[en] Accurately predicting functional outcomes for unresponsive patients with acute brain injury is a medical, scientific and ethical challenge. This prospective study assesses how a multimodal approach combining various numbers of behavioral, neuroimaging and electrophysiological markers affects the performance of outcome predictions. We analyzed data from 349 patients admitted to a tertiary neurointensive care unit between 2009 and 2021, categorizing prognoses as good, uncertain or poor, and compared these predictions with observed outcomes using the Glasgow Outcome Scale-Extended (GOS-E, levels ranging from 1 to 8, with higher levels indicating better outcomes). After excluding cases with life-sustaining therapy withdrawal to mitigate the self-fulfilling prophecy bias, our findings reveal that a good prognosis, compared with a poor or uncertain one, is associated with better one-year functional outcomes (common odds ratio (95% CI) for higher GOS-E: OR = 14.57 (5.70-40.32), P < 0.001; and 2.9 (1.56-5.45), P < 0.001, respectively). Moreover, increasing the number of assessment modalities decreased uncertainty (OR = 0.35 (0.21-0.59), P < 0.001) and improved prognostic accuracy (OR = 2.72 (1.18-6.47), P = 0.011). Our results underscore the value of multimodal assessment in refining neuroprognostic precision, thereby offering a robust foundation for clinical decision-making processes for acutely brain-injured patients. ClinicalTrials.gov registration: NCT04534777 .
Disciplines :
Neurosciences & behavior
Author, co-author :
Rohaut, B ; Sorbonne Université, Paris, France. benjamin.rohaut@sorbonne-universite.fr ; Paris Brain Institute - ICM, Inserm, CNRS, PICNIC-Lab, Paris, France. benjamin.rohaut@sorbonne-universite.fr ; APHP, Hôpital de la Pitié Salpêtrière, DMU Neurosciences - Neuro ICU, Paris, France. benjamin.rohaut@sorbonne-universite.fr
Calligaris, C; APHP, Hôpital de la Pitié Salpêtrière, DMU Neurosciences - Neuro ICU, Paris, France ; GHU Paris Psychiatrie et Neurosciences, Pole Neuro, Sainte‑Anne Hospital, Anesthesia and Intensive Care Department, Paris, France
Hermann, B ; Paris Brain Institute - ICM, Inserm, CNRS, PICNIC-Lab, Paris, France ; APHP, Hôpital de la Pitié Salpêtrière, DMU Neurosciences - Neuro ICU, Paris, France ; GHU Paris Psychiatrie et Neurosciences, Pole Neuro, Sainte‑Anne Hospital, Anesthesia and Intensive Care Department, Paris, France
Perez, P ; Paris Brain Institute - ICM, Inserm, CNRS, PICNIC-Lab, Paris, France ; APHP, Hôpital de la Pitié Salpêtrière, DMU Neurosciences - Neuro ICU, Paris, France
Faugeras, F; Paris Brain Institute - ICM, Inserm, CNRS, PICNIC-Lab, Paris, France
Raimondo, Federico ; Université de Liège - ULiège > GIGA > GIGA Neurosciences - Physiology of Cognition ; Paris Brain Institute - ICM, Inserm, CNRS, PICNIC-Lab, Paris, France
King, J-R ; Paris Brain Institute - ICM, Inserm, CNRS, PICNIC-Lab, Paris, France ; Laboratoire des systèmes perceptifs, Département d'études cognitives, École normale supérieure, PSL University, CNRS, Paris, France
Engemann, D ; Paris Brain Institute - ICM, Inserm, CNRS, PICNIC-Lab, Paris, France
Marois, C; Paris Brain Institute - ICM, Inserm, CNRS, PICNIC-Lab, Paris, France ; APHP, Hôpital de la Pitié Salpêtrière, DMU Neurosciences - Neuro ICU, Paris, France
Le Guennec, L ; Sorbonne Université, Paris, France ; APHP, Hôpital de la Pitié Salpêtrière, DMU Neurosciences - Neuro ICU, Paris, France
Di Meglio, L ; Sorbonne Université, Paris, France ; APHP, Hôpital de la Pitié Salpêtrière, DMU Neurosciences - Neuro ICU, Paris, France ; GHU Paris Psychiatrie et Neurosciences, Pole Neuro, Sainte‑Anne Hospital, Anesthesia and Intensive Care Department, Paris, France
Sangaré, A; Sorbonne Université, Paris, France ; Paris Brain Institute - ICM, Inserm, CNRS, PICNIC-Lab, Paris, France ; APHP, Hôpital de la Pitié Salpêtrière, DMU Neurosciences - Neurophysiology, Paris, France
Munoz Musat, E ; Paris Brain Institute - ICM, Inserm, CNRS, PICNIC-Lab, Paris, France ; APHP, Hôpital de la Pitié Salpêtrière, DMU Neurosciences - Neurophysiology, Paris, France
Valente, M; Paris Brain Institute - ICM, Inserm, CNRS, PICNIC-Lab, Paris, France
Ben Salah, A; Sorbonne Université, Paris, France ; Paris Brain Institute - ICM, Inserm, CNRS, PICNIC-Lab, Paris, France
Demertzi, Athina ; Université de Liège - ULiège > GIGA > GIGA Neurosciences - Physiology of Cognition ; Paris Brain Institute - ICM, Inserm, CNRS, PICNIC-Lab, Paris, France
Belloli, L ; Paris Brain Institute - ICM, Inserm, CNRS, PICNIC-Lab, Paris, France
Manasova, D ; Paris Brain Institute - ICM, Inserm, CNRS, PICNIC-Lab, Paris, France
Jodaitis, L; Paris Brain Institute - ICM, Inserm, CNRS, PICNIC-Lab, Paris, France ; APHP, Hôpital de la Pitié Salpêtrière, DMU Neurosciences - Neuro ICU, Paris, France
Habert, M O ; Sorbonne Université, Paris, France ; APHP, Hôpital de la Pitié Salpêtrière, Departement of Nuclear Medicine, Laboratoire d'Imagerie Biomédicale, Inserm, CNRS, Paris, France
Lambrecq, V ; Sorbonne Université, Paris, France ; APHP, Hôpital de la Pitié Salpêtrière, DMU Neurosciences - Neurophysiology, Paris, France
Pyatigorskaya, N; Sorbonne Université, Paris, France ; APHP, Hôpital de la Pitié Salpêtrière, Departement of Neuro-radiology, Paris, France
Galanaud, D; Sorbonne Université, Paris, France ; APHP, Hôpital de la Pitié Salpêtrière, Departement of Neuro-radiology, Paris, France
Puybasset, L; Sorbonne Université, Paris, France ; APHP, Hôpital de la Pitié Salpêtrière, Departement of Neuro-anaesthesiology and Neurocritical care, Paris, France
Weiss, N ; Sorbonne Université, Paris, France ; APHP, Hôpital de la Pitié Salpêtrière, DMU Neurosciences - Neuro ICU, Paris, France
Demeret, S; APHP, Hôpital de la Pitié Salpêtrière, DMU Neurosciences - Neuro ICU, Paris, France
Lejeune, F X ; Paris Brain Institute - ICM, Inserm, CNRS, Data Analysis Core, Paris, France
Sitt, J D ; Paris Brain Institute - ICM, Inserm, CNRS, PICNIC-Lab, Paris, France
Naccache, L; Sorbonne Université, Paris, France ; Paris Brain Institute - ICM, Inserm, CNRS, PICNIC-Lab, Paris, France ; APHP, Hôpital de la Pitié Salpêtrière, DMU Neurosciences - Neurophysiology, Paris, France
This work was supported by Sorbonne Universit\u00E9, the James S. McDonnell Foundation, FRM 2015, UNIM, the Acad\u00E9mie des Sciences-Lamonica Prize 2016 (L.N.) and a PerMed grant (J.D.S.). The research leading to these results received funding from the program Investissements d\u2019avenir ANR-10-IAIHU-06. There was no industry involvement in or support for the study. The authors vouch for the accuracy and completeness of the data and for the fidelity of the trial to the protocol.
J.T. Giacino B.L. Edlow Covert consciousness in the intensive care unit Trends Neurosci. 2019 42 844 847 1:CAS:528:DC%2BC1MXhslWgtLrN 31514975
B. Rohaut A. Eliseyev J. Claassen Uncovering consciousness in unresponsive ICU patients: technical, medical and ethical considerations Crit. Care 2019 23 30850022 6408788
Lissak, I. A. & Young, M. J. Limitation of life sustaining therapy in disorders of consciousness: ethics and practice. Brain awae060 (2024).
B.L. Edlow J. Claassen N.D. Schiff D.M. Greer Recovery from disorders of consciousness: mechanisms, prognosis and emerging therapies Nat. Rev. Neurol. 2021 17 135 156 33318675
C. Sandroni et al. Prediction of poor neurological outcome in comatose survivors of cardiac arrest: a systematic review Intensive Care Med 2020 46 1803 1851 32915254 7527362
C. Sandroni et al. Prediction of good neurological outcome in comatose survivors of cardiac arrest: a systematic review Intensive Care Med 2022 48 389 413 1:CAS:528:DC%2BB38XhtVWrsr7I 35244745 8940794
MRC CRASH Trial Collaborators et al. Predicting outcome after traumatic brain injury: practical prognostic models based on large cohort of international patients Brit. Med. J. 2008 336 425 429 2249681
D. Kondziella et al. European Academy of Neurology guideline on the diagnosis of coma and other disorders of consciousness Eur. J. Neurol. 2020 27 741 756 1:STN:280:DC%2BB387ltleqsQ%3D%3D 32090418
J.T. Giacino et al. Practice guideline update recommendations summary: disorders of consciousness Neurology 2018 91 450 30089618 6139814
A. Comanducci et al. Clinical and advanced neurophysiology in the prognostic and diagnostic evaluation of disorders of consciousness: review of an IFCN-endorsed expert group Clin. Neurophysiol. 2020 131 2736 2765 1:STN:280:DC%2BB38bns1egug%3D%3D 32917521
Fischer, D. & Edlow, B. L. Coma prognostication after acute brain injury: a review. JAMA Neurol.81, 405–415 (2024).
B. Rohaut J. Claassen Decision making in perceived devastating brain injury: a call to explore the impact of cognitive biases Br. J. Anaesth. 2018 120 5 9 1:STN:280:DC%2BC1MvntVWisg%3D%3D 29397137
C. Lakhlifi B. Rohaut Heuristics and biases in medical decision-making under uncertainty: the case of neuropronostication for consciousness disorders Presse Med. 2023 52 104181 37821058
L. Naccache Minimally conscious state or cortically mediated state? Brain 2018 141 949 960 29206895
F. Faugeras et al. Survival and consciousness recovery are better in the minimally conscious state than in the vegetative state Brain Inj. 2018 32 72 77 29156989
J. Luauté et al. Long-term outcomes of chronic minimally conscious and vegetative states Neurology 2010 75 246 252 20554940
L. Naccache J. Luauté S. Silva J.D. Sitt B. Rohaut Toward a coherent structuration of disorders of consciousness expertise at a country scale: a proposal for France Rev. Neurol. 2022 178 9 20 1:STN:280:DC%2BB2M%2Fjt12gsw%3D%3D 34980510
J. Claassen et al. Detection of brain activation in unresponsive patients with acute brain injury N. Engl. J. Med. 2019 380 2497 2505 31242361
B. Hermann et al. Habituation of auditory startle reflex is a new sign of minimally conscious state Brain 2020 143 2154 2172 32582938 7364741
L. Velly et al. 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 17 317 326 29500154
F. Raimondo et al. Brain–heart interactions reveal consciousness in noncommunicating patients Ann. Neurol. 2017 82 578 591 28892566
Hermann, B. et al. Aberrant brain–heart coupling is associated with the severity of post cardiac arrest brain injury. Ann. Clin. Transl. Neurol.11, 866–882 (2024).
A. Arzi et al. Olfactory sniffing signals consciousness in unresponsive patients with brain injuries Nature 2020 581 428 433 1:CAS:528:DC%2BB3cXot1aksbk%3D 32461641
B.L. Edlow et al. Measuring consciousness in the intensive care unit Neurocrit. Care 2023 38 584 590 37029315
J. Elmer et al. Association of early withdrawal of life-sustaining therapy for perceived neurological prognosis with mortality after cardiac arrest Resuscitation 2016 102 127 135 26836944 4834233
J.M. Weimer A.S. Nowacki J.A. Frontera Withdrawal of life-sustaining therapy in patients with intracranial hemorrhage: self-fulfilling prophecy or accurate prediction of outcome? Crit. Care Med 2016 44 1161 1172 26807687
T.L. May et al. Early withdrawal of life support after resuscitation from cardiac arrest is common and may result in additional deaths Resuscitation 2019 139 308 313 30836171 6555675
E. von Elm et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies PLoS Med. 2007 4 e296
R.H.J.M. Kurvers et al. Boosting medical diagnostics by pooling independent judgments Proc. Natl Acad. Sci. USA 2016 113 8777 8782 1:CAS:528:DC%2BC28XhtFOrsr%2FF 27432950 4978286
M.L. Barnett D. Boddupalli S. Nundy D.W. Bates Comparative accuracy of diagnosis by collective intelligence of multiple physicians vs individual physicians JAMA Netw. Open 2019 2 e190096 30821822 6484633
K. Kalmar J.T. Giacino The JFK Coma Recovery Scale–Revised Neuropsychol. Rehabil. 2005 15 454 460 16350986
N. Weiss et al. The French version of the FOUR score: a new coma score Rev. Neurol. 2009 165 796 802 1:STN:280:DC%2BD1Mnkslygug%3D%3D 19296997
B. Hermann et al. Wisdom of the caregivers: pooling individual subjective reports to diagnose states of consciousness in brain-injured patients, a monocentric prospective study BMJ Open 2019 9 e026211 30792234 6410088
T.A. Bekinschtein et al. Neural signature of the conscious processing of auditory regularities Proc. Natl Acad. Sci. USA 2009 106 1672 1677 1:CAS:528:DC%2BD1MXhvVyju78%3D 19164526 2635770
F. Faugeras et al. Probing consciousness with event-related potentials in the vegetative state Neurology 2011 77 264 268 1:STN:280:DC%2BC3MnovFOjtw%3D%3D 21593438 3136052
F. Faugeras et al. Event related potentials elicited by violations of auditory regularities in patients with impaired consciousness Neuropsychologia 2012 50 403 418 22230230
J.D. Sitt et al. Large scale screening of neural signatures of consciousness in patients in a vegetative or minimally conscious state Brain 2014 137 2258 2270 24919971 4610185
D. Galanaud et al. Assessment of white matter injury and outcome in severe brain trauma: a prospective multicenter cohort Anesthesiology 2012 117 1300 1310 23135261
C.-E. Luyt et al. Diffusion tensor imaging to predict long-term outcome after cardiac arrest: a bicentric pilot study Anesthesiology 2012 117 1311 1321 23135257
A. Demertzi et al. Intrinsic functional connectivity differentiates minimally conscious from unresponsive patients Brain 2015 138 2619 2631 26117367
J. Stender et al. Diagnostic precision of PET imaging and functional MRI in disorders of consciousness: a clinical validation study Lancet 2014 384 514 522 24746174
J. Stender et al. The minimal energetic requirement of sustained awareness after brain injury Curr. Biol. 2016 26 1494 1499 1:CAS:528:DC%2BC28XptVSls7w%3D 27238279
B. Jennett J. Snoek M.R. Bond N. Brooks Disability after severe head injury: observations on the use of the Glasgow Outcome Scale J. Neurol. Neurosurg. Psychiatry 1981 44 285 293 1:STN:280:DyaL3M3hslCqtA%3D%3D 6453957 490949
J. Lu et al. A method for reducing misclassification in the extended Glasgow Outcome Score J. Neurotrauma 2010 27 843 852 20334503 2943940
B. Roozenbeek et al. The added value of ordinal analysis in clinical trials: an example in traumatic brain injury Crit. Care 2011 15 21586148 3218993
W.G. Cochran Some methods for strengthening the common χ2 tests Biometrics 1954 10 417 451
P. Armitage Tests for linear trends in proportions and frequencies Biometrics 1955 11 375
K.-A. Lê Cao S. Boitard P. Besse Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems BMC Bioinf. 2011 12 253
F. Rohart B. Gautier A. Singh K.-A. Lê Cao mixOmics: an R package for’omics feature selection and multiple data integration PLoS Comput. Biol. 2017 13 e1005752 29099853 5687754
Wold, H. Path models with latent variables: the NIPALS approach. in Quantitative Sociology 307–357 (Academic Press, 1975).