revealed neuronal loss, astrogliosis/astrocytosis, and microglia activation. Further, gait analysis showed transient impairments in cadence, cycle time, % stance, step length, and stride length, as well as long-term impairments in weight distribution after TBI. Taken together, this study illustrates the distinct time course of TBI pathoanatomic and functional responses up to 12 weeks post-TBI in a piglet TBI model. The study of TBI injury and recovery mechanisms, as well as the testing of therapeutics in this translational model, are likely to be more predictive of human responses and clinical outcomes compared to traditional small animal models.
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Affiliation(s)
- Holly A Kinder
- Regenerative Bioscience Center, University of Georgia, Athens, Georgia.,Department of Animal and Dairy Science, University of Georgia, Athens, Georgia
| | - Emily W Baker
- Regenerative Bioscience Center, University of Georgia, Athens, Georgia.,Department of Animal and Dairy Science, University of Georgia, Athens, Georgia
| | - Silun Wang
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, Georgia
| | - Candace C Fleischer
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, Georgia
| | - Elizabeth W Howerth
- Regenerative Bioscience Center, University of Georgia, Athens, Georgia.,Department of Pathology, University of Georgia, Athens, Georgia
| | - Kylee J Duberstein
- Regenerative Bioscience Center, University of Georgia, Athens, Georgia.,Department of Animal and Dairy Science, University of Georgia, Athens, Georgia
| | - Hui Mao
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, Georgia
| | - Simon R Platt
- Regenerative Bioscience Center, University of Georgia, Athens, Georgia.,Department of Small Animal Medicine and Surgery, University of Georgia, Athens, Georgia
| | - Franklin D West
- Regenerative Bioscience Center, University of Georgia, Athens, Georgia.,Department of Animal and Dairy Science, University of Georgia, Athens, Georgia
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Jain S, Vyvere TV, Terzopoulos V, Sima DM, Roura E, Maas A, Wilms G, Verheyden J. Automatic Quantification of Computed Tomography Features in Acute Traumatic Brain Injury. J Neurotrauma 2019; 36:1794-1803. [PMID: 30648469 PMCID: PMC6551991 DOI: 10.1089/neu.2018.6183] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Traumatic brain injury is a complex and diverse medical condition with a high frequency of intracranial abnormalities. These can typically be visualized on a computed tomography (CT) scan, which provides important information for further patient management, such as the need for operative intervention. In order to quantify the extent of acute intracranial lesions and associated secondary injuries, such as midline shift and cisternal compression, visual assessment of CT images has limitations, including observer variability and lack of quantitative interpretation. Automated image analysis can quantify the extent of intracranial abnormalities and provide added value in routine clinical practice. In this article, we present icobrain, a fully automated method that reliably computes acute intracranial lesions volume based on deep learning, cistern volume, and midline shift on the noncontrast CT image of a patient. The accuracy of our method is evaluated on a subset of the multi-center data set from the CENTER-TBI (Collaborative European Neurotrauma Effectiveness Research in Traumatic Brain Injury) study for which expert annotations were used as a reference. Median volume differences between expert assessments and icobrain are 0.07 mL for acute intracranial lesions and -0.01 mL for cistern segmentation. Correlation between expert assessments and icobrain is 0.91 for volume of acute intracranial lesions and 0.94 for volume of the cisterns. For midline shift computations, median error is -0.22 mm, with a correlation of 0.93 with expert assessments.
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Affiliation(s)
- Saurabh Jain
- Research and Development, icometrix, Leuven, Belgium
| | - Thijs Vande Vyvere
- Research and Development, icometrix, Leuven, Belgium
- Department of Radiology, Antwerp University Hospital and University of Antwerp, Antwerp, Belgium
| | | | | | - Eloy Roura
- Research and Development, icometrix, Leuven, Belgium
| | - Andrew Maas
- Department of Neurosurgery, Antwerp University Hospital and University of Antwerp, Antwerp, Belgium
| | - Guido Wilms
- Research and Development, icometrix, Leuven, Belgium
- Department of Radiology, UZ Leuven, Leuven, Belgium
| | - Jan Verheyden
- Research and Development, icometrix, Leuven, Belgium
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Final outcome trends in severe traumatic brain injury: a 25-year analysis of single center data. Acta Neurochir (Wien) 2018; 160:2291-2302. [PMID: 30377831 DOI: 10.1007/s00701-018-3705-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2018] [Accepted: 10/16/2018] [Indexed: 01/03/2023]
Abstract
BACKGROUND Evidence from the last 25 years indicates a modest reduction of mortality after severe traumatic head injury (sTBI). This study evaluates the variation over time of the whole Glasgow Outcome Scale (GOS) throughout those years. METHODS The study is an observational cohort study of adults (≥ 15 years old) with closed sTBI (GCS ≤ 8) who were admitted within 48 h after injury. The final outcome was the 1-year GOS, which was divided as follows: (1) dead/vegetative, (2) severely disabled (dependent patients), and (3) good/moderate recovery (independent patients). Patients were treated uniformly according to international protocols in a dedicated ICU. We considered patient characteristics that were previously identified as important predictors and could be determined easily and reliably. The admission years were divided into three intervals (1987-1995, 1996-2004, and 2005-2012), and the following individual CT characteristics were noted: the presence of traumatic subarachnoid or intraventricular hemorrhage (tSAH, IVH), midline shift, cisternal status, and the volume of mass lesions (A × B × C/2). Ordinal logistic regression was performed to estimate associations between predictors and outcomes. The patients' estimated propensity scores were included as an independent variable in the ordinal logistic regression model (TWANG R package). FINDINGS The variables associated with the outcome were age, pupils, motor score, deterioration, shock, hypoxia, cistern status, IVH, tSAH, and epidural volume. When adjusting for those variables and the propensity score, we found a reduction in mortality from 55% (1987-1995) to 38% (2005-2012), but we discovered an increase in dependent patients from 10 to 21% and just a modest increase in independent patients of 6%. CONCLUSIONS This study covers 25 years of management of sTBI in a single neurosurgical center. The prognostic factors are similar to those in the literature. The improvement in mortality does not translate to better quality of life.
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36
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Dubinski D, Won SY, Behmanesh B, Brawanski N, Geisen C, Seifert V, Senft C, Konczalla J. The clinical relevance of ABO blood type in 100 patients with acute subdural hematoma. PLoS One 2018; 13:e0204331. [PMID: 30286106 PMCID: PMC6171832 DOI: 10.1371/journal.pone.0204331] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2018] [Accepted: 09/06/2018] [Indexed: 11/19/2022] Open
Abstract
OBJECTIVE The correlation of depleted blood through midline shift in acute subdural hematoma remains the most reliable clinical predictor to date. On the other hand, patient's ABO blood type has a profound impact on coagulation and hemostasis. We conducted this study to evaluate the role of patient's blood type in terms of incidence, clinical course and outcome after acute subdural hematoma bleeding. METHODS 100 patients with acute subdural hematoma treated between 2010 and 2015 at the author's institution were included. Baseline characteristics and clinical findings including Glasgow coma scale, Glasgow outcome scale, hematoma volume, rebleeding, midline shift, postoperative seizures and the presence of anticoagulation were analyzed for their association with ABO blood type. RESULTS Patient's with blood type O were found to have a lower midline shift (p<0.01) and significantly less seizures (OR: 0.43; p<0.05) compared to non-O patients. Furthermore, patients with blood type A had the a significantly higher midline shift (p<0.05) and a significantly increased risk for postoperative seizures (OR: 4.01; p<0.001). There was no difference in ABO blood type distribution between acute subdural hematoma patients and the average population. CONCLUSION The ABO blood type has significant influence on acute subdural hematoma sequelae. Patient's with blood type O benefit in their clinical course after acute subdural hematoma whereas blood type A patients are at highest risk for increased midline shift and postoperative seizures. Further studies elucidating the biological mechanisms of blood type depended hemostaseology and its role in acute subdural hematoma are required for the development of an appropriate intervention.
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Affiliation(s)
- Daniel Dubinski
- Department of Neurosurgery, University Hospital, Goethe University, Frankfurt, Germany
| | - Sae-Yeon Won
- Department of Neurosurgery, University Hospital, Goethe University, Frankfurt, Germany
| | - Bedjan Behmanesh
- Department of Neurosurgery, University Hospital, Goethe University, Frankfurt, Germany
| | - Nina Brawanski
- Department of Neurosurgery, University Hospital, Goethe University, Frankfurt, Germany
| | - Christof Geisen
- Institute for Transfusion Medicine and Immunohematology, Goethe University, Frankfurt, Germany
| | - Volker Seifert
- Department of Neurosurgery, University Hospital, Goethe University, Frankfurt, Germany
| | - Christian Senft
- Department of Neurosurgery, University Hospital, Goethe University, Frankfurt, Germany
| | - Juergen Konczalla
- Department of Neurosurgery, University Hospital, Goethe University, Frankfurt, Germany
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Gerzanich V, Stokum JA, Ivanova S, Woo SK, Tsymbalyuk O, Sharma A, Akkentli F, Imran Z, Aarabi B, Sahuquillo J, Simard JM. Sulfonylurea Receptor 1, Transient Receptor Potential Cation Channel Subfamily M Member 4, and KIR6.2:Role in Hemorrhagic Progression of Contusion. J Neurotrauma 2018; 36:1060-1079. [PMID: 30160201 PMCID: PMC6446209 DOI: 10.1089/neu.2018.5986] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
In severe traumatic brain injury (TBI), contusions often are worsened by contusion expansion or hemorrhagic progression of contusion (HPC), which may double the original contusion volume and worsen outcome. In humans and rodents with contusion-TBI, sulfonylurea receptor 1 (SUR1) is upregulated in microvessels and astrocytes, and in rodent models, blockade of SUR1 with glibenclamide reduces HPC. SUR1 does not function by itself, but must co-assemble with either KIR6.2 or transient receptor potential cation channel subfamily M member 4 (TRPM4) to form KATP (SUR1-KIR6.2) or SUR1-TRPM4 channels, with the two having opposite effects on membrane potential. Both KIR6.2 and TRPM4 are reportedly upregulated in TBI, especially in astrocytes, but the identity and function of SUR1-regulated channels post-TBI is unknown. Here, we analyzed human and rat brain tissues after contusion-TBI to characterize SUR1, TRPM4, and KIR6.2 expression, and in the rat model, to examine the effects on HPC of inhibiting expression of the three subunits using intravenous antisense oligodeoxynucleotides (AS-ODN). Glial fibrillary acidic protein (GFAP) immunoreactivity was used to operationally define core versus penumbral tissues. In humans and rats, GFAP-negative core tissues contained microvessels that expressed SUR1 and TRPM4, whereas GFAP-positive penumbral tissues contained astrocytes that expressed all three subunits. Förster resonance energy transfer imaging demonstrated SUR1-TRPM4 heteromers in endothelium, and SUR1-TRPM4 and SUR1-KIR6.2 heteromers in astrocytes. In rats, glibenclamide as well as AS-ODN targeting SUR1 and TRPM4, but not KIR6.2, reduced HPC at 24 h post-TBI. Our findings demonstrate upregulation of SUR1-TRPM4 and KATP after contusion-TBI, identify SUR1-TRPM4 as the primary molecular mechanism that accounts for HPC, and indicate that SUR1-TRPM4 is a crucial target of glibenclamide.
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Affiliation(s)
- Volodymyr Gerzanich
- 1 Department of Neurosurgery, University of Maryland School of Medicine, Baltimore, Maryland
| | - Jesse A Stokum
- 1 Department of Neurosurgery, University of Maryland School of Medicine, Baltimore, Maryland
| | - Svetlana Ivanova
- 1 Department of Neurosurgery, University of Maryland School of Medicine, Baltimore, Maryland
| | - Seung Kyoon Woo
- 1 Department of Neurosurgery, University of Maryland School of Medicine, Baltimore, Maryland
| | - Orest Tsymbalyuk
- 1 Department of Neurosurgery, University of Maryland School of Medicine, Baltimore, Maryland
| | - Amit Sharma
- 1 Department of Neurosurgery, University of Maryland School of Medicine, Baltimore, Maryland
| | - Fatih Akkentli
- 1 Department of Neurosurgery, University of Maryland School of Medicine, Baltimore, Maryland
| | - Ziyan Imran
- 1 Department of Neurosurgery, University of Maryland School of Medicine, Baltimore, Maryland
| | - Bizhan Aarabi
- 1 Department of Neurosurgery, University of Maryland School of Medicine, Baltimore, Maryland
| | - Juan Sahuquillo
- 2 Neurotraumatology and Neurosurgery Research Unit, Vall d'Hebron University Hospital, Universitat Autònoma de Barcelona, Barcelona, Spain.,3 Department of Neurosurgery, Vall d'Hebron University Hospital, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - J Marc Simard
- 1 Department of Neurosurgery, University of Maryland School of Medicine, Baltimore, Maryland.,4 Department of Pathology, University of Maryland School of Medicine, Baltimore, Maryland.,5 Department of Physiology, University of Maryland School of Medicine, Baltimore, Maryland
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Liao CC, Chen YF, Xiao F. Brain Midline Shift Measurement and Its Automation: A Review of Techniques and Algorithms. Int J Biomed Imaging 2018; 2018:4303161. [PMID: 29849536 PMCID: PMC5925103 DOI: 10.1155/2018/4303161] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Accepted: 03/04/2018] [Indexed: 11/17/2022] Open
Abstract
Midline shift (MLS) of the brain is an important feature that can be measured using various imaging modalities including X-ray, ultrasound, computed tomography, and magnetic resonance imaging. Shift of midline intracranial structures helps diagnosing intracranial lesions, especially traumatic brain injury, stroke, brain tumor, and abscess. Being a sign of increased intracranial pressure, MLS is also an indicator of reduced brain perfusion caused by an intracranial mass or mass effect. We review studies that used the MLS to predict outcomes of patients with intracranial mass. In some studies, the MLS was also correlated to clinical features. Automated MLS measurement algorithms have significant potentials for assisting human experts in evaluating brain images. In symmetry-based algorithms, the deformed midline is detected and its distance from the ideal midline taken as the MLS. In landmark-based ones, MLS was measured following identification of specific anatomical landmarks. To validate these algorithms, measurements using these algorithms were compared to MLS measurements made by human experts. In addition to measuring the MLS on a given imaging study, there were newer applications of MLS that included comparing multiple MLS measurement before and after treatment and developing additional features to indicate mass effect. Suggestions for future research are provided.
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Affiliation(s)
- Chun-Chih Liao
- Institute of Biomedical Engineering, National Taiwan University, No. 1, Sec. 1, Renai Rd., Taipei City 10051, Taiwan
- Department of Neurosurgery, Taipei Hospital, Ministry of Health and Welfare, No. 127, Siyuan Rd., New Taipei City 24213, Taiwan
| | - Ya-Fang Chen
- Department of Medical Imaging, National Taiwan University Hospital, No. 7, Zhongshan S. Rd., Taipei City 10002, Taiwan
| | - Furen Xiao
- Institute of Biomedical Engineering, National Taiwan University, No. 1, Sec. 1, Renai Rd., Taipei City 10051, Taiwan
- Department of Neurosurgery, National Taiwan University Hospital, No. 7, Zhongshan S. Rd., Taipei City 10002, Taiwan
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Hanko M, Richterova R, Kolarovszki B. Efficiency and Limitations of Decompressive Craniectomy in Patients after Traumatic Brain Injury – Preliminary Results. ACTA MEDICA MARTINIANA 2018. [DOI: 10.1515/acm-2017-0015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Abstract
Introduction: Decompressive craniectomy (DC) has been recently proven effective tier II therapeutic procedure in the treatment of refractory posttraumatic intracranial hypertension. However, its full potential and effectivity is yet to be described and this surgery remains controversial. The goals of our study include analysis of efficiency of DC and description of risk factors associated with unfavourable outcome.
Methods: 24 patients who underwent DC at the Clinic of Neurosurgery, JFM CU in Martin, during years 2015–2016 were prospectively observed. Selected demographic, clinical, and radiographic factors were analysed and compared with patient’s GOS (Glasgow Outcome Scale) at the time of their first ambulatory control (after 3.5 months in average).
Results: We observed mortality of 29.17 %. Good outcome (GOS 4–5) was achieved by 29.17 % of the patients as well. Preoperative GCS ≤ 5 (p = 0.049), intraventricular bleeding (p = 0.0268), midline shift ≥ 15 mm (p = 0.0067), and the volume of intracranial lesion (R = −0.41, p = 0.046), especially its extracerebral component (R = −0.46, p = 0.02), were identified as statistically significant negative prognostic factors.
Conclusion: DC is effective in the management of patients with traumatic brain injury. Good outcome is achieved by 29.17 % of the patients. Described negative prognostic factors (preoperative GCS ≤ 5, intraventricular bleeding, midline shift ≥ 15 mm, and increasing the volume of traumatic mass lesion) could help in targeting this surgery only to patients who are expected to benefit from it.
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Affiliation(s)
- M Hanko
- Clinic of Neurosurgery, Jessenius Faculty of Medicine in Martin , Comenius University in Bratislava, University Hospital Martin , Slovakia
| | - R Richterova
- Clinic of Neurosurgery, Jessenius Faculty of Medicine in Martin , Comenius University in Bratislava, University Hospital Martin , Slovakia
| | - B. Kolarovszki
- Clinic of Neurosurgery, Jessenius Faculty of Medicine in Martin , Comenius University in Bratislava, University Hospital Martin , Slovakia
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Blixt J, Gunnarson E, Wanecek M. Erythropoietin Attenuates the Brain Edema Response after Experimental Traumatic Brain Injury. J Neurotrauma 2018; 35:671-680. [PMID: 29179621 PMCID: PMC5806078 DOI: 10.1089/neu.2017.5015] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Erythropoietin (EPO) has neuroprotective effects in multiple central nervous system (CNS) injury models; however EPO's effects on traumatic brain edema are elusive. To explore EPO as an intervention in traumatic brain edema, male Sprague–Dawley (SD) rats were subjected to blunt, controlled traumatic brain injury (TBI). Animals were randomized to EPO 5000 IU/kg or saline (control group) intraperitoneally within 30 min after trauma and once daily for 4 consecutive days. Brain MRI, immunohistofluorescence, immunohistochemistry, and quantitative protein analysis were performed at days 1 and 4 post- trauma. EPO significantly prevented the loss of the tight junction protein zona occludens 1 (ZO-1) observed in control animals after trauma. The decrease of ZO-1 in the control group was associated with an immunoglobulin (Ig)G increase in the perilesional parenchyma, indicating blood–brain barrier (BBB) dysfunction and increased permeability. EPO treatment attenuated decrease in apparent diffusion coefficient (ADC) after trauma, suggesting a reduction of cytotoxic edema, and reduced the IgG leakage, indicating that EPO contributed to preserve BBB integrity and attenuated vasogenic edema. Animals treated with EPO demonstrated conserved levels of aquaporin 4 (AQP4) protein expression in the perilesional area, whereas control animals showed a reduction of AQP4. We show that post TBI administration of EPO decreases early cytotoxic brain edema and preserves structural and functional properties of the BBB, leading to attenuation of the vasogenic edema response. The data support that the mechanisms involve preservation of the tight junction protein ZO-1 and the water channel AQP4, and indicate that treatment with EPO may have beneficial effects on the brain edema response following TBI.
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Affiliation(s)
- Jonas Blixt
- 1 Perioperative Medicine and Intensive Care, Karolinska University Hospital, Karolinska Institutet , Stockholm, Sweden .,2 Department of Physiology and Pharmacology, Karolinska University Hospital, Karolinska Institutet , Stockholm, Sweden
| | - Eli Gunnarson
- 3 Department of Women's and Children's Health Karolinska University Hospital, Karolinska Institutet , Stockholm, Sweden
| | - Michael Wanecek
- 2 Department of Physiology and Pharmacology, Karolinska University Hospital, Karolinska Institutet , Stockholm, Sweden
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Won YD, Na MK, Ryu JI, Cheong JH, Kim JM, Kim CH, Han MH. Radiologic Factors Predicting Deterioration of Mental Status in Patients with Acute Traumatic Subdural Hematoma. World Neurosurg 2017; 111:e120-e134. [PMID: 29248778 DOI: 10.1016/j.wneu.2017.12.025] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2017] [Revised: 11/30/2017] [Accepted: 12/04/2017] [Indexed: 02/02/2023]
Abstract
OBJECTIVE To evaluate whether subdural hematoma (SDH) volume and other radiologic factors predict deterioration of mental status in patients with acute traumatic SDH. METHODS SDH volumes were measured with a semiautomated tool. The area under the receiver operating characteristic curve was used to determine optimal cutoff values for mental deterioration, including the variables midline shift, SDH volume, hematoma thickness, and Sylvian fissure ratio. Multivariate logistic regression was used to calculate the odds ratio for mental deterioration based on several predictive factors. RESULTS We enrolled 103 consecutive patients admitted to our hospital with acute traumatic SDH over an 8-year period. We observed an increase in SDH volume of approximately 7.2 mL as SDH thickness increased by 1 mm. A steeper slope for midline shift was observed in patients with SDH volumes of approximately 75 mL in the younger age group compared with patients in the older age group. When comparing cutoff values used to predict poor mental status at time of admission between the 2 age groups, we observed smaller midline shifts in the older patients. CONCLUSIONS Among younger patients, an overall tendency for more rapid midline shift progression was observed in patients with relatively low SDH volumes compared with older patients. Older patients seem to tolerate larger hematoma volumes owing to brain atrophy compared with younger patients. When there is a midline shift, older patients seem to be more vulnerable to mental deterioration than younger patients.
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Affiliation(s)
- Yu Deok Won
- Department of Neurosurgery, Hanyang University Guri Hospital, Gyonggi-do, Korea
| | - Min Kyun Na
- Department of Neurosurgery, Hanyang University Guri Hospital, Gyonggi-do, Korea
| | - Je-Il Ryu
- Department of Neurosurgery, Hanyang University Guri Hospital, Gyonggi-do, Korea
| | - Jin-Hwan Cheong
- Department of Neurosurgery, Hanyang University Guri Hospital, Gyonggi-do, Korea
| | - Jae-Min Kim
- Department of Neurosurgery, Hanyang University Guri Hospital, Gyonggi-do, Korea
| | - Choong-Hyun Kim
- Department of Neurosurgery, Hanyang University Guri Hospital, Gyonggi-do, Korea
| | - Myung-Hoon Han
- Department of Neurosurgery, Hanyang University Guri Hospital, Gyonggi-do, Korea.
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Helsinki Computed Tomography Scoring System Can Independently Predict Long-Term Outcome in Traumatic Brain Injury. World Neurosurg 2017; 101:528-533. [DOI: 10.1016/j.wneu.2017.02.072] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2016] [Revised: 02/13/2017] [Accepted: 02/15/2017] [Indexed: 11/22/2022]
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43
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Neuromonitorización en el trauma craneoencefálico grave en pediatría. Neurocirugia (Astur) 2016; 27:176-85. [DOI: 10.1016/j.neucir.2015.11.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2015] [Revised: 11/06/2015] [Accepted: 11/10/2015] [Indexed: 11/18/2022]
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Balasa D, Tunas A, Rusu I, Hancu A, Butoi G, Gramanschi V. Acute cerebral MCA ischemia with secondary severe head injury and acute intracerebral and subdural haematoma. Case report. ROMANIAN NEUROSURGERY 2016. [DOI: 10.1515/romneu-2016-0033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Abstract
Generally, according to international literature, cerebral ischemia is a secondary posttraumatic lesion produced by direct compression in the context of a cerebral herniation syndrome or indirect by vasospasm produced by posttraumatic subarachnoid, subdural or intraventricular hemorrhages. We present the case of a patient with an acute MCA ischemia with severe head injury due to a fall with subsequent intracranial acute intracerebral and subdural hematoma which evolved with acute left uncal, parahipocampal and subfalcinecerebral herniation (coma, GCS 6, left mydriasis, right severe hemiparesis). Surgical emergency aspiration of the hematomas was performed. Postoperative treatment of cerebral ischemia and residual hematomas was properly done. We consider important and underdiagnosed the association of cerebral ischemia and secondary posttraumatic brain injuries.
Abbreviations: MCA-middle cerebral artery, GCS-Glasgow Coma Scale, ICA-internal carotid artery, PCA-posterior cerebral artery, ACA-anterior cerebral artery.
Conclusion: We present a case of a patient with an acute MCA ischemia with secondary head injury due to a fall with subsequent intracranial acute intracerebral and subdural hematomas. Surgical emergency aspiration of the hematomas was performed. The treatment was performed for both lesions (cerebral ischemia and posttraumatic hematomas) with vitamins B, neurotrophycs, pain killers, antibiotics. Unfortunately, due to aggravation of the Mendelson syndrome, the patient died 7 days later.
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45
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Yan P, Yan L, Hu T, Zhang Z, Feng J, Zhao H. Assessment of the accuracy of ABC/2 variations in traumatic epidural hematoma volume estimation: a retrospective study. PeerJ 2016; 4:e1921. [PMID: 27077012 PMCID: PMC4830250 DOI: 10.7717/peerj.1921] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2015] [Accepted: 03/19/2016] [Indexed: 11/27/2022] Open
Abstract
Background. The traumatic epidural hematoma (tEDH) volume is often used to assist in tEDH treatment planning and outcome prediction. ABC/2 is a well-accepted volume estimation method that can be used for tEDH volume estimation. Previous studies have proposed different variations of ABC/2; however, it is unclear which variation will provide a higher accuracy. Given the promising clinical contribution of accurate tEDH volume estimations, we sought to assess the accuracy of several ABC/2 variations in tEDH volume estimation. Methods. The study group comprised 53 patients with tEDH who had undergone non-contrast head computed tomography scans. For each patient, the tEDH volume was automatically estimated by eight ABC/2 variations (four traditional and four newly derived) with an in-house program, and results were compared to those from manual planimetry. Linear regression, the closest value, percentage deviation, and Bland-Altman plot were adopted to comprehensively assess accuracy. Results. Among all ABC/2 variations assessed, the traditional variations y = 0.5 × A1B1C1 (or A2B2C1) and the newly derived variations y = 0.65 × A1B1C1 (or A2B2C1) achieved higher accuracy than the other variations. No significant differences were observed between the estimated volume values generated by these variations and those of planimetry (p > 0.05). Comparatively, the former performed better than the latter in general, with smaller mean percentage deviations (7.28 ± 5.90% and 6.42 ± 5.74% versus 19.12 ± 6.33% and 21.28 ± 6.80%, respectively) and more values closest to planimetry (18/53 and 18/53 versus 2/53 and 0/53, respectively). Besides, deviations of most cases in the former fell within the range of <10% (71.70% and 84.91%, respectively), whereas deviations of most cases in the latter were in the range of 10–20% and >20% (90.57% and 96.23, respectively). Discussion. In the current study, we adopted an automatic approach to assess the accuracy of several ABC/2 variations for tEDH volume estimation. Our initial results showed that the variations y = 0.5 × A1B1C1 (or A2B2C1) performed better than the other traditional variations, suggesting that the adjusted depth is favorable. In addition, linear regression has been shown to be useful for improving the estimation accuracy of the ABC/2 method, and future studies are warranted to investigate the applicability of such linear regression-derived formulas for clinical application.
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Affiliation(s)
- Pengfei Yan
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ling Yan
- Department of Computer Science, University of Northern BC, Prince George, Canada
| | - Tingting Hu
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhen Zhang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jun Feng
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hongyang Zhao
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Amyot F, Arciniegas DB, Brazaitis MP, Curley KC, Diaz-Arrastia R, Gandjbakhche A, Herscovitch P, Hinds SR, Manley GT, Pacifico A, Razumovsky A, Riley J, Salzer W, Shih R, Smirniotopoulos JG, Stocker D. A Review of the Effectiveness of Neuroimaging Modalities for the Detection of Traumatic Brain Injury. J Neurotrauma 2015; 32:1693-721. [PMID: 26176603 PMCID: PMC4651019 DOI: 10.1089/neu.2013.3306] [Citation(s) in RCA: 114] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
The incidence of traumatic brain injury (TBI) in the United States was 3.5 million cases in 2009, according to the Centers for Disease Control and Prevention. It is a contributing factor in 30.5% of injury-related deaths among civilians. Additionally, since 2000, more than 260,000 service members were diagnosed with TBI, with the vast majority classified as mild or concussive (76%). The objective assessment of TBI via imaging is a critical research gap, both in the military and civilian communities. In 2011, the Department of Defense (DoD) prepared a congressional report summarizing the effectiveness of seven neuroimaging modalities (computed tomography [CT], magnetic resonance imaging [MRI], transcranial Doppler [TCD], positron emission tomography, single photon emission computed tomography, electrophysiologic techniques [magnetoencephalography and electroencephalography], and functional near-infrared spectroscopy) to assess the spectrum of TBI from concussion to coma. For this report, neuroimaging experts identified the most relevant peer-reviewed publications and assessed the quality of the literature for each of these imaging technique in the clinical and research settings. Although CT, MRI, and TCD were determined to be the most useful modalities in the clinical setting, no single imaging modality proved sufficient for all patients due to the heterogeneity of TBI. All imaging modalities reviewed demonstrated the potential to emerge as part of future clinical care. This paper describes and updates the results of the DoD report and also expands on the use of angiography in patients with TBI.
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Affiliation(s)
- Franck Amyot
- The Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, Maryland
- Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, Maryland
| | - David B. Arciniegas
- Beth K. and Stuart C. Yudofsky Division of Neuropsychiatry, Baylor College of Medicine, Houston, Texas
- Brain Injury Research, TIRR Memorial Hermann, Houston, Texas
| | | | - Kenneth C. Curley
- Combat Casualty Care Directorate (RAD2), U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland
| | - Ramon Diaz-Arrastia
- Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, Maryland
| | - Amir Gandjbakhche
- The Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, Maryland
| | - Peter Herscovitch
- Positron Emission Tomography Department, National Institutes of Health Clinical Center, Bethesda, Maryland
| | - Sidney R. Hinds
- Defense and Veterans Brain Injury Center, Defense Centers of Excellence for Psychological Health and Traumatic Brain Injury Silver Spring, Maryland
| | - Geoffrey T. Manley
- Brain and Spinal Injury Center, Department of Neurological Surgery, University of California, San Francisco, San Francisco, California
| | - Anthony Pacifico
- Congressionally Directed Medical Research Programs, Fort Detrick, Maryland
| | | | - Jason Riley
- Queens University, Kingston, Ontario, Canada
- ArcheOptix Inc., Picton, Ontario, Canada
| | - Wanda Salzer
- Congressionally Directed Medical Research Programs, Fort Detrick, Maryland
| | - Robert Shih
- Walter Reed National Military Medical Center, Bethesda, Maryland
| | - James G. Smirniotopoulos
- Department of Radiology, Neurology, and Biomedical Informatics, Uniformed Services University of the Health Sciences, Bethesda, Maryland
| | - Derek Stocker
- Walter Reed National Military Medical Center, Bethesda, Maryland
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Jang KM, Kwon JT, Hwang SN, Park YS, Nam TK. Comparison of the Outcomes and Recurrence with Three Surgical Techniques for Chronic Subdural Hematoma: Single, Double Burr Hole, and Double Burr Hole Drainage with Irrigation. Korean J Neurotrauma 2015; 11:75-80. [PMID: 27169069 PMCID: PMC4847514 DOI: 10.13004/kjnt.2015.11.2.75] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2015] [Revised: 08/10/2015] [Accepted: 08/28/2015] [Indexed: 12/02/2022] Open
Abstract
Objective Chronic subdural hematoma (CSDH), a disease commonly encountered by neurosurgeons, is treated by burr hole drainage (BHD). However, the optimal surgical technique among the three types of BHD has not been determined. Methods We conducted a retrospective study on BHD performed on 93 patients who were diagnosed with CSDH. The subjects were divided into three groups based on the surgical technique performed: single BHD without irrigation (Group A, n=31), double BHD without irrigation (Group B, n=32), and double BHD with irrigation (Group C, n=30). The clinical factors, radiological factors and recurrences were compared between the three groups. Moreover, independent factors affecting the recurrence were analyzed. Results The change in hematoma thickness was 29.77±7.94%, 49.73±12.87%, and 75.29±4.32% for Group A, B, and C, respectively, while the change in midline shift was 40.81±15.47%, 51.78±10.94%, and 56.16±16.16%, respectively. Thus, Group C showed the most effective for resolution of hematoma and midline shift (p<0.05). Group A, B, and C had 12 cases (38.7%), 8 cases (25.0%), and 3 cases (10.0%) of recurrences, respectively. Group C had a statistically significantly fewer recurrence rate than Group A (p<0.05). Double burr hole, irrigation, and coagulopathy were each identified as independent factors that reduce recurrence (p<0.05). Conclusion Among the three techniques, the double BHD with saline irrigation resulted in the fewest recurrences. It is probably the most effective technique for preventing the recurrence of CSDH.
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Affiliation(s)
- Kyoung-Min Jang
- Department of Neurosurgery, College of Medicine, Chung-Ang University, Seoul, Korea
| | - Jeong-Taik Kwon
- Department of Neurosurgery, College of Medicine, Chung-Ang University, Seoul, Korea
| | - Sung-Nam Hwang
- Department of Neurosurgery, College of Medicine, Chung-Ang University, Seoul, Korea
| | - Yong-Sook Park
- Department of Neurosurgery, College of Medicine, Chung-Ang University, Seoul, Korea
| | - Taek-Kyun Nam
- Department of Neurosurgery, College of Medicine, Chung-Ang University, Seoul, Korea
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48
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Bartels RHMA, Meijer FJA, van der Hoeven H, Edwards M, Prokop M. Midline shift in relation to thickness of traumatic acute subdural hematoma predicts mortality. BMC Neurol 2015; 15:220. [PMID: 26496765 PMCID: PMC4620003 DOI: 10.1186/s12883-015-0479-x] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2014] [Accepted: 10/16/2015] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Traumatic acute subdural hematoma has a high mortality despite intensive treatment. Despite the existence of several prediction models, it is very hard to predict an outcome. We investigated whether a specific combination of initial head CT-scan findings is a factor in predicting outcome, especially non-survival. METHODS We retrospectively studied admission head CT scans of all adult patients referred for a traumatic acute subdural hematoma between April 2009 and April 2013. Chart review was performed for every included patient. Midline shift and thickness of the hematoma were measured by two independent observers. The difference between midline shift and thickness of the hematoma was calculated. These differences were correlated with outcome. IRB has approved the study. RESULTS A total of 59 patients were included, of whom 29 died. We found a strong correlation between a midline shift exceeding the thickness of the hematoma by 3 mm or more, and subsequent mortality. For each evaluation, specificity was 1.0 (95 % CI: 0.85-1 for all evaluations), positive predictive value 1.0 (95 % CI between 0.31-1 and 0.56-1), while sensitivity ranged from 0.1 to 0.23 (95 % CI between 0.08-0.39 and 0.17-0.43), and negative predictive value varied from 0.52 to 0.56 (95 % CI between 0.38-0.65 and 0.41-0.69). CONCLUSIONS In case of a traumatic acute subdural hematoma, a difference between the midline shift and the thickness of the hematoma ≥ 3 mm at the initial CT predicted mortality in all cases. This is the first time that such a strong correlation was reported. Especially for the future development of prediction models, the relation between midline shift and thickness of the hematoma could be included as a separate factor.
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Affiliation(s)
- Ronald H M A Bartels
- Department of Neurosurgery, Radboud University Medical Center, Nijmegen, The Netherlands.
| | - Frederick J A Meijer
- Department of Radiology, Radboud University Medical Center, Nijmegen, The Netherlands.
| | - Hans van der Hoeven
- Department of Intensive Care Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.
| | - Michael Edwards
- Department of Trauma and Emergency Surgery, Radboud University Medical Center, Nijmegen, The Netherlands.
| | - Mathias Prokop
- Department of Radiology, Radboud University Medical Center, Nijmegen, The Netherlands.
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Prichep LS, Naunheim R, Bazarian J, Mould WA, Hanley D. Identification of hematomas in mild traumatic brain injury using an index of quantitative brain electrical activity. J Neurotrauma 2015; 32:17-22. [PMID: 25054838 DOI: 10.1089/neu.2014.3365] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Rapid identification of traumatic intracranial hematomas following closed head injury represents a significant health care need because of the potentially life-threatening risk they present. This study demonstrates the clinical utility of an index of brain electrical activity used to identify intracranial hematomas in traumatic brain injury (TBI) presenting to the emergency department (ED). Brain electrical activity was recorded from a limited montage located on the forehead of 394 closed head injured patients who were referred for CT scans as part of their standard ED assessment. A total of 116 of these patients were found to be CT positive (CT+), of which 46 patients with traumatic intracranial hematomas (CT+) were identified for study. A total of 278 patients were found to be CT negative (CT-) and were used as controls. CT scans were subjected to quantitative measurements of volume of blood and distance of bleed from recording electrodes by blinded independent experts, implementing a validated method for hematoma measurement. Using an algorithm based on brain electrical activity developed on a large independent cohort of TBI patients and controls (TBI-Index), patients were classified as either positive or negative for structural brain injury. Sensitivity to hematomas was found to be 95.7% (95% CI = 85.2, 99.5), specificity was 43.9% (95% CI = 38.0, 49.9). There was no significant relationship between the TBI-Index and distance of the bleed from recording sites (F = 0.044, p = 0.833), or volume of blood measured F = 0.179, p = 0.674). Results of this study are a validation and extension of previously published retrospective findings in an independent population, and provide evidence that a TBI-Index for structural brain injury is a highly sensitive measure for the detection of potentially life-threatening traumatic intracranial hematomas, and could contribute to the rapid, quantitative evaluation and treatment of such patients.
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Affiliation(s)
- Leslie S Prichep
- 1 NYU School of Medicine , Brain Research Laboratories, Department of Psychiatry, New York, New York
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50
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Raj R, Siironen J, Skrifvars MB, Hernesniemi J, Kivisaari R. Predicting outcome in traumatic brain injury: development of a novel computerized tomography classification system (Helsinki computerized tomography score). Neurosurgery 2015; 75:632-46; discussion 646-7. [PMID: 25181434 DOI: 10.1227/neu.0000000000000533] [Citation(s) in RCA: 118] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Early computerized tomography (CT) abnormalities are important predictors of outcome after traumatic brain injury (TBI). OBJECTIVE To develop a novel CT scoring system (Helsinki CT score) and to compare it with the Marshall CT classification and the Rotterdam CT score in predicting long-term outcome of patients with TBI. METHODS Eight hundred sixty-nine consecutive TBI patients were included in this open-cohort, retrospective, single-center study. Logistic regression was used to develop the Helsinki CT score. The scores from the Marshall, Rotterdam, and Helsinki CT scoring methods were added to a clinical model based on age, motor score, and pupils to evaluate their value in predicting outcome. Internal validity was assessed by a bootstrap technique and expressed as area under the curve (AUC). Outcome was 6-month unfavorable neurological outcome and mortality. RESULTS Variables included in the Helsinki CT score were bleeding type and size, intraventricular hemorrhage, and suprasellar cisterns. In the present data set, the performance of the Helsinki CT score was superior to that of the Marshall CT and Rotterdam CT scores (AUC, 0.74-0.75 vs 0.63-0.70; P < .001). Addition of the Helsinki CT score modestly increased prognostic performance of the clinical model (AUC neurological outcome +0.02 [P = .002]; AUC mortality, +0.01 [P = .21]). In contrast, the Marshall and Rotterdam CT scores were of no additional predictive value to the clinical model (P > .05). CONCLUSION Use of the novel Helsinki CT score improved outcome prediction accuracy, and the Helsinki CT score is a feasible alternative to the Rotterdam and Marshall CT systems. External validation of the Helsinki CT score is advocated to show generalizability.
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Affiliation(s)
- Rahul Raj
- *Departments of Neurosurgery and ‡Intensive Care, Helsinki University Hospital, Helsinki, Finland
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