1
|
Sorby-Adams A, Guo J, de Havenon A, Payabvash S, Sze G, Pinter NK, Jaikumar V, Siddiqui A, Baldassano S, Garcia-Guarniz AL, Zabinska J, Lalwani D, Peasley E, Goldstein JN, Nelson OK, Schaefer PW, Wira CR, Pitts J, Lee V, Muir KW, Nimjee SM, Kirsch J, Eugenio Iglesias J, Rosen MS, Sheth KN, Kimberly WT. Diffusion-Weighted Imaging Fluid-Attenuated Inversion Recovery Mismatch on Portable, Low-Field Magnetic Resonance Imaging Among Acute Stroke Patients. Ann Neurol 2024; 96:321-331. [PMID: 38738750 PMCID: PMC11293843 DOI: 10.1002/ana.26954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 04/26/2024] [Accepted: 04/28/2024] [Indexed: 05/14/2024]
Abstract
OBJECTIVE For stroke patients with unknown time of onset, mismatch between diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) can guide thrombolytic intervention. However, access to MRI for hyperacute stroke is limited. Here, we sought to evaluate whether a portable, low-field (LF)-MRI scanner can identify DWI-FLAIR mismatch in acute ischemic stroke. METHODS Eligible patients with a diagnosis of acute ischemic stroke underwent LF-MRI acquisition on a 0.064-T scanner within 24 h of last known well. Qualitative and quantitative metrics were evaluated. Two trained assessors determined the visibility of stroke lesions on LF-FLAIR. An image coregistration pipeline was developed, and the LF-FLAIR signal intensity ratio (SIR) was derived. RESULTS The study included 71 patients aged 71 ± 14 years and a National Institutes of Health Stroke Scale of 6 (interquartile range 3-14). The interobserver agreement for identifying visible FLAIR hyperintensities was high (κ = 0.85, 95% CI 0.70-0.99). Visual DWI-FLAIR mismatch had a 60% sensitivity and 82% specificity for stroke patients <4.5 h, with a negative predictive value of 93%. LF-FLAIR SIR had a mean value of 1.18 ± 0.18 <4.5 h, 1.24 ± 0.39 4.5-6 h, and 1.40 ± 0.23 >6 h of stroke onset. The optimal cut-point for LF-FLAIR SIR was 1.15, with 85% sensitivity and 70% specificity. A cut-point of 6.6 h was established for a FLAIR SIR <1.15, with an 89% sensitivity and 62% specificity. INTERPRETATION A 0.064-T portable LF-MRI can identify DWI-FLAIR mismatch among patients with acute ischemic stroke. Future research is needed to prospectively validate thresholds and evaluate a role of LF-MRI in guiding thrombolysis among stroke patients with uncertain time of onset. ANN NEUROL 2024;96:321-331.
Collapse
Affiliation(s)
- Annabel Sorby-Adams
- Department of Neurology and the Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Jennifer Guo
- Department of Neurology and the Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Adam de Havenon
- Department of Neurology, Yale Center for Brain & Mind Health, Yale School of Medicine, New Haven, CT, USA
| | - Seyedmehdi Payabvash
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Gordon Sze
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Nandor K. Pinter
- Department of Radiology, Jacobs School of Medicine & Biomedical Sciences, University of Buffalo, Buffalo, New York, USA
- Department of Neurosurgery, Jacobs School of Medicine & Biomedical Sciences, University of Buffalo, Buffalo, New York, USA
| | - Vinay Jaikumar
- Department of Neurosurgery, Jacobs School of Medicine & Biomedical Sciences, University of Buffalo, Buffalo, New York, USA
| | - Adnan Siddiqui
- Department of Neurosurgery, Jacobs School of Medicine & Biomedical Sciences, University of Buffalo, Buffalo, New York, USA
| | - Steven Baldassano
- Division of Neuroradiology, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Ana-Lucia Garcia-Guarniz
- Department of Neurology and the Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Julia Zabinska
- Department of Neurology, Yale Center for Brain & Mind Health, Yale School of Medicine, New Haven, CT, USA
| | - Dheeraj Lalwani
- Department of Neurology, Yale Center for Brain & Mind Health, Yale School of Medicine, New Haven, CT, USA
| | - Emma Peasley
- Department of Neurology, Yale Center for Brain & Mind Health, Yale School of Medicine, New Haven, CT, USA
| | - Joshua N. Goldstein
- Department of Emergency Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Olivia K. Nelson
- Department of Emergency Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Pamela W. Schaefer
- Division of Neuroradiology, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Charles R. Wira
- Department of Emergency Medicine, Yale New Haven Hospital and Yale School of Medicine, New Haven, Connecticut, USA
| | - John Pitts
- Hyperfine Incorporated, Guilford, Connecticut, USA
| | - Vivien Lee
- Wexner Medical Center, Ohio State University, Columbus, Ohio, USA
| | - Keith W. Muir
- School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
| | - Shahid M. Nimjee
- Wexner Medical Center, Ohio State University, Columbus, Ohio, USA
| | - John Kirsch
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Juan Eugenio Iglesias
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Centre for Medical Image Computing, University College London, London, UK
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Matthew S. Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Kevin N. Sheth
- Department of Neurology, Yale Center for Brain & Mind Health, Yale School of Medicine, New Haven, CT, USA
| | - W. Taylor Kimberly
- Department of Neurology and the Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| |
Collapse
|
2
|
Evans R, Bolduc PN, Pfaffenbach M, Gao F, May-Dracka T, Fang T, Hopkins BT, Chodaparambil JV, Henry KL, Li P, Metrick C, Nelson A, Trapa P, Thomas A, Burkly L, Peterson EA. The Discovery of 7-Isopropoxy-2-(1-methyl-2-oxabicyclo[2.1.1]hexan-4-yl)- N-(6-methylpyrazolo[1,5- a]pyrimidin-3-yl)imidazo[1,2- a]pyrimidine-6-carboxamide (BIO-7488), a Potent, Selective, and CNS-Penetrant IRAK4 Inhibitor for the Treatment of Ischemic Stroke. J Med Chem 2024. [PMID: 38467640 DOI: 10.1021/acs.jmedchem.3c02226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
Interleukin receptor-associated kinase 4 (IRAK4) is a key node of signaling within the innate immune system that regulates the production of inflammatory cytokines and chemokines. The presence of damage-associated molecular patterns (DAMPs) after tissue damage such as stroke or traumatic brain injury (TBI) initiates signaling through the IRAK4 pathway that can lead to a feed-forward inflammatory loop that can ultimately hinder patient recovery. Herein, we describe the first potent, selective, and CNS-penetrant IRAK4 inhibitors for the treatment of neuroinflammation. Lead compounds from the series were evaluated in CNS PK/PD models of inflammation, as well as a mouse model of ischemic stroke. The SAR optimization detailed within culminates in the discovery of BIO-7488, a highly selective and potent IRAK4 inhibitor that is CNS penetrant and has excellent ADME properties.
Collapse
Affiliation(s)
- Ryan Evans
- Department of Medicinal Chemistry, Biogen, 225 Binney Street, Cambridge, Massachusetts 02142, United States
| | - Philippe N Bolduc
- Department of Medicinal Chemistry, Biogen, 225 Binney Street, Cambridge, Massachusetts 02142, United States
| | - Magnus Pfaffenbach
- Department of Medicinal Chemistry, Biogen, 225 Binney Street, Cambridge, Massachusetts 02142, United States
| | - Fang Gao
- Department of Medicinal Chemistry, Biogen, 225 Binney Street, Cambridge, Massachusetts 02142, United States
| | - Tricia May-Dracka
- Department of Medicinal Chemistry, Biogen, 225 Binney Street, Cambridge, Massachusetts 02142, United States
| | - Terry Fang
- Department of Acute Neurology, Biogen, 225 Binney Street, Cambridge, Massachusetts 02142, United States
| | - Brian T Hopkins
- Department of Medicinal Chemistry, Biogen, 225 Binney Street, Cambridge, Massachusetts 02142, United States
| | - Jayanth V Chodaparambil
- Physical Biochemistry, Biogen, 225 Binney Street, Cambridge, Massachusetts 02142, United States
| | - Kate L Henry
- Department of Acute Neurology, Biogen, 225 Binney Street, Cambridge, Massachusetts 02142, United States
| | - Pei Li
- Drug Metabolism and Pharmacokinetics, Biogen, 225 Binney Street, Cambridge, Massachusetts 02142, United States
| | - Claire Metrick
- Physical Biochemistry, Biogen, 225 Binney Street, Cambridge, Massachusetts 02142, United States
| | - Ashley Nelson
- Department of Acute Neurology, Biogen, 225 Binney Street, Cambridge, Massachusetts 02142, United States
| | - Patrick Trapa
- Drug Metabolism and Pharmacokinetics, Biogen, 225 Binney Street, Cambridge, Massachusetts 02142, United States
| | - Ankur Thomas
- Department of Acute Neurology, Biogen, 225 Binney Street, Cambridge, Massachusetts 02142, United States
| | - Linda Burkly
- Department of Acute Neurology, Biogen, 225 Binney Street, Cambridge, Massachusetts 02142, United States
| | - Emily A Peterson
- Department of Medicinal Chemistry, Biogen, 225 Binney Street, Cambridge, Massachusetts 02142, United States
| |
Collapse
|
3
|
Kossen T, Madai VI, Mutke MA, Hennemuth A, Hildebrand K, Behland J, Aslan C, Hilbert A, Sobesky J, Bendszus M, Frey D. Image-to-image generative adversarial networks for synthesizing perfusion parameter maps from DSC-MR images in cerebrovascular disease. Front Neurol 2023; 13:1051397. [PMID: 36703627 PMCID: PMC9871486 DOI: 10.3389/fneur.2022.1051397] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 12/15/2022] [Indexed: 01/11/2023] Open
Abstract
Stroke is a major cause of death or disability. As imaging-based patient stratification improves acute stroke therapy, dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) is of major interest in image brain perfusion. However, expert-level perfusion maps require a manual or semi-manual post-processing by a medical expert making the procedure time-consuming and less-standardized. Modern machine learning methods such as generative adversarial networks (GANs) have the potential to automate the perfusion map generation on an expert level without manual validation. We propose a modified pix2pix GAN with a temporal component (temp-pix2pix-GAN) that generates perfusion maps in an end-to-end fashion. We train our model on perfusion maps infused with expert knowledge to encode it into the GANs. The performance was trained and evaluated using the structural similarity index measure (SSIM) on two datasets including patients with acute stroke and the steno-occlusive disease. Our temp-pix2pix architecture showed high performance on the acute stroke dataset for all perfusion maps (mean SSIM 0.92-0.99) and good performance on data including patients with the steno-occlusive disease (mean SSIM 0.84-0.99). While clinical validation is still necessary for future studies, our results mark an important step toward automated expert-level perfusion maps and thus fast patient stratification.
Collapse
Affiliation(s)
- Tabea Kossen
- Charité Lab for Artificial Intelligence in Medicine (CLAIM), Charité-Universitätsmedizin Berlin, Berlin, Germany,Department of Computer Engineering and Microelectronics, Computer Vision and Remote Sensing, Technical University Berlin, Berlin, Germany,*Correspondence: Tabea Kossen ✉
| | - Vince I. Madai
- Charité Lab for Artificial Intelligence in Medicine (CLAIM), Charité-Universitätsmedizin Berlin, Berlin, Germany,QUEST Center for Responsible Research, Berlin Institute of Health (BIH), Charité-Universitätsmedizin Berlin, Berlin, Germany,Faculty of Computing, Engineering and the Built Environment, School of Computing and Digital Technology, Birmingham City University, Birmingham, United Kingdom
| | - Matthias A. Mutke
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Anja Hennemuth
- Department of Computer Engineering and Microelectronics, Computer Vision and Remote Sensing, Technical University Berlin, Berlin, Germany,Institute for Imaging Science and Computational Modelling in Cardiovascular Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany,Fraunhofer MEVIS, Bremen, Germany
| | - Kristian Hildebrand
- Department of Computer Science and Media, Berlin University of Applied Sciences and Technology, Berlin, Germany
| | - Jonas Behland
- Charité Lab for Artificial Intelligence in Medicine (CLAIM), Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Cagdas Aslan
- Department of Computer Science and Media, Berlin University of Applied Sciences and Technology, Berlin, Germany
| | - Adam Hilbert
- Charité Lab for Artificial Intelligence in Medicine (CLAIM), Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Jan Sobesky
- Center for Stroke Research Berlin, Charité-Universitätsmedizin Berlin, Berlin, Germany,Johanna-Etienne-Hospital, Neuss, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Dietmar Frey
- Charité Lab for Artificial Intelligence in Medicine (CLAIM), Charité-Universitätsmedizin Berlin, Berlin, Germany
| |
Collapse
|
4
|
Lu Q, Bai Q, Ren H, Zhu B, Jiang T, Peng C, Chen X. Effectiveness and Predictors of Poor Prognosis Following Intravenous Thrombolysis in Patients with Wake-Up Ischemic Stroke Guided by Rapid MRI. Neuropsychiatr Dis Treat 2022; 18:317-325. [PMID: 35210778 PMCID: PMC8860628 DOI: 10.2147/ndt.s351524] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Accepted: 02/06/2022] [Indexed: 11/23/2022] Open
Abstract
PURPOSE Our aim was to investigate the effectiveness and predictors of poor prognosis in WUIS patients who received alteplase thrombolysis under the guidance of diffusion-weighted imaging (DWI)-T2-weighted imaging (T2WI) mismatch. PATIENTS AND METHODS We recruited patients within 4.5 h of acute ischemic stroke (AIS) and WUIS patients with uncertain onset times from two stroke centers. To evaluate effectiveness, we compared National Institutes of Health Stroke Scale (NIHSS) scores between the two groups at admission and at 24 h, 3 days, and 1 week thereafter. We also compared the two groups with respect to the modified Rankin Scale (mRS) score at 90 days after thrombolysis. The WUIS patients were divided into a favorable prognosis group (mRS score: 0-1 points) and a poor prognosis group (mRS score ≥2 points). Data were compared between the two subgroups to identify factors that influence poor prognoses. The receiver operating characteristic (ROC) curve was used to evaluate the predictive value of factors related to poor prognosis. RESULTS A total of 114 patients with WUIS and 316 patients with AIS were enrolled in the study. There were no significant differences between the two groups in terms of NIHSS or 90-day mRS scores (p > 0.05). Baseline NIHSS score (odds ratio [OR] = 1.490, 95% confidence interval [CI] 1.248-1.779, p < 0.001) and atrial fibrillation (OR = 3.825, 95% CI 1.218-12.016, p = 0.022) were identified as independent predictors of poor prognosis following thrombolysis in WUIS patients. The combined ROC diagnosis of these two variables had an area under the curve of 0.850. CONCLUSION The DWI-T2WI sequence is an effective method to guide intravenous thrombolytic therapy for WUIS. Baseline NIHSS score and atrial fibrillation were identified as independent predictors of poor prognosis following thrombolysis in WUIS patients.
Collapse
Affiliation(s)
- Qiuyun Lu
- Department of Neurology, Shanghai Eighth People's Hospital, Shanghai, People's Republic of China
| | - Qingke Bai
- Department of Neurology, Pudong New Area People's Hospital, Shanghai, People's Republic of China
| | - Haiyan Ren
- Department of Neurology, Shanghai Eighth People's Hospital, Shanghai, People's Republic of China
| | - Benju Zhu
- Department of Neurology, Shanghai Eighth People's Hospital, Shanghai, People's Republic of China
| | - Tianfang Jiang
- Department of Neurology, Shanghai Eighth People's Hospital, Shanghai, People's Republic of China
| | - Chen Peng
- Department of Neurology, Shanghai Eighth People's Hospital, Shanghai, People's Republic of China
| | - Xu Chen
- Department of Neurology, Shanghai Eighth People's Hospital, Shanghai, People's Republic of China
| |
Collapse
|
5
|
Grigonyte M, Kraujelyte A, Januskeviciute E, Semys G, Kriukelyte O, Kontrimaviciute E, Valeviciene NR, Jatuzis D. The Impact of the Pandemic on Acute Ischaemic Stroke Endovascular Treatment from a Multidisciplinary Perspective: A Nonsystematic Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:9464. [PMID: 34574386 PMCID: PMC8471435 DOI: 10.3390/ijerph18189464] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 09/04/2021] [Accepted: 09/06/2021] [Indexed: 12/26/2022]
Abstract
BACKGROUND At the beginning of the coronavirus disease 2019 (COVID-19) pandemic, reduced admissions for cerebrovascular events were identified, but acute ischaemic stroke (AIS) has remained one of the leading causes of death and disability for many years. The aim of this article is to review current literature data for multidisciplinary team (MDT) coordination, rational management of resources and facilities, ensuring timely medical care for large vessel occlusion (LVO) AIS patients requiring endovascular treatment during the pandemic. METHODS A detailed literature search was performed in Google Scholar and PubMed databases using these keywords and their combinations: acute ischaemic stroke, emergency, anaesthesia, airway management, mechanical thrombectomy, endovascular treatment, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), COVID-19. Published studies and guidelines from inception to April 2021 were screened. The following nonsystematic review is based on a comprehensive literature search of available data, wherein 59 were chosen for detailed analysis. RESULTS The pandemic has an impact on every aspect of AIS care, including prethrombectomy, intraprocedural and post-thrombectomy issues. Main challenges include institutional preparedness, increased number of AIS patients with multiorgan involvement, different work coordination principles and considerations about preferred anaesthetic technique. Care of these patients is led by MDT and nonoperating room anaesthesia (NORA) principles are applied. CONCLUSIONS Adequate management of AIS patients requiring mechanical thrombectomy during the pandemic is of paramount importance to maximise the benefit of the endovascular procedure. MDT work and familiarity with NORA principles decrease the negative impact of the disease on the clinical outcomes for AIS patients.
Collapse
Affiliation(s)
- Milda Grigonyte
- Faculty of Medicine, Vilnius University, 01513 Vilnius, Lithuania; (M.G.); (A.K.); (E.J.); (G.S.)
| | - Agne Kraujelyte
- Faculty of Medicine, Vilnius University, 01513 Vilnius, Lithuania; (M.G.); (A.K.); (E.J.); (G.S.)
| | - Elija Januskeviciute
- Faculty of Medicine, Vilnius University, 01513 Vilnius, Lithuania; (M.G.); (A.K.); (E.J.); (G.S.)
| | - Giedrius Semys
- Faculty of Medicine, Vilnius University, 01513 Vilnius, Lithuania; (M.G.); (A.K.); (E.J.); (G.S.)
| | - Oresta Kriukelyte
- Faculty of Medicine, Vilnius University, 01513 Vilnius, Lithuania; (M.G.); (A.K.); (E.J.); (G.S.)
| | - Egle Kontrimaviciute
- Clinic of Anaesthesiology and Intensive Care, Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, 01513 Vilnius, Lithuania;
| | - Nomeda Rima Valeviciene
- Department of Radiology, Nuclear Medicine and Medical Physics, Institute of Biomedical Sciences, Faculty of Medicine, Vilnius University, 01513 Vilnius, Lithuania;
| | - Dalius Jatuzis
- Clinic of Neurology and Neurosurgery, Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, 01513 Vilnius, Lithuania;
| |
Collapse
|
6
|
Timing the Ischemic Stroke by Multiparametric Quantitative Magnetic Resonance Imaging. Stroke 2021. [DOI: 10.36255/exonpublications.stroke.timingischemicstroke.2021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
|