Multimodal Resonance Imaging for Outcome Prediction on Coma Patients
NCT ID: NCT00577954
Last Updated: 2017-09-21
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
417 participants
OBSERVATIONAL
2006-10-31
2014-06-30
Brief Summary
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Detailed Description
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Objective: The goal of this study is to develop a composite score able to predict the awakening of coma patients following events such as a severe cranial trauma, ischemic or hemorrhagic cerebrovascular accident and cerebral anoxia. This composite score will be built from the results of the multimodal MRI (quantified indicator) in combination with clinical covariables (e.g., age of the patient, the mechanism of the accident (high versus low speed), etc.). The final score will aim to predict the outcome of patients at 1 year, evaluated by one of the following categories: favourable (Glasgow Outcome Scale (GOS 3+, 4, and 5) or unfavourable outcome (GOS 1, 2, and 3). GOS 3- score has been defined as minimally conscious state and GOS 3+ score as severe disability excluding cognitive sequelae.
MRI Analysis: The lesions present on the MRI will be quantified by a neuroradiologist and a dedicated clinical engineer from the coordination centre (Pitié-Salpêtrière Hospital) in a blinded way regarding patients' clinical data. Lesion load-indicators will be calculated on the sequences of FLAIR/T2, T2\*, MRS and diffusion tensor imaging from a predefined analysis grid allowing the regional study of the lesions as well as the appreciation of their nature, their uni- or bilateral character and if bilateral, their symmetry.
Hypothesis and applicability: The multivariate analysis of morphological MRI, MRS and diffusion tensor imaging data, combined with the clinical covariables, will aim to develop a statistical algorithm, able to predict the clinical outcome of the patients. In the long term, it will be integrated into an expert system which will be the subject of a patent submission. The final objective is to provide the clinicians a diagnostic tool able to determine outcome of patients with severe cranial trauma and other neurological conditions such as stroke, subarachnoid hemorrhage and cerebral anoxia.
Conditions
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Study Design
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COHORT
PROSPECTIVE
Study Groups
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1
Patients in a coma condition after a traumatic brain injury (250), stroke, cerebral anoxia or subarachnoid hemorrhage (150), for at least 7 days.
Multimodal MRI
Multimodal MRI
Interventions
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Multimodal MRI
Multimodal MRI
Eligibility Criteria
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Inclusion Criteria
* Hospitalized in neuroICU and requiring artificial ventilation following a severe cranial trauma, an ischemic and/or hemorrhagic cerebrovascular accident and/or a cerebral anoxia
* In coma defined as not answering simple orders at least 7 days after the event
* Receiving an amount of sedatives and not being able to explain the coma
* Having a standardized intracranial pressure (≤ 15 mm Hg) and in absence of severe hemodynamic or respiratory failure so that the MRI does not represent any additional danger
Exclusion Criteria
* Coma explained by sedation
* MRI contraindication (pace maker, medical material not MRI compatible)
* Persistent intracranial hypertension contraindicating the setting in prolonged dorsal decubitus (ICP \> 20 mm Hg during more than 10 minutes after positioning on decubitus)
* Severe hemodynamic failure
* Severe respiratory failure
* Life threatening extra-cerebral lesions
* Cranial trauma of ballistic origin
* Patient presenting a severe compromise of a major function bringing into play the vital outcome, with invalidity before the accident/event
* Former neurological pathology altering the clinical outcome of the cranial trauma
* Refusal of the family
* Patient protected by the law (under supervision or trusteeship)
18 Years
ALL
Yes
Sponsors
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Assistance Publique - Hôpitaux de Paris
OTHER
Responsible Party
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Principal Investigators
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Pr Louis Puybasset,, MD, PhD
Role: PRINCIPAL_INVESTIGATOR
Assistance Publique Hopitaux de Paris Pitié Salpetriere
Locations
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Assistance Publique Hopitaux de Paris Pitie Salpetriere
Paris, , France
Countries
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References
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Puybasset L, Perlbarg V, Unrug J, Cassereau D, Galanaud D, Torkomian G, Battisti V, Lefort M, Velly L, Degos V, Citerio G, Bayen E, Pelegrini-Issac M; MRI-COMA Investigators CENTER-TBI MRI Participants and MRI Only Investigators. Prognostic value of global deep white matter DTI metrics for 1-year outcome prediction in ICU traumatic brain injury patients: an MRI-COMA and CENTER-TBI combined study. Intensive Care Med. 2022 Feb;48(2):201-212. doi: 10.1007/s00134-021-06583-z. Epub 2022 Dec 14.
Simeone P, Auzias G, Lefevre J, Takerkart S, Coulon O, Lesimple B, Torkomian G, Battisti V, Jacquens A, Couret D, Naccache L, Bayen E, Bruder N, Perlbarg V, Puybasset L, Velly L. Long-term follow-up of neurodegenerative phenomenon in severe traumatic brain injury using MRI. Ann Phys Rehabil Med. 2022 Nov;65(6):101599. doi: 10.1016/j.rehab.2021.101599. Epub 2022 Feb 15.
Velly L, Perlbarg V, Boulier T, Adam N, Delphine S, Luyt CE, Battisti V, Torkomian G, Arbelot C, Chabanne R, Jean B, Di Perri C, Laureys S, Citerio G, Vargiolu A, Rohaut B, Bruder N, Girard N, Silva S, Cottenceau V, Tourdias T, Coulon O, Riou B, Naccache L, Gupta R, Benali H, Galanaud D, Puybasset L; MRI-COMA Investigators. Use of brain diffusion tensor imaging for the prediction of long-term neurological outcomes in patients after cardiac arrest: a multicentre, international, prospective, observational, cohort study. Lancet Neurol. 2018 Apr;17(4):317-326. doi: 10.1016/S1474-4422(18)30027-9. Epub 2018 Feb 27.
Luyt CE, Galanaud D, Perlbarg V, Vanhaudenhuyse A, Stevens RD, Gupta R, Besancenot H, Krainik A, Audibert G, Combes A, Chastre J, Benali H, Laureys S, Puybasset L; Neuro Imaging for Coma Emergence and Recovery Consortium. Diffusion tensor imaging to predict long-term outcome after cardiac arrest: a bicentric pilot study. Anesthesiology. 2012 Dec;117(6):1311-21. doi: 10.1097/ALN.0b013e318275148c.
Other Identifiers
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P051061
Identifier Type: -
Identifier Source: org_study_id