Combining Transcranial Doppler and EEG Data to Predict Delayed Cerebral Ischemia After Subarachnoid Hemorrhage.

Brain Ischemia / diagnostic imaging Electroencephalography / methods Humans Prospective Studies Retrospective Studies Subarachnoid Hemorrhage / complications Ultrasonography, Doppler, Transcranial Vasospasm, Intracranial / complications

Journal

Neurology
ISSN: 1526-632X
Titre abrégé: Neurology
Pays: United States
ID NLM: 0401060

Informations de publication

Date de publication:
01 02 2022
Historique:
received: 07 05 2021
accepted: 08 11 2021
pubmed: 1 12 2021
medline: 6 4 2022
entrez: 30 11 2021
Statut: ppublish

Résumé

Delayed cerebral ischemia (DCI) is the leading complication of subarachnoid hemorrhage (SAH). Because DCI was traditionally thought to be caused by large vessel vasospasm, transcranial Doppler ultrasounds (TCDs) have been the standard of care. Continuous EEG has emerged as a promising complementary monitoring modality and predicts increased DCI risk. Our objective was to determine whether combining EEG and TCD data improves prediction of DCI after SAH. We hypothesize that integrating these diagnostic modalities improves DCI prediction. We retrospectively assessed patients with moderate to severe SAH (2011-2015; Fisher 3-4 or Hunt-Hess 4-5) who had both prospective TCD and EEG acquisition during hospitalization. Middle cerebral artery (MCA) peak systolic velocities (PSVs) and the presence or absence of epileptiform abnormalities (EAs), defined as seizures, epileptiform discharges, and rhythmic/periodic activity, were recorded daily. Logistic regressions were used to identify significant covariates of EAs and TCD to predict DCI. Group-based trajectory modeling (GBTM) was used to account for changes over time by identifying distinct group trajectories of MCA PSV and EAs associated with DCI risk. We assessed 107 patients; DCI developed in 56 (51.9%). Univariate predictors of DCI are presence of high-MCA velocity (PSV ≥200 cm/s, sensitivity 27%, specificity 89%) and EAs (sensitivity 66%, specificity 62%) on or before day 3. Two univariate GBTM trajectories of EAs predicted DCI (sensitivity 64%, specificity 62.75%). Logistic regression and GBTM models using both TCD and EEG monitoring performed better. The best logistic regression and GBTM models used both TCD and EEG data, Hunt-Hess score at admission, and aneurysm treatment as predictors of DCI (logistic regression: sensitivity 90%, specificity 70%; GBTM: sensitivity 89%, specificity 67%). EEG and TCD biomarkers combined provide the best prediction of DCI. The conjunction of clinical variables with the timing of EAs and high MCA velocities improved model performance. These results suggest that TCD and cEEG are promising complementary monitoring modalities for DCI prediction. Our model has potential to serve as a decision support tool in SAH management. This study provides Class II evidence that combined TCD and EEG monitoring can identify delayed cerebral ischemia after SAH.

Sections du résumé

BACKGROUND AND OBJECTIVES
Delayed cerebral ischemia (DCI) is the leading complication of subarachnoid hemorrhage (SAH). Because DCI was traditionally thought to be caused by large vessel vasospasm, transcranial Doppler ultrasounds (TCDs) have been the standard of care. Continuous EEG has emerged as a promising complementary monitoring modality and predicts increased DCI risk. Our objective was to determine whether combining EEG and TCD data improves prediction of DCI after SAH. We hypothesize that integrating these diagnostic modalities improves DCI prediction.
METHODS
We retrospectively assessed patients with moderate to severe SAH (2011-2015; Fisher 3-4 or Hunt-Hess 4-5) who had both prospective TCD and EEG acquisition during hospitalization. Middle cerebral artery (MCA) peak systolic velocities (PSVs) and the presence or absence of epileptiform abnormalities (EAs), defined as seizures, epileptiform discharges, and rhythmic/periodic activity, were recorded daily. Logistic regressions were used to identify significant covariates of EAs and TCD to predict DCI. Group-based trajectory modeling (GBTM) was used to account for changes over time by identifying distinct group trajectories of MCA PSV and EAs associated with DCI risk.
RESULTS
We assessed 107 patients; DCI developed in 56 (51.9%). Univariate predictors of DCI are presence of high-MCA velocity (PSV ≥200 cm/s, sensitivity 27%, specificity 89%) and EAs (sensitivity 66%, specificity 62%) on or before day 3. Two univariate GBTM trajectories of EAs predicted DCI (sensitivity 64%, specificity 62.75%). Logistic regression and GBTM models using both TCD and EEG monitoring performed better. The best logistic regression and GBTM models used both TCD and EEG data, Hunt-Hess score at admission, and aneurysm treatment as predictors of DCI (logistic regression: sensitivity 90%, specificity 70%; GBTM: sensitivity 89%, specificity 67%).
DISCUSSION
EEG and TCD biomarkers combined provide the best prediction of DCI. The conjunction of clinical variables with the timing of EAs and high MCA velocities improved model performance. These results suggest that TCD and cEEG are promising complementary monitoring modalities for DCI prediction. Our model has potential to serve as a decision support tool in SAH management.
CLASSIFICATION OF EVIDENCE
This study provides Class II evidence that combined TCD and EEG monitoring can identify delayed cerebral ischemia after SAH.

Identifiants

DOI: 10.1212/WNL.0000000000013126 PMID: 34845057 PMC: PMC8826465
pubmed: 34845057
pii: WNL.0000000000013126
doi: 10.1212/WNL.0000000000013126
pmc: PMC8826465
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e459-e469

Subventions

Organisme : NINDS NIH HHS
ID : K23 NS110980
Pays : United States
Organisme : NINR NIH HHS
ID : R01 NR018335
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS102190
Pays : United States
Organisme : NINDS NIH HHS
ID : U24 NS107136
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR001863
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS119825
Pays : United States
Organisme : NINDS NIH HHS
ID : K23 NS112596
Pays : United States
Organisme : NIA NIH HHS
ID : RF1 AG064312
Pays : United States
Organisme : NINDS NIH HHS
ID : R03 NS112859
Pays : United States
Organisme : NINDS NIH HHS
ID : U24 NS107215
Pays : United States
Organisme : NINDS NIH HHS
ID : U01 NS106513
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS102574
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS109062
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS107291
Pays : United States
Organisme : NINDS NIH HHS
ID : K23 NS105950
Pays : United States
Organisme : NINDS NIH HHS
ID : K23 NS097629
Pays : United States
Organisme : NINDS NIH HHS
ID : K23 NS114201
Pays : United States

Informations de copyright

© 2021 American Academy of Neurology.

Références

Neurocrit Care. 2016 Apr;24(2):207-16
pubmed: 26432793
Stroke. 2001 Sep;32(9):2012-20
pubmed: 11546890
J Clin Neurophysiol. 2016 Jun;33(3):174-82
pubmed: 27258440
J Neurol Neurosurg Psychiatry. 2000 Mar;68(3):337-41
pubmed: 10675216
Stroke. 2005 Sep;36(9):2024-6
pubmed: 16051891
Stroke. 2010 Oct;41(10):2391-5
pubmed: 20798370
Stroke. 2015 Jul;46(7):1826-31
pubmed: 25977276
Stroke. 2013 May;44(5):1288-94
pubmed: 23512975
Neurosurgery. 1999 Jun;44(6):1237-47; discussion 1247-8
pubmed: 10371622
Clin Neurophysiol. 2015 Aug;126(8):1514-23
pubmed: 25500193
Neurosurg Clin N Am. 2010 Apr;21(2):291-303
pubmed: 20380971
Neurocrit Care. 2011 Apr;14(2):152-61
pubmed: 21207187
Neurosurgery. 2006 Jul;59(1):21-7; discussion 21-7
pubmed: 16823296
Resuscitation. 2019 Apr;137:197-204
pubmed: 30825550
Electroencephalogr Clin Neurophysiol. 1997 Dec;103(6):607-15
pubmed: 9546487
Stroke. 2009 Nov;40(11):3493-8
pubmed: 19762703
Neuroradiology. 2019 Mar;61(3):247-256
pubmed: 30693409
Annu Rev Clin Psychol. 2010;6:109-38
pubmed: 20192788
Radiographics. 2013 Jan-Feb;33(1):E1-E14
pubmed: 23322845
Stroke. 2012 Mar;43(3):697-701
pubmed: 22198987
Biomed Res Int. 2014;2014:628597
pubmed: 24967389
Clin Neurophysiol. 2017 Jun;128(6):1091-1099
pubmed: 28258936
Stroke. 2013 Jan;44(1):43-54
pubmed: 23250997
CNS Neurosci Ther. 2019 Oct;25(10):1173-1181
pubmed: 31407513
Transl Stroke Res. 2013 Aug;4(4):432-46
pubmed: 23894255
Neurocrit Care. 2011 Sep;15(2):308-11
pubmed: 21748502
J Neurosurg. 1998 Sep;89(3):405-11
pubmed: 9724114
Neuroscientist. 2013 Feb;19(1):25-42
pubmed: 22829393
Ann Neurol. 2018 May;83(5):958-969
pubmed: 29659050
Electroencephalogr Clin Neurophysiol. 1991 May;78(5):325-32
pubmed: 1711451
Clin Neurophysiol. 2004 Dec;115(12):2699-710
pubmed: 15546778
Neurocrit Care. 2011 Sep;15(2):312-7
pubmed: 21748499
J Neurosurg. 1982 Dec;57(6):769-74
pubmed: 7143059
J Neurosurg. 2016 May;124(5):1257-64
pubmed: 26495942
Neuroradiology. 2009 Dec;51(12):813-9
pubmed: 19623472

Auteurs

Hsin Yi Chen (HY)

From the Department of Neurology (H.Y.C., E.J.G., L.J.H., H.P.Z., K.N.S., N.H.P., J.A.K.), Yale University, New Haven, CT; Department of Critical Care Medicine (J.E.), University of Pittsburgh Medical Center, PA; and Department of Neurology (S.F.Z., M.G., V.M.J., E.S.R., M.B.W.), Massachusetts General Hospital, Boston.
ORCID: 0000-0002-7907-2335

Jonathan Elmer (J)

From the Department of Neurology (H.Y.C., E.J.G., L.J.H., H.P.Z., K.N.S., N.H.P., J.A.K.), Yale University, New Haven, CT; Department of Critical Care Medicine (J.E.), University of Pittsburgh Medical Center, PA; and Department of Neurology (S.F.Z., M.G., V.M.J., E.S.R., M.B.W.), Massachusetts General Hospital, Boston.

Sahar F Zafar (SF)

From the Department of Neurology (H.Y.C., E.J.G., L.J.H., H.P.Z., K.N.S., N.H.P., J.A.K.), Yale University, New Haven, CT; Department of Critical Care Medicine (J.E.), University of Pittsburgh Medical Center, PA; and Department of Neurology (S.F.Z., M.G., V.M.J., E.S.R., M.B.W.), Massachusetts General Hospital, Boston.

Manohar Ghanta (M)

From the Department of Neurology (H.Y.C., E.J.G., L.J.H., H.P.Z., K.N.S., N.H.P., J.A.K.), Yale University, New Haven, CT; Department of Critical Care Medicine (J.E.), University of Pittsburgh Medical Center, PA; and Department of Neurology (S.F.Z., M.G., V.M.J., E.S.R., M.B.W.), Massachusetts General Hospital, Boston.

Valdery Moura Junior (V)

From the Department of Neurology (H.Y.C., E.J.G., L.J.H., H.P.Z., K.N.S., N.H.P., J.A.K.), Yale University, New Haven, CT; Department of Critical Care Medicine (J.E.), University of Pittsburgh Medical Center, PA; and Department of Neurology (S.F.Z., M.G., V.M.J., E.S.R., M.B.W.), Massachusetts General Hospital, Boston.
ORCID: 0000-0001-5735-9143

Eric S Rosenthal (ES)

From the Department of Neurology (H.Y.C., E.J.G., L.J.H., H.P.Z., K.N.S., N.H.P., J.A.K.), Yale University, New Haven, CT; Department of Critical Care Medicine (J.E.), University of Pittsburgh Medical Center, PA; and Department of Neurology (S.F.Z., M.G., V.M.J., E.S.R., M.B.W.), Massachusetts General Hospital, Boston.
ORCID: 0000-0003-3900-356X

Emily J Gilmore (EJ)

From the Department of Neurology (H.Y.C., E.J.G., L.J.H., H.P.Z., K.N.S., N.H.P., J.A.K.), Yale University, New Haven, CT; Department of Critical Care Medicine (J.E.), University of Pittsburgh Medical Center, PA; and Department of Neurology (S.F.Z., M.G., V.M.J., E.S.R., M.B.W.), Massachusetts General Hospital, Boston.

Lawrence J Hirsch (LJ)

From the Department of Neurology (H.Y.C., E.J.G., L.J.H., H.P.Z., K.N.S., N.H.P., J.A.K.), Yale University, New Haven, CT; Department of Critical Care Medicine (J.E.), University of Pittsburgh Medical Center, PA; and Department of Neurology (S.F.Z., M.G., V.M.J., E.S.R., M.B.W.), Massachusetts General Hospital, Boston.
ORCID: 0000-0002-6333-832X

Hitten P Zaveri (HP)

From the Department of Neurology (H.Y.C., E.J.G., L.J.H., H.P.Z., K.N.S., N.H.P., J.A.K.), Yale University, New Haven, CT; Department of Critical Care Medicine (J.E.), University of Pittsburgh Medical Center, PA; and Department of Neurology (S.F.Z., M.G., V.M.J., E.S.R., M.B.W.), Massachusetts General Hospital, Boston.

Kevin N Sheth (KN)

From the Department of Neurology (H.Y.C., E.J.G., L.J.H., H.P.Z., K.N.S., N.H.P., J.A.K.), Yale University, New Haven, CT; Department of Critical Care Medicine (J.E.), University of Pittsburgh Medical Center, PA; and Department of Neurology (S.F.Z., M.G., V.M.J., E.S.R., M.B.W.), Massachusetts General Hospital, Boston.

Nils H Petersen (NH)

From the Department of Neurology (H.Y.C., E.J.G., L.J.H., H.P.Z., K.N.S., N.H.P., J.A.K.), Yale University, New Haven, CT; Department of Critical Care Medicine (J.E.), University of Pittsburgh Medical Center, PA; and Department of Neurology (S.F.Z., M.G., V.M.J., E.S.R., M.B.W.), Massachusetts General Hospital, Boston.
ORCID: 0000-0001-9711-3340

M Brandon Westover (MB)

From the Department of Neurology (H.Y.C., E.J.G., L.J.H., H.P.Z., K.N.S., N.H.P., J.A.K.), Yale University, New Haven, CT; Department of Critical Care Medicine (J.E.), University of Pittsburgh Medical Center, PA; and Department of Neurology (S.F.Z., M.G., V.M.J., E.S.R., M.B.W.), Massachusetts General Hospital, Boston.
ORCID: 0000-0003-4803-312X

Jennifer A Kim (JA)

From the Department of Neurology (H.Y.C., E.J.G., L.J.H., H.P.Z., K.N.S., N.H.P., J.A.K.), Yale University, New Haven, CT; Department of Critical Care Medicine (J.E.), University of Pittsburgh Medical Center, PA; and Department of Neurology (S.F.Z., M.G., V.M.J., E.S.R., M.B.W.), Massachusetts General Hospital, Boston. jennifer.a.kim@yale.edu.
ORCID: 0000-0003-3072-6198

Articles similaires

[Redispensing of expensive oral anticancer medicines: a practical application].

Lisanne N van Merendonk, Kübra Akgöl, Bastiaan Nuijen
1.00
Humans Antineoplastic Agents Administration, Oral Drug Costs Counterfeit Drugs

Smoking Cessation and Incident Cardiovascular Disease.

Jun Hwan Cho, Seung Yong Shin, Hoseob Kim et al.
1.00
Humans Male Smoking Cessation Cardiovascular Diseases Female

Evaluation of Low-Value Services Across Major Medicare Advantage Insurers and Traditional Medicare.

Ciara Duggan, Adam L Beckman, Ishani Ganguli et al.
1.00
Humans United States Aged Cross-Sectional Studies Medicare Part C

Effectiveness of Virtual Yoga for Chronic Low Back Pain: A Randomized Clinical Trial.

Hallie Tankha, Devyn Gaskins, Amanda Shallcross et al.
1.00
Humans Yoga Low Back Pain Female Male

Classifications MeSH

questionsmedicales.fr › Maladies cardiovasculaires › Maladies vasculaires › Angiopathies intracrâniennes › Encéphalopathie ischémique › Combining Transcranial Doppler and EEG Data to...
questionsmedicales.fr › Diagnostic › Techniques et procédures diagnostiques › Électrodiagnostic › Électroencéphalographie › Combining Transcranial Doppler and EEG Data to...
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Combining Transcranial Doppler and EEG Data to...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Caractéristiques des études épidémiologiques › Études épidémiologiques › Études de cohortes › Études prospectives › Combining Transcranial Doppler and EEG Data to...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Caractéristiques des études épidémiologiques › Études épidémiologiques › Études de cohortes › Études rétrospectives › Combining Transcranial Doppler and EEG Data to...
questionsmedicales.fr › États, signes et symptômes pathologiques › Processus pathologiques › Hémorragie › Hémorragies intracrâniennes › Hémorragie meningée › Combining Transcranial Doppler and EEG Data to...
questionsmedicales.fr › Techniques d'investigation › Neuroimagerie › Neuroradiographie › Échoencéphalographie › Échographie-doppler transcrânienne › Combining Transcranial Doppler and EEG Data to...
questionsmedicales.fr › Maladies cardiovasculaires › Maladies vasculaires › Angiopathies intracrâniennes › Vasospasme intracrânien › Combining Transcranial Doppler and EEG Data to...