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European Radiology
Erschienen in:

15.01.2024 | Neuro

Early and delayed blood-brain barrier permeability predicts delayed cerebral ischemia and outcomes following aneurysmal subarachnoid hemorrhage

verfasst von: Chao Zhang, Wenjuan Tang, Liang Cheng, Chen Yang, Ting Wang, Juan Wang, Zhuang Miao, Xintong Zhao, Xinggen Fang, Yunfeng Zhou

Erschienen in: European Radiology | Ausgabe 8/2024

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Abstract

Objectives

This study aimed to monitor blood-brain barrier permeability within 24 h and during the delayed cerebral ischemia (DCI) time window (DCITW) spanning 4–14 days after aneurysmal subarachnoid hemorrhage (aSAH) and to investigate its correlation with both DCI occurrence and outcomes at three months.

Methods

A total of 128 patients were stratified based on the DCI occurrence and three-month modified Rankin scale scores. Comparison of Ktrans at admission (admission Ktrans) and during DCITW (DCITW Ktrans) was conducted between DCI and non-DCI groups, as well as between groups with good and poor outcomes. Changes in Ktrans were also analyzed. Multivariate logistic regression analysis was performed to identify independent predictors of DCI and poor outcomes.

Results

Admission Ktrans (0.58 ± 0.18 vs 0.47 ± 0.12, p = 0.002) and DCITW Ktrans (0.54 ± 0.19 vs 0.41 ± 0.14, p < 0.001) were significantly higher in the DCI group compared with the non-DCI group. Although both were higher in the poor outcome group than the good outcome group, the difference was not statistically significant at admission (0.53 ± 0.18 vs 0.49 ± 0.14, p = 0.198). Ktrans in the non-DCI group (0.47 ± 0.12 vs 0.41 ± 0.14, p = 0.004) and good outcome group (0.49 ± 0.14 vs 0.41 ± 0.14, p < 0.001) decreased significantly from the admission to DCITW. Multivariate analysis identified DCITW Ktrans and admission Ktrans as independent predictors of poor outcomes (OR = 1.73, 95%CI: 1.24–2.43, p = 0.001) and DCI (OR = 1.75, 95%CI: 1.25–2.44, p = 0.001), respectively.

Conclusion

Elevated Ktrans at admission is associated with the occurrence of DCI. Continuous monitoring of Ktrans from admission to DCITW can accurately identify reversible and irreversible changes and can predict outcomes at 3 months.

Clinical relevance statement

Ktrans measured with CT perfusion is a valuable tool for predicting both delayed cerebral ischemia and three-month outcomes following aneurysmal subarachnoid hemorrhage. Monitoring changes in Ktrans from admission to time window of delayed cerebral ischemia can guide treatment and management decisions for aneurysmal subarachnoid hemorrhage patients.

Key Points

• Ktrans measured at admission and during the delayed cerebral ischemia time window (4–14 days) holds distinct clinical significance following aneurysmal subarachnoid hemorrhage.
• Admission Ktrans serves as a predictor for delayed cerebral ischemia, while continuous assessment of Ktrans from admission to the delayed cerebral ischemia time window can predict three-month outcomes.
• Monitoring Ktrans at different stages improves instrumental in enhancing decision-making and treatment planning for patients with aneurysmal subarachnoid hemorrhage.
Literatur
1.
2.
3.
Roquer J, Cuadrado-Godia E, Guimaraens L et al (2020) Short- and long-term outcome of patients with aneurysmal subarachnoid hemorrhage. Neurology 95:e1819–e1829CrossRefPubMedPubMedCentral
4.
Maher M, Schweizer TA, Macdonald RL (2020) Treatment of spontaneous subarachnoid hemorrhage: guidelines and gaps. Stroke 51:1326–1332CrossRefPubMed
5.
Yuan JY, Chen Y, Kumar A et al (2021) Automated Quantification of Reduced Sulcal Volume Identifies Early Brain Injury After Aneurysmal Subarachnoid Hemorrhage. Stroke 52:1380–1389CrossRefPubMedPubMedCentral
6.
Savarraj JPJ, Hergenroeder GW, Zhu L et al (2021) Machine learning to predict delayed cerebral ischemia and outcomes in subarachnoid hemorrhage. Neurology 96:e553–e562CrossRefPubMedPubMedCentral
7.
8.
Afat S, Brockmann C, Nikoubashman O et al (2018) Diagnostic accuracy of simulated low-dose perfusion CT to detect cerebral perfusion impairment after aneurysmal subarachnoid hemorrhage: a retrospective analysis. Radiology 287:643–650CrossRefPubMed
9.
Connolly ES Jr, Rabinstein AA, Carhuapoma JR et al (2012) Guidelines for the management of aneurysmal subarachnoid hemorrhage: a guideline for healthcare professionals from the American Heart Association/american Stroke Association. Stroke 43:1711–1737CrossRefPubMed
10.
Shao A, Zhou Y, Yao Y, Zhang W, Zhang J, Deng Y (2019) The role and therapeutic potential of heat shock proteins in haemorrhagic stroke. J Cell Mol Med 23:5846–5858CrossRefPubMedPubMedCentral
11.
12.
Chen S, Xu P, Fang Y, Lenahan C (2020) The updated role of the blood brain barrier in subarachnoid hemorrhage: from basic and clinical studies. Curr Neuropharmacol 18:1266–1278CrossRefPubMedPubMedCentral
13.
Aviv RI, d’Esterre CD, Murphy BD et al (2009) Hemorrhagic transformation of ischemic stroke: prediction with CT perfusion. Radiology 250:867–877CrossRefPubMed
14.
Xyda A, Haberland U, Klotz E et al (2012) Diagnostic performance of whole brain volume perfusion CT in intra-axial brain tumors: preoperative classification accuracy and histopathologic correlation. Eur J Radiol 81:4105–4111CrossRefPubMed
15.
Zhang C, Feng L, You F, Zhao X, Fang X, Zhou Y (2022) Whole-brain permeability analysis on admission improves prediction of delayed cerebral ischemia following aneurysmal subarachnoid hemorrhage. J Stroke Cerebrovasc Dis 31:106312CrossRefPubMed
16.
Lublinsky S, Major S, Kola V et al (2019) Early blood-brain barrier dysfunction predicts neurological outcome following aneurysmal subarachnoid hemorrhage. EBioMedicine 43:460–472CrossRefPubMedPubMedCentral
17.
Russin JJ, Montagne A, D’Amore F et al (2018) Permeability imaging as a predictor of delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage. J Cereb Blood Flow Metab 38:973–979CrossRefPubMedPubMedCentral
18.
Malinova V, Iliev B, Tsogkas I, Rohde V, Psychogios MN, Mielke D (2019) Assessment of tissue permeability by early CT perfusion as a surrogate parameter for early brain injury after subarachnoid hemorrhage. J Neurosurg 23:1–6
19.
Ivanidze J, Ferraro RA, Giambrone AE, Segal AZ, Gupta A, Sanelli PC (2018) Blood-brain barrier permeability in aneurysmal subarachnoid hemorrhage: correlation with clinical outcomes. AJR Am J Roentgenol 211:891–895CrossRefPubMed
20.
Ivanidze J, Kesavabhotla K, Kallas ON et al (2015) Evaluating blood-brain barrier permeability in delayed cerebral infarction after aneurysmal subarachnoid hemorrhage. AJNR Am J Neuroradiol 36:850–854CrossRefPubMedPubMedCentral
21.
Murphy A, Manoel AL, Burgers K et al (2015) Early CT perfusion changes and blood-brain barrier permeability after aneurysmal subarachnoid hemorrhage. Neuroradiology 57:767–773CrossRefPubMed
22.
Simpkins AN, Dias C, Leigh R, National Institutes of Health Natural History of Stroke Investigators (2016) Identification of Reversible Disruption of the Human Blood-Brain Barrier Following Acute Ischemia. Stroke 47:2405–2408CrossRefPubMedPubMedCentral
23.
Liu C, Zhang S, Yan S et al (2018) Reperfusion facilitates reversible disruption of the human blood-brain barrier following acute ischaemic stroke. Eur Radiol 28:642–649CrossRefPubMed
24.
25.
Vergouwen MD, Vermeulen M, van Gijn J et al (2010) Definition of delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage as an outcome event in clinical trials and observational studies: proposal of a multidisciplinary research group. Stroke 41:2391–2395CrossRefPubMed
26.
Schramm P, Xyda A, Klotz E, Tronnier V, Knauth M, Hartmann M (2010) Dynamic CT perfusion imaging of intra-axial brain tumours: differentiation of high-grade gliomas from primary CNS lymphomas. Eur Radiol 20(10):2482–2490CrossRefPubMedPubMedCentral
27.
Xyda A, Haberland U, Klotz E et al (2008) Brain volume perfusion CT performed with 128-detector row CT system in patients with cerebral gliomas: a feasibility study. Eur Radiol 21:1811–1819CrossRef
28.
Sandoval KE, Witt KA (2008) Blood-brain barrier tight junction permeability and ischemic stroke. Neurobiol Dis 32:200–219CrossRefPubMed
29.
Anetsberger A, Gempt J, Blobner M et al (2020) Impact of goal-directed therapy on delayed ischemia after aneurysmal subarachnoid hemorrhage: randomized controlled trial. Stroke 51:2287–2296CrossRefPubMed
30.
Salmela MB, Mortazavi S, Jagadeesan BD et al (2017) ACR Appropriateness Criteria® Cerebrovascular Disease. J Am Coll Radiol 14:S34–S61CrossRefPubMed
31.
Rautalin I, Juvela S, Martini ML, Macdonald RL, Korja M (2022) Risk factors for delayed cerebral ischemia in good-grade patients with aneurysmal subarachnoid hemorrhage. J Am Heart Assoc 11:e027453CrossRefPubMedPubMedCentral
32.
Fang YJ, Mei SH, Lu JN et al (2019) New risk score of the early period after spontaneous subarachnoid hemorrhage: for the prediction of delayed cerebral ischemia. CNS Neurosci Ther 25:1173–1181CrossRefPubMedPubMedCentral
33.
Rubbert C, Patil KR, Beseoglu K et al (2018) Prediction of outcome after aneurysmal subarachnoid haemorrhage using data from patient admission. Eur Radiol 28:4949–4958CrossRefPubMed
Metadaten
Titel
Early and delayed blood-brain barrier permeability predicts delayed cerebral ischemia and outcomes following aneurysmal subarachnoid hemorrhage
verfasst von
Chao Zhang
Wenjuan Tang
Liang Cheng
Chen Yang
Ting Wang
Juan Wang
Zhuang Miao
Xintong Zhao
Xinggen Fang
Yunfeng Zhou
Publikationsdatum
15.01.2024
Verlag
Springer Berlin Heidelberg
Erschienen in
European Radiology / Ausgabe 8/2024
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-023-10571-w

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