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Delteil C, Manlius T, Bailly N, Godio-Raboutet Y, Piercecchi-Marti MD, Tuchtan L, Hak JF, Velly L, Simeone P, Thollon L. Traumatic axonal injury: Clinic, forensic and biomechanics perspectives. Leg Med (Tokyo) 2024; 70:102465. [PMID: 38838409 DOI: 10.1016/j.legalmed.2024.102465] [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: 03/26/2024] [Revised: 05/21/2024] [Accepted: 06/01/2024] [Indexed: 06/07/2024]
Abstract
Identification of Traumatic axonal injury (TAI) is critical in clinical practice, particularly in terms of long-term prognosis, but also for medico-legal issues, to verify whether the death or the after-effects were attributable to trauma. Multidisciplinary approaches are an undeniable asset when it comes to solving these problems. The aim of this work is therefore to list the different techniques needed to identify axonal lesions and to understand the lesion mechanisms involved in their formation. Imaging can be used to assess the consequences of trauma, to identify indirect signs of TAI, to explain the patient's initial symptoms and even to assess the patient's prognosis. Three-dimensional reconstructions of the skull can highlight fractures suggestive of trauma. Microscopic and immunohistochemical techniques are currently considered as the most reliable tools for the early identification of TAI following trauma. Finite element models use mechanical equations to predict biomechanical parameters, such as tissue stresses and strains in the brain, when subjected to external forces, such as violent impacts to the head. These parameters, which are difficult to measure experimentally, are then used to predict the risk of injury. The integration of imaging data with finite element models allows researchers to create realistic and personalized computational models by incorporating actual geometry and properties obtained from imaging techniques. The personalization of these models makes their forensic approach particularly interesting.
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Affiliation(s)
- Clémence Delteil
- Forensic Department, Assistance Publique-Hôpitaux de Marseille, La Timone, 264 rue St Pierre, 13385 Marseille Cedex 05, France; Aix Marseille Univ, CNRS, EFS, ADES, Marseille, France.
| | - Thais Manlius
- Aix Marseille Univ, Univ Gustave Eiffel, LBA, Marseille, France.
| | - Nicolas Bailly
- Aix Marseille Univ, Univ Gustave Eiffel, LBA, Marseille, France; Neuroimagery Department, Assistance Publique-Hôpitaux de Marseille, La Timone, 264 rue St Pierre, 13385 Marseille Cedex 05, France.
| | | | - Marie-Dominique Piercecchi-Marti
- Forensic Department, Assistance Publique-Hôpitaux de Marseille, La Timone, 264 rue St Pierre, 13385 Marseille Cedex 05, France; Aix Marseille Univ, CNRS, EFS, ADES, Marseille, France.
| | - Lucile Tuchtan
- Forensic Department, Assistance Publique-Hôpitaux de Marseille, La Timone, 264 rue St Pierre, 13385 Marseille Cedex 05, France; Aix Marseille Univ, CNRS, EFS, ADES, Marseille, France.
| | | | - Lionel Velly
- Département d'Anesthésie-Réanimation, Assistance Publique-Hôpitaux de Marseille, La Timone, Marseille, France; Université Aix-Marseille/CNRS, Institut des Neurosciences de la Timone, UMR7289, Marseille, France.
| | - Pierre Simeone
- Département d'Anesthésie-Réanimation, Assistance Publique-Hôpitaux de Marseille, La Timone, Marseille, France; Université Aix-Marseille/CNRS, Institut des Neurosciences de la Timone, UMR7289, Marseille, France.
| | - Lionel Thollon
- Aix Marseille Univ, Univ Gustave Eiffel, LBA, Marseille, France.
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Duarte S, Ou Z, Cao M, Cho SM, Thakor NV, Ritzl EK, Geocadin RG. High frequency oscillations may improve somatosensory evoked potential detection of good outcomes in disorders of consciousness secondary to acute neurologic injury. Resuscitation 2024:110377. [PMID: 39187152 DOI: 10.1016/j.resuscitation.2024.110377] [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: 07/16/2024] [Revised: 08/19/2024] [Accepted: 08/20/2024] [Indexed: 08/28/2024]
Abstract
BACKGROUND Somatosensory evoked potentials (SEPs) are highly specific predictors of poor prognosis in hypoxic-ischemic coma when cortical responses (N20s) are absent. However, bilateral N20 presence is nonspecific for good outcomes. High-frequency oscillations (HFOs) in the SEP waveform predict neurologic recovery in animals, but clinical applications are poorly understood. We sought to develop a clinical measure of HFOs to potentially improve detection of good outcomes in coma. MATERIALS AND METHODS We collected SEP waveform data from all comatose inpatients (GCS<=8) who underwent neurologic prognostication from 2020-2022 at Johns Hopkins Hospital. We developed a novel measure - HFO evoked to spontaneous ratios (HFO-ESRs) - and applied this to those patients with bilaterally present N20s using both standard univariate classification and cubic kernal vector machine (SVM) models to predict the last documented in-hospital Glasgow Coma Scale (GCS) prior to discharge or death. RESULTS Of 58 total patients, 34 (58.6%) had bilaterally present N20s. Of these, 14 had final GCS>=9, and 20 had final GCS<=8. Mean age was 52 (+/- 17) years, 20.1% female. Etiologies of coma were primarily global hypoxic-ischemic brain injury (79.4%), intracranial hemorrhage (11.8%), and traumatic brain injury (2.9%). In univariate classification, the addition of averaged HFO-ESRs to present N20s predicted final GCS>=9 with 68% specificity. The SVM model further improved specificity to 85%. CONCLUSIONS In this pilot investigation, we developed a novel clinical measure of SEP HFOs. Incorporation of this measure may improve the specificity of the SEP to predict in-hospital GCS outcomes in coma, but requires further validation in specific neurologic injuries and with longitudinal outcomes.
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Affiliation(s)
- Siena Duarte
- Department of Neurology - Division of Neurocritical Care, University of California, San Francisco.
| | - Ze Ou
- Medical Scientist Training Program, Washington University in St. Louis School of Medicine, St. Louis, MO 63110, USA.
| | - Mingfeng Cao
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
| | - Sung-Min Cho
- Departments of Neurology, Anesthesiology, Critical Care Medicine and Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.
| | - Nitish V Thakor
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
| | - Eva K Ritzl
- Department of Neurology, Mass General Brigham, Boston, MA.
| | - Romergryko G Geocadin
- Departments of Neurology, Anesthesiology, Critical Care Medicine and Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.
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Martin FP, Goronflot T, Moyer JD, Huet O, Asehnoune K, Cinotti R, Gourraud PA, Roquilly A. Predictive Models of Long-Term Outcome in Patients with Moderate to Severe Traumatic Brain Injury are Biased Toward Mortality Prediction. Neurocrit Care 2024:10.1007/s12028-024-02082-3. [PMID: 39138720 DOI: 10.1007/s12028-024-02082-3] [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: 10/25/2023] [Accepted: 07/26/2024] [Indexed: 08/15/2024]
Abstract
BACKGROUND The prognostication of long-term functional outcomes remains challenging in patients with traumatic brain injury (TBI). Our aim was to demonstrate that intensive care unit (ICU) variables are not efficient to predict 6-month functional outcome in survivors with moderate to severe TBI (msTBI) but are mostly associated with mortality, which leads to a mortality bias for models predicting a composite outcome of mortality and severe disability. METHODS We analyzed the data from the multicenter randomized controlled Continuous Hyperosmolar Therapy in Traumatic Brain-Injured Patients trial and developed predictive models using machine learning methods and baseline characteristics and predictors collected during ICU stay. We compared our models' predictions of 6-month binary Glasgow Outcome Scale extended (GOS-E) score in all patients with msTBI (unfavorable GOS-E 1-4 vs. favorable GOS-E 5-8) with mortality (GOS-E 1 vs. GOS-E 2-8) and binary functional outcome in survivors with msTBI (severe disability GOS-E 2-4 vs. moderate to no disability GOS-E 5-8). We investigated the link between ICU variables and long-term functional outcomes in survivors with msTBI using predictive modeling and factor analysis of mixed data and validated our hypotheses on the International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT) model. RESULTS Based on data from 370 patients with msTBI and classically used ICU variables, the prediction of the 6-month outcome in survivors was inefficient (mean area under the receiver operating characteristic 0.52). Using factor analysis of mixed data graph, we demonstrated that high-variance ICU variables were not associated with outcome in survivors with msTBI (p = 0.15 for dimension 1, p = 0.53 for dimension 2) but mostly with mortality (p < 0.001 for dimension 1), leading to a mortality bias for models predicting a composite outcome of mortality and severe disability. We finally identified this mortality bias in the IMPACT model. CONCLUSIONS We demonstrated using machine learning-based predictive models that classically used ICU variables are strongly associated with mortality but not with 6-month outcome in survivors with msTBI, leading to a mortality bias when predicting a composite outcome of mortality and severe disability.
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Affiliation(s)
- Florian P Martin
- Nantes Université, Institut National de la Santé et de la Recherche Médicale (INSERM), UMR 1064, Center for Research in Transplantation and Translational Immunology (CR2TI), 22 Boulevard Bénoni Goullin, 44200, Nantes, France.
- Department of Anesthesiology and Surgical Intensive Care Unit, Centre Hospitalier Universitaire (CHU) Nantes, Nantes, France.
| | - Thomas Goronflot
- CHU Nantes, Pôle Hospitalo-Universitaire 11: Santé Publique, Clinique Des Données, INSERM, Nantes Université, Nantes, France
| | - Jean D Moyer
- Department of Anesthesia and Critical Care, Départements Médico-Universitaires Parabol, Assistance Publique-Hôpitaux de Paris Nord, Beaujon Hospital, Paris, France
| | - Olivier Huet
- Anesthesia and Intensive Care Unit, CHU Brest, Brest, France
| | - Karim Asehnoune
- Nantes Université, Institut National de la Santé et de la Recherche Médicale (INSERM), UMR 1064, Center for Research in Transplantation and Translational Immunology (CR2TI), 22 Boulevard Bénoni Goullin, 44200, Nantes, France
- Department of Anesthesiology and Surgical Intensive Care Unit, Centre Hospitalier Universitaire (CHU) Nantes, Nantes, France
| | - Raphaël Cinotti
- Department of Anesthesiology and Surgical Intensive Care Unit, Centre Hospitalier Universitaire (CHU) Nantes, Nantes, France
- Methods in Patient-Centered Outcomes and Healthy Research (SPHERE), INSERM, UMR 1246, Nantes Université, Université de Tours, Nantes, France
| | - Pierre A Gourraud
- Nantes Université, Institut National de la Santé et de la Recherche Médicale (INSERM), UMR 1064, Center for Research in Transplantation and Translational Immunology (CR2TI), 22 Boulevard Bénoni Goullin, 44200, Nantes, France
- CHU Nantes, Pôle Hospitalo-Universitaire 11: Santé Publique, Clinique Des Données, INSERM, Nantes Université, Nantes, France
| | - Antoine Roquilly
- Nantes Université, Institut National de la Santé et de la Recherche Médicale (INSERM), UMR 1064, Center for Research in Transplantation and Translational Immunology (CR2TI), 22 Boulevard Bénoni Goullin, 44200, Nantes, France
- Department of Anesthesiology and Surgical Intensive Care Unit, Centre Hospitalier Universitaire (CHU) Nantes, Nantes, France
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Altmayer V, Sangare A, Calligaris C, Puybasset L, Perlbarg V, Naccache L, Sitt JD, Rohaut B. Functional and structural brain connectivity in disorders of consciousness. Brain Struct Funct 2024:10.1007/s00429-024-02839-8. [PMID: 39052096 DOI: 10.1007/s00429-024-02839-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 07/12/2024] [Indexed: 07/27/2024]
Abstract
Brain connectivity, allowing information to be shared between distinct cortical areas and thus to be processed in an integrated way, has long been considered critical for consciousness. However, the relationship between functional intercortical interactions and the structural connections thought to underlie them is poorly understood. In the present work, we explore both functional (with an EEG-based metric: the median weighted symbolic mutual information in the theta band) and structural (with a brain MRI-based metric: fractional anisotropy) connectivities in a cohort of 78 patients with disorders of consciousness. Both metrics could distinguish patients in a vegetative state from patients in minimally conscious state. Crucially, we discovered a significant positive correlation between functional and structural connectivities. Furthermore, we showed that this structure-function relationship is more specifically observed when considering structural connectivity within the intra- and inter-hemispheric long-distance cortico-cortical bundles involved in the Global Neuronal Workspace (GNW) theory of consciousness, thus supporting predictions of this model. Altogether, these results support the interest of multimodal assessments of brain connectivity in refining the diagnostic evaluation of patients with disorders of consciousness.
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Affiliation(s)
- Victor Altmayer
- Sorbonne University, Paris, F-75013, France
- Department of Neurology, AP-HP, Pitié-Salpêtrière Hospital, Neuro-ICU, Paris, F-75013, France
| | - Aude Sangare
- Sorbonne University, Paris, F-75013, France
- Department of Neurophysiology, AP-HP, Pitié-Salpêtrière Hospital, Paris, F-75013, France
- PICNIC-Lab, Paris Brain Institute, (ICM), INSERM, CNRS, Hôpital Pitié Salpêtrière, 47 bvd de l'hôpital, Paris, F-75013, France
| | - Charlotte Calligaris
- Sorbonne University, Paris, F-75013, France
- Department of Neurology, AP-HP, Pitié-Salpêtrière Hospital, Neuro-ICU, Paris, F-75013, France
| | - Louis Puybasset
- Sorbonne University, Paris, F-75013, France
- Department of Neuro-anesthesiology and Neurocritical Care, AP-HP, Pitié-Salpêtrière Hospital, Paris, F-75013, France
| | | | - Lionel Naccache
- Sorbonne University, Paris, F-75013, France
- Department of Neurophysiology, AP-HP, Pitié-Salpêtrière Hospital, Paris, F-75013, France
- PICNIC-Lab, Paris Brain Institute, (ICM), INSERM, CNRS, Hôpital Pitié Salpêtrière, 47 bvd de l'hôpital, Paris, F-75013, France
| | - Jacobo Diego Sitt
- PICNIC-Lab, Paris Brain Institute, (ICM), INSERM, CNRS, Hôpital Pitié Salpêtrière, 47 bvd de l'hôpital, Paris, F-75013, France
| | - Benjamin Rohaut
- Sorbonne University, Paris, F-75013, France.
- Department of Neurology, AP-HP, Pitié-Salpêtrière Hospital, Neuro-ICU, Paris, F-75013, France.
- PICNIC-Lab, Paris Brain Institute, (ICM), INSERM, CNRS, Hôpital Pitié Salpêtrière, 47 bvd de l'hôpital, Paris, F-75013, France.
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5
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Lu Y, Zhang Q, Jiang J, Zhang Y. An externally validated prognostic model for critically ill patients with traumatic brain injury. Ann Clin Transl Neurol 2024. [PMID: 38973122 DOI: 10.1002/acn3.52148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Revised: 06/23/2024] [Accepted: 06/26/2024] [Indexed: 07/09/2024] Open
Abstract
OBJECTIVE Patients with traumatic brain injury (TBI) who are admitted to the intensive care unit often exhibit critical conditions; thus, early prediction of in-hospital mortality is crucial. In this study, we aimed to develop a reliable and easily promotable model for predicting the in-hospital mortality of critically ill patients with TBI using easily accessible indicators and validate the model using external data. METHODS Patient data from the Medical Information Mart for Intensive Care-IV 2.2 database were used as training and internal validation sets to establish and internally validate the prognostic model. Data from the Affiliated Dongyang Hospital of Wenzhou Medical University were used for external validation. The Boruta algorithm was used for the initial feature selection, followed by univariate and multivariate logistic regression analyses to identify the final independent predictors. The predictive performance was evaluated using a receiver operating characteristic curve, calibration curve, clinical practicality decision curve analysis, and clinical impact curve. RESULTS This study included 3225 patients (training set: 2042; internal validation set: 874; and external validation set: 309). Ten variables were selected for inclusion in the nomogram model: age, mechanical ventilation usage, vasoactive agent usage, intracerebral hemorrhage, temperature, respiration rate, white blood cell count, platelet count, red blood cell distribution width, and glucose. The nomogram demonstrated good predictive performance in both the internal and external validation sets. INTERPRETATION We developed an externally validated nomogram that exhibited good discrimination, calibration, and clinical utility for predicting in-hospital mortality in critically ill patients with TBI.
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Affiliation(s)
- Yan Lu
- Clinical Laboratory, Affiliated Dongyang Hospital of Wenzhou Medical University, 60 West Wuning Road, Dongyang, Zhejiang, 322100, China
| | - Qiaohong Zhang
- Clinical Laboratory, Affiliated Dongyang Hospital of Wenzhou Medical University, 60 West Wuning Road, Dongyang, Zhejiang, 322100, China
| | - Jinwen Jiang
- Clinical Laboratory, Affiliated Dongyang Hospital of Wenzhou Medical University, 60 West Wuning Road, Dongyang, Zhejiang, 322100, China
| | - Yongjun Zhang
- Clinical Laboratory, Affiliated Dongyang Hospital of Wenzhou Medical University, 60 West Wuning Road, Dongyang, Zhejiang, 322100, China
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Sanders WR, Barber JK, Temkin NR, Foreman B, Giacino JT, Williamson T, Edlow BL, Manley GT, Bodien YG. Recovery Potential in Patients Who Died After Withdrawal of Life-Sustaining Treatment: A TRACK-TBI Propensity Score Analysis. J Neurotrauma 2024. [PMID: 38739032 DOI: 10.1089/neu.2024.0014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/14/2024] Open
Abstract
Among patients with severe traumatic brain injury (TBI), there is high prognostic uncertainty but growing evidence that recovery of independence is possible. Nevertheless, families are often asked to make decisions about withdrawal of life-sustaining treatment (WLST) within days of injury. The range of potential outcomes for patients who died after WLST (WLST+) is unknown, posing a challenge for prognostic modeling and clinical counseling. We investigated the potential for survival and recovery of independence after acute TBI in patients who died after WLST. We used Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) data and propensity score matching to pair participants with WLST+ to those with a similar probability of WLST (based on demographic and clinical characteristics), but for whom life-sustaining treatment was not withdrawn (WLST-). To optimize matching, we divided the WLST- cohort into tiers (Tier 1 = 0-11%, Tier 2 = 11-27%, Tier 3 = 27-70% WLST propensity). We estimated the level of recovery that could be expected in WLST+ participants by evaluating 3-, 6-, and 12-month Glasgow Outcome Scale-Extended (GOSE) and Disability Rating Scale outcomes in matched WLST- participants. Of 90 WLST+ participants (80% male, mean [standard deviation; SD] age = 59.2 [17.9] years, median [IQR] days to WLST = 5.4 [2.2, 11.7]), 80 could be matched to WLST- participants. Of 56 WLST- participants who were followed at 6 months, 31 (55%) died. Among survivors in the overall sample and survivors in Tiers 1 and 2, more than 30% recovered at least partial independence (GOSE ≥4). In Tier 3, recovery to GOSE ≥4 occurred at 12 months, but not 6 months, post-injury. These results suggest a substantial proportion of patients with TBI and WLST may have survived and achieved at least partial independence. However, death or severe disability is a common outcome when the probability of WLST is high. While further validation is needed, our findings support a more cautious clinical approach to WLST and more complete reporting on WLST in TBI studies.
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Affiliation(s)
- William R Sanders
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, USA
| | - Jason K Barber
- Department of Neurological Surgery, University of Washington, Seattle, Washington, USA
| | - Nancy R Temkin
- Department of Neurological Surgery, University of Washington, Seattle, Washington, USA
- Department of Biostatistics, University of Washington, Seattle, Washington, USA
| | - Brandon Foreman
- Department of Neurology and Rehabilitation Medicine, University of Cincinnati, Cincinnati, Ohio, USA
| | - Joseph T Giacino
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Charlestown, Massachusetts, USA
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Boston, Massachusetts, USA
| | - Theresa Williamson
- Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Brian L Edlow
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA
| | - Geoffrey T Manley
- Department of Neurological Surgery, University of California San Francisco, San Francisco, California, USA
| | - Yelena G Bodien
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Charlestown, Massachusetts, USA
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Boston, Massachusetts, USA
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7
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Fischer D, Edlow BL. Coma Prognostication After Acute Brain Injury: A Review. JAMA Neurol 2024:2815829. [PMID: 38436946 DOI: 10.1001/jamaneurol.2023.5634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2024]
Abstract
Importance Among the most impactful neurologic assessments is that of neuroprognostication, defined here as the prediction of neurologic recovery from disorders of consciousness caused by severe, acute brain injury. Across a range of brain injury etiologies, these determinations often dictate whether life-sustaining treatment is continued or withdrawn; thus, they have major implications for morbidity, mortality, and health care costs. Neuroprognostication relies on a diverse array of tests, including behavioral, radiologic, physiological, and serologic markers, that evaluate the brain's functional and structural integrity. Observations Prognostic markers, such as the neurologic examination, electroencephalography, and conventional computed tomography and magnetic resonance imaging (MRI), have been foundational in assessing a patient's current level of consciousness and capacity for recovery. Emerging techniques, such as functional MRI, diffusion MRI, and advanced forms of electroencephalography, provide new ways of evaluating the brain, leading to evolving schemes for characterizing neurologic function and novel methods for predicting recovery. Conclusions and Relevance Neuroprognostic markers are rapidly evolving as new ways of assessing the brain's structural and functional integrity after brain injury are discovered. Many of these techniques remain in development, and further research is needed to optimize their prognostic utility. However, even as such efforts are underway, a series of promising findings coupled with the imperfect predictive value of conventional prognostic markers and the high stakes of these assessments have prompted clinical guidelines to endorse emerging techniques for neuroprognostication. Thus, clinicians have been thrust into an uncertain predicament in which emerging techniques are not yet perfected but too promising to ignore. This review illustrates the current, and likely future, landscapes of prognostic markers. No matter how much prognostic markers evolve and improve, these assessments must be approached with humility and individualized to reflect each patient's values.
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Affiliation(s)
- David Fischer
- Division of Neurocritical Care, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Brian L Edlow
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown
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8
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Robba C, Zanier ER, Lopez Soto C, Park S, Sonneville R, Helbolk R, Sarwal A, Newcombe VFJ, van der Jagt M, Gunst J, Gauss T, Figueiredo S, Duranteau J, Skrifvars MB, Iaquaniello C, Muehlschlegel S, Metaxa V, Sandroni C, Citerio G, Meyfroidt G. Mastering the brain in critical conditions: an update. Intensive Care Med Exp 2024; 12:1. [PMID: 38182945 PMCID: PMC10770006 DOI: 10.1186/s40635-023-00587-3] [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: 11/28/2023] [Accepted: 12/18/2023] [Indexed: 01/07/2024] Open
Abstract
Acute brain injuries, such as traumatic brain injury and ischemic and hemorragic stroke, are a leading cause of death and disability worldwide. While characterized by clearly distict primary events-vascular damage in strokes and biomechanical damage in traumatic brain injuries-they share common secondary injury mechanisms influencing long-term outcomes. Growing evidence suggests that a more personalized approach to optimize energy substrate delivery to the injured brain and prognosticate towards families could be beneficial. In this context, continuous invasive and/or non-invasive neuromonitoring, together with clinical evaluation and neuroimaging to support strategies that optimize cerebral blood flow and metabolic delivery, as well as approaches to neuroprognostication are gaining interest. Recently, the European Society of Intensive Care Medicine organized a 2-day course focused on a practical case-based clinical approach of acute brain-injured patients in different scenarios and on future perspectives to advance the management of this population. The aim of this manuscript is to update clinicians dealing with acute brain injured patients in the intensive care unit, describing current knowledge and clinical practice based on the insights presented during this course.
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Affiliation(s)
- Chiara Robba
- Anesthesia and Intensive Care, San Martino Policlinico Hospital, IRCCS for Oncology and Neurosciences, Genoa, Italy
- Department of Surgical Sciences and Integrated Diagnostics, University of Genoa, Genoa, Italy
| | - Elisa R Zanier
- Department of Acute Brain and Cardiovascular Injury, Mario Negri Institute for Pharmacological Research IRCCS, Milan, Italy.
| | - Carmen Lopez Soto
- Department of Critical Care, King's College Hospital NHS Foundation Trust, London, SE5 9RS, UK
| | - Soojin Park
- Departments of Neurology and Biomedical Informatics, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
| | - Romain Sonneville
- Department of Intensive Care Medicine, Hôpital Bichat-Claude Bernard, Université Paris Cité, INSERM UMR 1137, IAME, APHP.Nord, Paris, France
| | - Raimund Helbolk
- Neurological Intensive Care Unit, Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
- Department of Neurology, Johannes Kepler University, Linz, Austria
- Clinical Research Institute Neuroscience, Johannes Kepler University, Linz, Austria
| | - Aarti Sarwal
- Wake Forest Baptist Health Center, Winston-Salem, NC, USA
| | | | - Mathieu van der Jagt
- Department of Intensive Care Adults, Erasmus MC-University Medical Centre, Room Ne-415, PO BOX 2040, 3000 CA, Rotterdam, The Netherlands
| | - Jan Gunst
- Department of Intensive Care Medicine, University Hospitals Leuven, Leuven, Belgium
- Laboratory of Intensive Care Medicine, Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium
| | - Tobias Gauss
- Department of Anaesthesia and Intensive Care, Centre Hospitalier Universitaire Grenoble, Universitaire Grenoble Alpes, Grenoble, France
- INSERM U1216, Grenoble Institut Neurosciences, Grenoble, France
| | - Samy Figueiredo
- Department of Anaesthesiology and Critical Care Medicine, Bicêtre Hospital, Université Paris-Saclay, Assistance Publique des Hôpitaux de Paris, Équipe DYNAMIC, Inserm UMR 999, Le Kremlin-Bicêtre, France
| | - Jacques Duranteau
- Department of Anaesthesiology and Critical Care Medicine, Bicêtre Hospital, Université Paris-Saclay, Assistance Publique des Hôpitaux de Paris, Équipe DYNAMIC, Inserm UMR 999, Le Kremlin-Bicêtre, France
| | - Markus B Skrifvars
- Department of Emergency Care and Services, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Carolina Iaquaniello
- Neuroanesthesia and Intensive Care, Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Susanne Muehlschlegel
- Division of Neurosciences Critical Care, Departments of Neurology and Anesthesiology/Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Victoria Metaxa
- Department of Critical Care, King's College Hospital NHS Foundation Trust, London, SE5 9RS, UK
| | - Claudio Sandroni
- Department of Intensive Care, Emergency Medicine and Anaesthesiology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Rome, Italy
- Institute of Anaesthesiology and Intensive Care Medicine, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Giuseppe Citerio
- School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
| | - Geert Meyfroidt
- Department of Intensive Care Medicine, University Hospitals Leuven, Leuven, Belgium
- Laboratory of Intensive Care Medicine, Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium
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9
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Snider SB, Temkin NR, Barber J, Edlow BL, Giacino JT, Hammond FM, Izzy S, Kowalski RG, Markowitz AJ, Rovito CA, Shih SL, Zafonte RD, Manley GT, Bodien YG. Predicting Functional Dependency in Patients with Disorders of Consciousness: A TBI-Model Systems and TRACK-TBI Study. Ann Neurol 2023; 94:1008-1023. [PMID: 37470289 PMCID: PMC10799195 DOI: 10.1002/ana.26741] [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: 03/28/2023] [Revised: 06/27/2023] [Accepted: 06/29/2023] [Indexed: 07/21/2023]
Abstract
OBJECTIVE It is not currently possible to predict long-term functional dependency in patients with disorders of consciousness (DoC) after traumatic brain injury (TBI). Our objective was to fit and externally validate a prediction model for 1-year dependency in patients with DoC ≥ 2 weeks after TBI. METHODS We included adults with TBI enrolled in TBI Model Systems (TBI-MS) or Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) studies who were not following commands at rehabilitation admission or 2 weeks post-injury, respectively. We fit a logistic regression model in TBI-MS and validated it in TRACK-TBI. The primary outcome was death or dependency at 1 year post-injury, defined using the Disability Rating Scale. RESULTS In the TBI-MS Discovery Sample, 1,960 participants (mean age 40 [18] years, 76% male, 68% white) met inclusion criteria, and 406 (27%) were dependent 1 year post-injury. In a TBI-MS held out cohort, the dependency prediction model's area under the receiver operating characteristic curve was 0.79 (95% CI 0.74-0.85), positive predictive value was 53% and negative predictive value was 86%. In the TRACK-TBI external validation (n = 124, age 40 [16] years, 77% male, 81% white), the area under the receiver operating characteristic curve was 0.66 (0.53, 0.79), equivalent to the standard IMPACTcore + CT score (p = 0.8). INTERPRETATION We developed a 1-year dependency prediction model using the largest existing cohort of patients with DoC after TBI. The sensitivity and negative predictive values were greater than specificity and positive predictive values. Accuracy was diminished in an external sample, but equivalent to the IMPACT model. Further research is needed to improve dependency prediction in patients with DoC after TBI. ANN NEUROL 2023;94:1008-1023.
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Affiliation(s)
- Samuel B. Snider
- Division of Neurocritical Care, Department of Neurology, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Nancy R. Temkin
- Department of Neurological Surgery, University of Washington, Seattle, Washington, USA
- Department of Biostatistics, University of Washington, Seattle, Washington, USA
| | - Jason Barber
- Department of Neurological Surgery, University of Washington, Seattle, Washington, USA
| | - Brian L. Edlow
- Harvard Medical School, Boston, MA, USA
- Center for Neurotechnology and Neurorecovery and Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Joseph T. Giacino
- Harvard Medical School, Boston, MA, USA
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Boston, MA USA
| | - Flora M. Hammond
- Department of Physical Medicine and Rehabilitation, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Saef Izzy
- Division of Neurocritical Care, Department of Neurology, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Robert G. Kowalski
- Departments of Neurosurgery and Neurology, University of Colorado School of Medicine, Aurora CO, USA
| | | | - Craig A. Rovito
- Harvard Medical School, Boston, MA, USA
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Boston, MA USA
| | - Shirley L. Shih
- Harvard Medical School, Boston, MA, USA
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Boston, MA USA
| | - Ross D. Zafonte
- Harvard Medical School, Boston, MA, USA
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Boston, MA USA
| | - Geoffrey T. Manley
- Department of Neurological Surgery, UCSF, San Francisco, CA USA
- Brain and Spinal Cord Injury Center, Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA USA
| | - Yelena G. Bodien
- Harvard Medical School, Boston, MA, USA
- Center for Neurotechnology and Neurorecovery and Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Boston, MA USA
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10
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Edlow BL, Boerwinkle VL, Annen J, Boly M, Gosseries O, Laureys S, Mukherjee P, Puybasset L, Stevens RD, Threlkeld ZD, Newcombe VFJ, Fernandez-Espejo D. Common Data Elements for Disorders of Consciousness: Recommendations from the Working Group on Neuroimaging. Neurocrit Care 2023; 39:611-617. [PMID: 37552410 DOI: 10.1007/s12028-023-01794-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 06/22/2023] [Indexed: 08/09/2023]
Abstract
BACKGROUND Over the past 5 decades, advances in neuroimaging have yielded insights into the pathophysiologic mechanisms that cause disorders of consciousness (DoC) in patients with severe brain injuries. Structural, functional, metabolic, and perfusion imaging studies have revealed specific neuroanatomic regions, such as the brainstem tegmentum, thalamus, posterior cingulate cortex, medial prefrontal cortex, and occipital cortex, where lesions correlate with the current or future state of consciousness. Advanced imaging modalities, such as diffusion tensor imaging, resting-state functional magnetic resonance imaging (fMRI), and task-based fMRI, have been used to improve the accuracy of diagnosis and long-term prognosis, culminating in the endorsement of fMRI for the clinical evaluation of patients with DoC in the 2018 US (task-based fMRI) and 2020 European (task-based and resting-state fMRI) guidelines. As diverse neuroimaging techniques are increasingly used for patients with DoC in research and clinical settings, the need for a standardized approach to reporting results is clear. The success of future multicenter collaborations and international trials fundamentally depends on the implementation of a shared nomenclature and infrastructure. METHODS To address this need, the Neurocritical Care Society's Curing Coma Campaign convened an international panel of DoC neuroimaging experts to propose common data elements (CDEs) for data collection and reporting in this field. RESULTS We report the recommendations of this CDE development panel and disseminate CDEs to be used in neuroimaging studies of patients with DoC. CONCLUSIONS These CDEs will support progress in the field of DoC neuroimaging and facilitate international collaboration.
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Affiliation(s)
- Brian L Edlow
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.
| | - Varina L Boerwinkle
- Clinical Resting-State Functional Magnetic Resonance Imaging Laboratory and Service, Department of Neurology, University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - Jitka Annen
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium
- Centre de Cerveau2, University Hospital of Liège, Liège, Belgium
| | - Melanie Boly
- Department of Neurology, University of Wisconsin, Madison, WI, USA
- Department of Psychiatry, Wisconsin Institute for Sleep and Consciousness, University of Wisconsin, Madison, WI, USA
| | - Olivia Gosseries
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium
- Centre de Cerveau2, University Hospital of Liège, Liège, Belgium
| | - Steven Laureys
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium
- Centre de Cerveau2, University Hospital of Liège, Liège, Belgium
- CERVO Research Institute, Laval University, Quebec, Canada
| | - Pratik Mukherjee
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
| | - Louis Puybasset
- Department of Anesthesiology and Intensive Care, Groupe Hospitalier Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Robert D Stevens
- Departments of Anesthesiology and Critical Care Medicine, Neurology, Radiology, and Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Zachary D Threlkeld
- Department of Neurology, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Davinia Fernandez-Espejo
- School of Psychology and Centre for Human Brain Health, University of Birmingham, Birmingham, UK
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11
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Caruso PF, Greco M, Ebm C, Angelotti G, Cecconi M. Implementing Artificial Intelligence: Assessing the Cost and Benefits of Algorithmic Decision-Making in Critical Care. Crit Care Clin 2023; 39:783-793. [PMID: 37704340 DOI: 10.1016/j.ccc.2023.03.007] [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] [Indexed: 09/15/2023]
Abstract
This article provides an overview of the most useful artificial intelligence algorithms developed in critical care, followed by a comprehensive outline of the benefits and limitations. We begin by describing how nurses and physicians might be aided by these new technologies. We then move to the possible changes in clinical guidelines with personalized medicine that will allow tailored therapies and probably will increase the quality of the care provided to patients. Finally, we describe how artificial intelligence models can unleash researchers' minds by proposing new strategies, by increasing the quality of clinical practice, and by questioning current knowledge and understanding.
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Affiliation(s)
- Pier Francesco Caruso
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, Milan 20072, Italy; Department of Anesthesiology and Intensive Care, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, Milan 20089, Italy
| | - Massimiliano Greco
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, Milan 20072, Italy; Department of Anesthesiology and Intensive Care, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, Milan 20089, Italy.
| | - Claudia Ebm
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, Milan 20072, Italy
| | - Giovanni Angelotti
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, Milan 20089, Italy
| | - Maurizio Cecconi
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, Milan 20072, Italy; Department of Anesthesiology and Intensive Care, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, Milan 20089, Italy
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12
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Siqueira Pinto M, Winzeck S, Kornaropoulos EN, Richter S, Paolella R, Correia MM, Glocker B, Williams G, Vik A, Posti JP, Haberg A, Stenberg J, Guns PJ, den Dekker AJ, Menon DK, Sijbers J, Van Dyck P, Newcombe VFJ. Use of Support Vector Machines Approach via ComBat Harmonized Diffusion Tensor Imaging for the Diagnosis and Prognosis of Mild Traumatic Brain Injury: A CENTER-TBI Study. J Neurotrauma 2023; 40:1317-1338. [PMID: 36974359 DOI: 10.1089/neu.2022.0365] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023] Open
Abstract
The prediction of functional outcome after mild traumatic brain injury (mTBI) is challenging. Conventional magnetic resonance imaging (MRI) does not do a good job of explaining the variance in outcome, as many patients with incomplete recovery will have normal-appearing clinical neuroimaging. More advanced quantitative techniques such as diffusion MRI (dMRI), can detect microstructural changes not otherwise visible, and so may offer a way to improve outcome prediction. In this study, we explore the potential of linear support vector classifiers (linearSVCs) to identify dMRI biomarkers that can predict recovery after mTBI. Simultaneously, the harmonization of fractional anisotropy (FA) and mean diffusivity (MD) via ComBat was evaluated and compared for the classification performances of the linearSVCs. We included dMRI scans of 179 mTBI patients and 85 controls from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI), a multi-center prospective cohort study, up to 21 days post-injury. Patients were dichotomized according to their Extended Glasgow Outcome Scale (GOSE) scores at 6 months into complete (n = 92; GOSE = 8) and incomplete (n = 87; GOSE <8) recovery. FA and MD maps were registered to a common space and harmonized via the ComBat algorithm. LinearSVCs were applied to distinguish: (1) mTBI patients from controls and (2) mTBI patients with complete from those with incomplete recovery. The linearSVCs were trained on (1) age and sex only, (2) non-harmonized, (3) two-category-harmonized ComBat, and (4) three-category-harmonized ComBat FA and MD images combined with age and sex. White matter FA and MD voxels and regions of interest (ROIs) within the John Hopkins University (JHU) atlas were examined. Recursive feature elimination was used to identify the 10% most discriminative voxels or the 10 most discriminative ROIs for each implementation. mTBI patients displayed significantly higher MD and lower FA values than controls for the discriminative voxels and ROIs. For the analysis between mTBI patients and controls, the three-category-harmonized ComBat FA and MD voxel-wise linearSVC provided significantly higher classification scores (81.4% accuracy, 93.3% sensitivity, 80.3% F1-score, and 0.88 area under the curve [AUC], p < 0.05) compared with the classification based on age and sex only and the ROI approaches (accuracies: 59.8% and 64.8%, respectively). Similar to the analysis between mTBI patients and controls, the three-category-harmonized ComBat FA and MD maps voxelwise approach yields statistically significant prediction scores between mTBI patients with complete and those with incomplete recovery (71.8% specificity, 66.2% F1-score and 0.71 AUC, p < 0.05), which provided a modest increase in the classification score (accuracy: 66.4%) compared with the classification based on age and sex only and ROI-wise approaches (accuracy: 61.4% and 64.7%, respectively). This study showed that ComBat harmonized FA and MD may provide additional information for diagnosis and prognosis of mTBI in a multi-modal machine learning approach. These findings demonstrate that dMRI may assist in the early detection of patients at risk of incomplete recovery from mTBI.
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Affiliation(s)
- Maíra Siqueira Pinto
- Department of Radiology, Antwerp University Hospital, Antwerp, Belgium
- imec-Vision Lab, University of Antwerp, Antwerp, Belgium
- μNEURO Research Center of Excellence, University of Antwerp, Antwerp, Belgium
| | - Stefan Winzeck
- BioMedIA Group, Department of Computing, Imperial College London, London, United Kingdom
- Division of Anaesthesia, Department of Medicine, Department of Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Evgenios N Kornaropoulos
- Division of Anaesthesia, Department of Medicine, Department of Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Sophie Richter
- Division of Anaesthesia, Department of Medicine, Department of Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Roberto Paolella
- imec-Vision Lab, University of Antwerp, Antwerp, Belgium
- μNEURO Research Center of Excellence, University of Antwerp, Antwerp, Belgium
- Icometrix, Leuven, Belgium
| | - Marta M Correia
- MRC Cognition and Brain Sciences Unit, Department of Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Ben Glocker
- BioMedIA Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Guy Williams
- Wolfson Brain Imaging Centre, Department of Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Anne Vik
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
- Department of Neurosurgery, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Jussi P Posti
- Department of Neurosurgery and Turku Brain Injury Center, Turku University Hospital and University of Turku, Turku, Finland
| | - Asta Haberg
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Jonas Stenberg
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | | | - Arnold J den Dekker
- imec-Vision Lab, University of Antwerp, Antwerp, Belgium
- μNEURO Research Center of Excellence, University of Antwerp, Antwerp, Belgium
| | - David K Menon
- Division of Anaesthesia, Department of Medicine, Department of Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Jan Sijbers
- imec-Vision Lab, University of Antwerp, Antwerp, Belgium
- μNEURO Research Center of Excellence, University of Antwerp, Antwerp, Belgium
| | - Pieter Van Dyck
- Department of Radiology, Antwerp University Hospital, Antwerp, Belgium
- mVISION, University of Antwerp, Antwerp, Belgium
| | - Virginia F J Newcombe
- Division of Anaesthesia, Department of Medicine, Department of Neurosciences, University of Cambridge, Cambridge, United Kingdom
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Nasrallah F, Bellapart J, Walsham J, Jacobson E, To XV, Manzanero S, Brown N, Meyer J, Stuart J, Evans T, Chandra SS, Ross J, Campbell L, Senthuran S, Newcombe V, McCullough J, Fleming J, Pollard C, Reade M. PREdiction and Diagnosis using Imaging and Clinical biomarkers Trial in Traumatic Brain Injury (PREDICT-TBI) study protocol: an observational, prospective, multicentre cohort study for the prediction of outcome in moderate-to-severe TBI. BMJ Open 2023; 13:e067740. [PMID: 37094888 PMCID: PMC10151972 DOI: 10.1136/bmjopen-2022-067740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/26/2023] Open
Abstract
INTRODUCTION Traumatic brain injury (TBI) is a heterogeneous condition with a broad spectrum of injury severity, pathophysiological processes and variable outcomes. For moderate-to-severe TBI survivors, recovery is often protracted and outcomes can range from total dependence to full recovery. Despite advances in medical treatment options, prognosis remains largely unchanged. The objective of this study is to develop a machine learning predictive model for neurological outcomes at 6 months in patients with a moderate-to-severe TBI, incorporating longitudinal clinical, multimodal neuroimaging and blood biomarker predictor variables. METHODS AND ANALYSIS A prospective, observational, cohort study will enrol 300 patients with moderate-to-severe TBI from seven Australian hospitals over 3 years. Candidate predictors including demographic and general health variables, and longitudinal clinical, neuroimaging (CT and MRI), blood biomarker and patient-reported outcome measures will be collected at multiple time points within the acute phase of injury. The predictor variables will populate novel machine learning models to predict the Glasgow Outcome Scale Extended 6 months after injury. The study will also expand on current prognostic models by including novel blood biomarkers (circulating cell-free DNA), and the results of quantitative neuroimaging such as Quantitative Susceptibility Mapping and Dynamic Contrast Enhanced MRI as predictor variables. ETHICS AND DISSEMINATION Ethical approval has been obtained by the Royal Brisbane and Women's Hospital Human Research Ethics Committee, Queensland. Participants or their substitute decision-maker/s will receive oral and written information about the study before providing written informed consent. Study findings will be disseminated by peer-review publications and presented at national and international conferences and clinical networks. TRIAL REGISTRATION NUMBER ACTRN12620001360909.
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Affiliation(s)
- Fatima Nasrallah
- The Queensland Brain Institute, The University of Queensland, Saint Lucia, Queensland, Australia
| | - Judith Bellapart
- Intensive Care Unit, Royal Brisbane and Women's Hospital, Metro North Health Service District, Herston, Queensland, Australia
| | - James Walsham
- Intensive Care Unit, Princess Alexandra Hospital, Woolloongabba, Queensland, Australia
- Faculty of Medicine, The University of Queensland, Saint Lucia, Queensland, Australia
| | - Esther Jacobson
- Jamieson Trauma Institute, Metro North Health Service District, Herston, Queensland, Australia
| | - Xuan Vinh To
- The Queensland Brain Institute, The University of Queensland, Saint Lucia, Queensland, Australia
| | - Silvia Manzanero
- Jamieson Trauma Institute, Metro North Health Service District, Herston, Queensland, Australia
| | - Nathan Brown
- Intensive Care Unit, Royal Brisbane and Women's Hospital, Metro North Health Service District, Herston, Queensland, Australia
- Faculty of Medicine, The University of Queensland, Saint Lucia, Queensland, Australia
| | - Jason Meyer
- Intensive Care Unit, Princess Alexandra Hospital, Woolloongabba, Queensland, Australia
| | - Janine Stuart
- Intensive Care Unit, Royal Brisbane and Women's Hospital, Metro North Health Service District, Herston, Queensland, Australia
| | - Tracey Evans
- The Queensland Brain Institute, The University of Queensland, Saint Lucia, Queensland, Australia
| | - Shekhar S Chandra
- School of Information Technology and Electrical Engineering, Architecture and Information Technology, The University of Queensland, Saint Lucia, Queensland, Australia
| | - Jason Ross
- Health and Biosecurity, CSIRO, Westmead, New South Wales, Australia
| | - Lewis Campbell
- Intensive Care Unit, Royal Darwin Hospital, Casuarina, Darwin, Australia
- College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia
| | - Siva Senthuran
- Intensive Care Unit, Townsville Hospital and Health Service, Townsville, Queensland, Australia
| | - Virginia Newcombe
- University Division of Anaesthesia, University of Cambridge, Cambridge, UK
| | - James McCullough
- Intensive Care Unit, Gold Coast Hospital and Health Service, Gold Coast, Queensland, Australia
| | - Jennifer Fleming
- School of Health and Rehabilitation Sciences, The University of Queensland, Saint Lucia, Queensland, Australia
| | - Clifford Pollard
- School of Information Technology and Electrical Engineering, Architecture and Information Technology, The University of Queensland, Saint Lucia, Queensland, Australia
| | - Michael Reade
- Intensive Care Unit, Royal Brisbane and Women's Hospital, Metro North Health Service District, Herston, Queensland, Australia
- Faculty of Medicine, The University of Queensland, Saint Lucia, Queensland, Australia
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14
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Pizcueta P, Vergara C, Emanuele M, Vilalta A, Rodríguez-Pascau L, Martinell M. Development of PPARγ Agonists for the Treatment of Neuroinflammatory and Neurodegenerative Diseases: Leriglitazone as a Promising Candidate. Int J Mol Sci 2023; 24:ijms24043201. [PMID: 36834611 PMCID: PMC9961553 DOI: 10.3390/ijms24043201] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 01/21/2023] [Accepted: 02/01/2023] [Indexed: 02/08/2023] Open
Abstract
Increasing evidence suggests that the peroxisome proliferator-activated receptor γ (PPARγ), a member of the nuclear receptor superfamily, plays an important role in physiological processes in the central nervous system (CNS) and is involved in cellular metabolism and repair. Cellular damage caused by acute brain injury and long-term neurodegenerative disorders is associated with alterations of these metabolic processes leading to mitochondrial dysfunction, oxidative stress, and neuroinflammation. PPARγ agonists have demonstrated the potential to be effective treatments for CNS diseases in preclinical models, but to date, most drugs have failed to show efficacy in clinical trials of neurodegenerative diseases including amyotrophic lateral sclerosis, Parkinson's disease, and Alzheimer's disease. The most likely explanation for this lack of efficacy is the insufficient brain exposure of these PPARγ agonists. Leriglitazone is a novel, blood-brain barrier (BBB)-penetrant PPARγ agonist that is being developed to treat CNS diseases. Here, we review the main roles of PPARγ in physiology and pathophysiology in the CNS, describe the mechanism of action of PPARγ agonists, and discuss the evidence supporting the use of leriglitazone to treat CNS diseases.
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Affiliation(s)
- Pilar Pizcueta
- Minoryx Therapeutics SL, 08302 Barcelona, Spain
- Correspondence:
| | | | - Marco Emanuele
- Minoryx Therapeutics BE, Gosselies, 6041 Charleroi, Belgium
| | | | | | - Marc Martinell
- Minoryx Therapeutics SL, 08302 Barcelona, Spain
- Minoryx Therapeutics BE, Gosselies, 6041 Charleroi, Belgium
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15
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Chabanne R, Gergele L, Launey Y. Intracranial pressure monitoring devices compatibility with magnetic resonance imaging. Intensive Care Med 2023; 49:472-474. [PMID: 36688975 DOI: 10.1007/s00134-022-06970-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/26/2022] [Indexed: 01/24/2023]
Affiliation(s)
- Russell Chabanne
- Pole de Médecine Périopératoire, Centre Hospitalier Universitaire (CHU) de Clermont-Ferrand, Clermont-Ferrand, France.
| | - Laurent Gergele
- Département Anesthésie Réanimation, Ramsay Santé, Hôpital Privé de la Loire, Saint-Etienne, France
| | - Yoann Launey
- Département Anesthésie Réanimation et Médecine Périopératoire, Centre Hospitalier Universitaire (CHU) Rennes, Rennes, France
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16
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Maas AIR, Menon DK, Manley GT, Abrams M, Åkerlund C, Andelic N, Aries M, Bashford T, Bell MJ, Bodien YG, Brett BL, Büki A, Chesnut RM, Citerio G, Clark D, Clasby B, Cooper DJ, Czeiter E, Czosnyka M, Dams-O’Connor K, De Keyser V, Diaz-Arrastia R, Ercole A, van Essen TA, Falvey É, Ferguson AR, Figaji A, Fitzgerald M, Foreman B, Gantner D, Gao G, Giacino J, Gravesteijn B, Guiza F, Gupta D, Gurnell M, Haagsma JA, Hammond FM, Hawryluk G, Hutchinson P, van der Jagt M, Jain S, Jain S, Jiang JY, Kent H, Kolias A, Kompanje EJO, Lecky F, Lingsma HF, Maegele M, Majdan M, Markowitz A, McCrea M, Meyfroidt G, Mikolić A, Mondello S, Mukherjee P, Nelson D, Nelson LD, Newcombe V, Okonkwo D, Orešič M, Peul W, Pisică D, Polinder S, Ponsford J, Puybasset L, Raj R, Robba C, Røe C, Rosand J, Schueler P, Sharp DJ, Smielewski P, Stein MB, von Steinbüchel N, Stewart W, Steyerberg EW, Stocchetti N, Temkin N, Tenovuo O, Theadom A, Thomas I, Espin AT, Turgeon AF, Unterberg A, Van Praag D, van Veen E, Verheyden J, Vyvere TV, Wang KKW, Wiegers EJA, Williams WH, Wilson L, Wisniewski SR, Younsi A, Yue JK, Yuh EL, Zeiler FA, Zeldovich M, Zemek R. Traumatic brain injury: progress and challenges in prevention, clinical care, and research. Lancet Neurol 2022; 21:1004-1060. [PMID: 36183712 PMCID: PMC10427240 DOI: 10.1016/s1474-4422(22)00309-x] [Citation(s) in RCA: 245] [Impact Index Per Article: 122.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 07/22/2022] [Indexed: 02/06/2023]
Abstract
Traumatic brain injury (TBI) has the highest incidence of all common neurological disorders, and poses a substantial public health burden. TBI is increasingly documented not only as an acute condition but also as a chronic disease with long-term consequences, including an increased risk of late-onset neurodegeneration. The first Lancet Neurology Commission on TBI, published in 2017, called for a concerted effort to tackle the global health problem posed by TBI. Since then, funding agencies have supported research both in high-income countries (HICs) and in low-income and middle-income countries (LMICs). In November 2020, the World Health Assembly, the decision-making body of WHO, passed resolution WHA73.10 for global actions on epilepsy and other neurological disorders, and WHO launched the Decade for Action on Road Safety plan in 2021. New knowledge has been generated by large observational studies, including those conducted under the umbrella of the International Traumatic Brain Injury Research (InTBIR) initiative, established as a collaboration of funding agencies in 2011. InTBIR has also provided a huge stimulus to collaborative research in TBI and has facilitated participation of global partners. The return on investment has been high, but many needs of patients with TBI remain unaddressed. This update to the 2017 Commission presents advances and discusses persisting and new challenges in prevention, clinical care, and research. In LMICs, the occurrence of TBI is driven by road traffic incidents, often involving vulnerable road users such as motorcyclists and pedestrians. In HICs, most TBI is caused by falls, particularly in older people (aged ≥65 years), who often have comorbidities. Risk factors such as frailty and alcohol misuse provide opportunities for targeted prevention actions. Little evidence exists to inform treatment of older patients, who have been commonly excluded from past clinical trials—consequently, appropriate evidence is urgently required. Although increasing age is associated with worse outcomes from TBI, age should not dictate limitations in therapy. However, patients injured by low-energy falls (who are mostly older people) are about 50% less likely to receive critical care or emergency interventions, compared with those injured by high-energy mechanisms, such as road traffic incidents. Mild TBI, defined as a Glasgow Coma sum score of 13–15, comprises most of the TBI cases (over 90%) presenting to hospital. Around 50% of adult patients with mild TBI presenting to hospital do not recover to pre-TBI levels of health by 6 months after their injury. Fewer than 10% of patients discharged after presenting to an emergency department for TBI in Europe currently receive follow-up. Structured follow-up after mild TBI should be considered good practice, and urgent research is needed to identify which patients with mild TBI are at risk for incomplete recovery. The selection of patients for CT is an important triage decision in mild TBI since it allows early identification of lesions that can trigger hospital admission or life-saving surgery. Current decision making for deciding on CT is inefficient, with 90–95% of scanned patients showing no intracranial injury but being subjected to radiation risks. InTBIR studies have shown that measurement of blood-based biomarkers adds value to previously proposed clinical decision rules, holding the potential to improve efficiency while reducing radiation exposure. Increased concentrations of biomarkers in the blood of patients with a normal presentation CT scan suggest structural brain damage, which is seen on MR scanning in up to 30% of patients with mild TBI. Advanced MRI, including diffusion tensor imaging and volumetric analyses, can identify additional injuries not detectable by visual inspection of standard clinical MR images. Thus, the absence of CT abnormalities does not exclude structural damage—an observation relevant to litigation procedures, to management of mild TBI, and when CT scans are insufficient to explain the severity of the clinical condition. Although blood-based protein biomarkers have been shown to have important roles in the evaluation of TBI, most available assays are for research use only. To date, there is only one vendor of such assays with regulatory clearance in Europe and the USA with an indication to rule out the need for CT imaging for patients with suspected TBI. Regulatory clearance is provided for a combination of biomarkers, although evidence is accumulating that a single biomarker can perform as well as a combination. Additional biomarkers and more clinical-use platforms are on the horizon, but cross-platform harmonisation of results is needed. Health-care efficiency would benefit from diversity in providers. In the intensive care setting, automated analysis of blood pressure and intracranial pressure with calculation of derived parameters can help individualise management of TBI. Interest in the identification of subgroups of patients who might benefit more from some specific therapeutic approaches than others represents a welcome shift towards precision medicine. Comparative-effectiveness research to identify best practice has delivered on expectations for providing evidence in support of best practices, both in adult and paediatric patients with TBI. Progress has also been made in improving outcome assessment after TBI. Key instruments have been translated into up to 20 languages and linguistically validated, and are now internationally available for clinical and research use. TBI affects multiple domains of functioning, and outcomes are affected by personal characteristics and life-course events, consistent with a multifactorial bio-psycho-socio-ecological model of TBI, as presented in the US National Academies of Sciences, Engineering, and Medicine (NASEM) 2022 report. Multidimensional assessment is desirable and might be best based on measurement of global functional impairment. More work is required to develop and implement recommendations for multidimensional assessment. Prediction of outcome is relevant to patients and their families, and can facilitate the benchmarking of quality of care. InTBIR studies have identified new building blocks (eg, blood biomarkers and quantitative CT analysis) to refine existing prognostic models. Further improvement in prognostication could come from MRI, genetics, and the integration of dynamic changes in patient status after presentation. Neurotrauma researchers traditionally seek translation of their research findings through publications, clinical guidelines, and industry collaborations. However, to effectively impact clinical care and outcome, interactions are also needed with research funders, regulators, and policy makers, and partnership with patient organisations. Such interactions are increasingly taking place, with exemplars including interactions with the All Party Parliamentary Group on Acquired Brain Injury in the UK, the production of the NASEM report in the USA, and interactions with the US Food and Drug Administration. More interactions should be encouraged, and future discussions with regulators should include debates around consent from patients with acute mental incapacity and data sharing. Data sharing is strongly advocated by funding agencies. From January 2023, the US National Institutes of Health will require upload of research data into public repositories, but the EU requires data controllers to safeguard data security and privacy regulation. The tension between open data-sharing and adherence to privacy regulation could be resolved by cross-dataset analyses on federated platforms, with the data remaining at their original safe location. Tools already exist for conventional statistical analyses on federated platforms, however federated machine learning requires further development. Support for further development of federated platforms, and neuroinformatics more generally, should be a priority. This update to the 2017 Commission presents new insights and challenges across a range of topics around TBI: epidemiology and prevention (section 1 ); system of care (section 2 ); clinical management (section 3 ); characterisation of TBI (section 4 ); outcome assessment (section 5 ); prognosis (Section 6 ); and new directions for acquiring and implementing evidence (section 7 ). Table 1 summarises key messages from this Commission and proposes recommendations for the way forward to advance research and clinical management of TBI.
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Affiliation(s)
- Andrew I R Maas
- Department of Neurosurgery, Antwerp University Hospital and University of Antwerp, Edegem, Belgium
| | - David K Menon
- Division of Anaesthesia, University of Cambridge, Addenbrooke’s Hospital, Cambridge, UK
| | - Geoffrey T Manley
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
| | - Mathew Abrams
- International Neuroinformatics Coordinating Facility, Karolinska Institutet, Stockholm, Sweden
| | - Cecilia Åkerlund
- Department of Physiology and Pharmacology, Section of Perioperative Medicine and Intensive Care, Karolinska Institutet, Stockholm, Sweden
| | - Nada Andelic
- Division of Clinical Neuroscience, Department of Physical Medicine and Rehabilitation, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - Marcel Aries
- Department of Intensive Care, Maastricht UMC, Maastricht, Netherlands
| | - Tom Bashford
- Division of Anaesthesia, University of Cambridge, Addenbrooke’s Hospital, Cambridge, UK
| | - Michael J Bell
- Critical Care Medicine, Neurological Surgery and Pediatrics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Yelena G Bodien
- Department of Neurology and Department of Physical Medicine and Rehabilitation, Harvard Medical School, Boston, MA, USA
| | - Benjamin L Brett
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, USA
| | - András Büki
- Department of Neurosurgery, Faculty of Medicine and Health Örebro University, Örebro, Sweden
- Department of Neurosurgery, Medical School; ELKH-PTE Clinical Neuroscience MR Research Group; and Neurotrauma Research Group, Janos Szentagothai Research Centre, University of Pecs, Pecs, Hungary
| | - Randall M Chesnut
- Department of Neurological Surgery and Department of Orthopaedics and Sports Medicine, University of Washington, Harborview Medical Center, Seattle, WA, USA
| | - Giuseppe Citerio
- School of Medicine and Surgery, Universita Milano Bicocca, Milan, Italy
- NeuroIntensive Care, San Gerardo Hospital, Azienda Socio Sanitaria Territoriale (ASST) Monza, Monza, Italy
| | - David Clark
- Brain Physics Lab, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Addenbrooke’s Hospital, Cambridge, UK
| | - Betony Clasby
- Department of Sociological Studies, University of Sheffield, Sheffield, UK
| | - D Jamie Cooper
- School of Public Health and Preventive Medicine, Monash University and The Alfred Hospital, Melbourne, VIC, Australia
| | - Endre Czeiter
- Department of Neurosurgery, Medical School; ELKH-PTE Clinical Neuroscience MR Research Group; and Neurotrauma Research Group, Janos Szentagothai Research Centre, University of Pecs, Pecs, Hungary
| | - Marek Czosnyka
- Brain Physics Lab, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Addenbrooke’s Hospital, Cambridge, UK
| | - Kristen Dams-O’Connor
- Department of Rehabilitation and Human Performance and Department of Neurology, Brain Injury Research Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Véronique De Keyser
- Department of Neurosurgery, Antwerp University Hospital and University of Antwerp, Edegem, Belgium
| | - Ramon Diaz-Arrastia
- Department of Neurology and Center for Brain Injury and Repair, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Ari Ercole
- Division of Anaesthesia, University of Cambridge, Addenbrooke’s Hospital, Cambridge, UK
| | - Thomas A van Essen
- Department of Neurosurgery, Leiden University Medical Center, Leiden, Netherlands
- Department of Neurosurgery, Medical Center Haaglanden, The Hague, Netherlands
| | - Éanna Falvey
- College of Medicine and Health, University College Cork, Cork, Ireland
| | - Adam R Ferguson
- Brain and Spinal Injury Center, Department of Neurological Surgery, Weill Institute for Neurosciences, University of California San Francisco and San Francisco Veterans Affairs Healthcare System, San Francisco, CA, USA
| | - Anthony Figaji
- Division of Neurosurgery and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Melinda Fitzgerald
- Curtin Health Innovation Research Institute, Curtin University, Bentley, WA, Australia
- Perron Institute for Neurological and Translational Sciences, Nedlands, WA, Australia
| | - Brandon Foreman
- Department of Neurology and Rehabilitation Medicine, University of Cincinnati Gardner Neuroscience Institute, University of Cincinnati, Cincinnati, OH, USA
| | - Dashiell Gantner
- School of Public Health and Preventive Medicine, Monash University and The Alfred Hospital, Melbourne, VIC, Australia
| | - Guoyi Gao
- Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine
| | - Joseph Giacino
- Department of Physical Medicine and Rehabilitation, Harvard Medical School and Spaulding Rehabilitation Hospital, Charlestown, MA, USA
| | - Benjamin Gravesteijn
- Department of Public Health, Erasmus MC University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Fabian Guiza
- Department and Laboratory of Intensive Care Medicine, University Hospitals Leuven and KU Leuven, Leuven, Belgium
| | - Deepak Gupta
- Department of Neurosurgery, Neurosciences Centre and JPN Apex Trauma Centre, All India Institute of Medical Sciences, New Delhi, India
| | - Mark Gurnell
- Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Juanita A Haagsma
- Department of Public Health, Erasmus MC University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Flora M Hammond
- Department of Physical Medicine and Rehabilitation, Indiana University School of Medicine, Rehabilitation Hospital of Indiana, Indianapolis, IN, USA
| | - Gregory Hawryluk
- Section of Neurosurgery, GB1, Health Sciences Centre, University of Manitoba, Winnipeg, MB, Canada
| | - Peter Hutchinson
- Brain Physics Lab, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Addenbrooke’s Hospital, Cambridge, UK
| | - Mathieu van der Jagt
- Department of Intensive Care, Erasmus MC University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Sonia Jain
- Biostatistics Research Center, Herbert Wertheim School of Public Health, University of California, San Diego, CA, USA
| | - Swati Jain
- Brain Physics Lab, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Addenbrooke’s Hospital, Cambridge, UK
| | - Ji-yao Jiang
- Department of Neurosurgery, Shanghai Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Hope Kent
- Department of Psychology, University of Exeter, Exeter, UK
| | - Angelos Kolias
- Brain Physics Lab, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Addenbrooke’s Hospital, Cambridge, UK
| | - Erwin J O Kompanje
- Department of Intensive Care, Erasmus MC University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Fiona Lecky
- Centre for Urgent and Emergency Care Research, Health Services Research Section, School of Health and Related Research, University of Sheffield, Sheffield, UK
| | - Hester F Lingsma
- Department of Public Health, Erasmus MC University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Marc Maegele
- Cologne-Merheim Medical Center, Department of Trauma and Orthopedic Surgery, Witten/Herdecke University, Cologne, Germany
| | - Marek Majdan
- Institute for Global Health and Epidemiology, Department of Public Health, Faculty of Health Sciences and Social Work, Trnava University, Trnava, Slovakia
| | - Amy Markowitz
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
| | - Michael McCrea
- Department of Neurosurgery and Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Geert Meyfroidt
- Department and Laboratory of Intensive Care Medicine, University Hospitals Leuven and KU Leuven, Leuven, Belgium
| | - Ana Mikolić
- Department of Public Health, Erasmus MC University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Stefania Mondello
- Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, Messina, Italy
| | - Pratik Mukherjee
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - David Nelson
- Section for Anesthesiology and Intensive Care, Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Lindsay D Nelson
- Department of Neurosurgery and Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Virginia Newcombe
- Division of Anaesthesia, University of Cambridge, Addenbrooke’s Hospital, Cambridge, UK
| | - David Okonkwo
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA
| | - Matej Orešič
- School of Medical Sciences, Örebro University, Örebro, Sweden
| | - Wilco Peul
- Department of Neurosurgery, Leiden University Medical Center, Leiden, Netherlands
| | - Dana Pisică
- Department of Public Health, Erasmus MC University Medical Center Rotterdam, Rotterdam, Netherlands
- Department of Neurosurgery, Erasmus MC University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Suzanne Polinder
- Department of Public Health, Erasmus MC University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Jennie Ponsford
- Monash-Epworth Rehabilitation Research Centre, Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, VIC, Australia
| | - Louis Puybasset
- Department of Anesthesiology and Intensive Care, APHP, Sorbonne Université, Hôpital Pitié-Salpêtrière, Paris, France
| | - Rahul Raj
- Department of Neurosurgery, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Chiara Robba
- Department of Anaesthesia and Intensive Care, Policlinico San Martino IRCCS for Oncology and Neuroscience, Genova, Italy, and Dipartimento di Scienze Chirurgiche e Diagnostiche, University of Genoa, Italy
| | - Cecilie Røe
- Division of Clinical Neuroscience, Department of Physical Medicine and Rehabilitation, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - Jonathan Rosand
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | | | - David J Sharp
- Department of Brain Sciences, Imperial College London, London, UK
| | - Peter Smielewski
- Brain Physics Lab, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Addenbrooke’s Hospital, Cambridge, UK
| | - Murray B Stein
- Department of Psychiatry and Department of Family Medicine and Public Health, UCSD School of Medicine, La Jolla, CA, USA
| | - Nicole von Steinbüchel
- Institute of Medical Psychology and Medical Sociology, University Medical Center Goettingen, Goettingen, Germany
| | - William Stewart
- Department of Neuropathology, Queen Elizabeth University Hospital and University of Glasgow, Glasgow, UK
| | - Ewout W Steyerberg
- Department of Biomedical Data Sciences Leiden University Medical Center, Leiden, Netherlands
| | - Nino Stocchetti
- Department of Pathophysiology and Transplantation, Milan University, and Neuroscience ICU, Fondazione IRCCS Ca Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Nancy Temkin
- Departments of Neurological Surgery, and Biostatistics, University of Washington, Seattle, WA, USA
| | - Olli Tenovuo
- Department of Rehabilitation and Brain Trauma, Turku University Hospital, and Department of Neurology, University of Turku, Turku, Finland
| | - Alice Theadom
- National Institute for Stroke and Applied Neurosciences, Faculty of Health and Environmental Studies, Auckland University of Technology, Auckland, New Zealand
| | - Ilias Thomas
- School of Medical Sciences, Örebro University, Örebro, Sweden
| | - Abel Torres Espin
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
| | - Alexis F Turgeon
- Department of Anesthesiology and Critical Care Medicine, Division of Critical Care Medicine, Université Laval, CHU de Québec-Université Laval Research Center, Québec City, QC, Canada
| | - Andreas Unterberg
- Department of Neurosurgery, Heidelberg University Hospital, Heidelberg, Germany
| | - Dominique Van Praag
- Departments of Clinical Psychology and Neurosurgery, Antwerp University Hospital, and University of Antwerp, Edegem, Belgium
| | - Ernest van Veen
- Department of Public Health, Erasmus MC University Medical Center Rotterdam, Rotterdam, Netherlands
| | | | - Thijs Vande Vyvere
- Department of Radiology, Faculty of Medicine and Health Sciences, Department of Rehabilitation Sciences (MOVANT), Antwerp University Hospital, and University of Antwerp, Edegem, Belgium
| | - Kevin K W Wang
- Department of Psychiatry, University of Florida, Gainesville, FL, USA
| | - Eveline J A Wiegers
- Department of Public Health, Erasmus MC University Medical Center Rotterdam, Rotterdam, Netherlands
| | - W Huw Williams
- Centre for Clinical Neuropsychology Research, Department of Psychology, University of Exeter, Exeter, UK
| | - Lindsay Wilson
- Division of Psychology, University of Stirling, Stirling, UK
| | - Stephen R Wisniewski
- University of Pittsburgh Graduate School of Public Health, Pittsburgh, Pennsylvania, USA
| | - Alexander Younsi
- Department of Neurosurgery, Heidelberg University Hospital, Heidelberg, Germany
| | - John K Yue
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
| | - Esther L Yuh
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Frederick A Zeiler
- Departments of Surgery, Human Anatomy and Cell Science, and Biomedical Engineering, Rady Faculty of Health Sciences and Price Faculty of Engineering, University of Manitoba, Winnipeg, MB, Canada
| | - Marina Zeldovich
- Institute of Medical Psychology and Medical Sociology, University Medical Center Goettingen, Goettingen, Germany
| | - Roger Zemek
- Departments of Pediatrics and Emergency Medicine, University of Ottawa, Children’s Hospital of Eastern Ontario, ON, Canada
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17
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Management of moderate to severe traumatic brain injury: an update for the intensivist. Intensive Care Med 2022; 48:649-666. [PMID: 35595999 DOI: 10.1007/s00134-022-06702-4] [Citation(s) in RCA: 54] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 04/09/2022] [Indexed: 01/04/2023]
Abstract
Traumatic brain injury (TBI) remains one of the most fatal and debilitating conditions in the world. Current clinical management in severe TBI patients is mainly concerned with reducing secondary insults and optimizing the balance between substrate delivery and consumption. Over the past decades, multimodality monitoring has become more widely available, and clinical management protocols have been published that recommend potential interventions to correct pathophysiological derangements. Even while evidence from randomized clinical trials is still lacking for many of the recommended interventions, these protocols and algorithms can be useful to define a clear standard of therapy where novel interventions can be added or be compared to. Over the past decade, more attention has been paid to holistic management, in which hemodynamic, respiratory, inflammatory or coagulation disturbances are detected and treated accordingly. Considerable variability with regards to the trajectories of recovery exists. Even while most of the recovery occurs in the first months after TBI, substantial changes may still occur in a later phase. Neuroprognostication is challenging in these patients, where a risk of self-fulfilling prophecies is a matter of concern. The present article provides a comprehensive and practical review of the current best practice in clinical management and long-term outcomes of moderate to severe TBI in adult patients admitted to the intensive care unit.
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Puybasset L, Perlbarg V, Pelegrini-Issac M. Prognostication model for traumatic brain injury based on age and white matter diffusion metrics in MRI brain. Author's reply. Intensive Care Med 2022; 48:500-501. [PMID: 35146533 DOI: 10.1007/s00134-022-06641-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/03/2022] [Indexed: 11/05/2022]
Affiliation(s)
- Louis Puybasset
- Neurosurgical Intensive Care Unit, Hôpital Pitié-Salpêtrière, APHP, Sorbonne Université, Paris, France. .,Laboratoire d'Imagerie Biomédicale (LIB), CNRS, INSERM, Sorbonne Université, Paris, France. .,Clinical Research Group 29, Sorbonne Université, Paris, France. .,Department of Anesthesiology and Intensive Care, Groupe Hospitalier Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, 47-83 Boulevard de l'Hôpital, 75013, Paris, France.
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19
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Prognostication model for traumatic brain injury based on age and white matter diffusion metrics in brain MRI. Intensive Care Med 2022; 48:498-499. [DOI: 10.1007/s00134-022-06628-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/10/2022] [Indexed: 12/23/2022]
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