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Ganesh A, Volny O, Kovacova I, Tomek A, Bar M, Pádr R, Cihlar F, Nevsimalova M, Jurak L, Havlicek R, Kovar M, Sevcik P, Rohan V, Fiksa J, Cerník D, Jura R, Vaclavik D, Hill MD, Mikulík R. Utilization, Workflow, and Outcomes of Endovascular Thrombectomy in Patients With vs Without Premorbid Disability in a National Registry. Neurol Clin Pract 2024; 14:e200341. [PMID: 39185095 PMCID: PMC11341008 DOI: 10.1212/cpj.0000000000200341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 04/17/2024] [Indexed: 08/27/2024]
Abstract
Background and Objectives Given the paucity of high-quality safety/efficacy data on acute stroke therapies in patients with premorbid disability, they risk being routinely excluded from such therapies. We examined utilization of endovascular thrombectomy (EVT), associated workflow, and poststroke outcomes among patients with vs without premorbid disability. Methods We used national registry data on thrombolysis/EVT for the Czech Republic from 1 January 2016 to 31 December 2020. Premorbid disability was defined as prestroke modified Rankin Scale score (mRS) ≥3. We compared proportions of patients with vs without premorbid disability who received EVT and examined workflow times. We compared ΔmRS-change in mRS from prestroke to 3 months-in patients with vs without premorbid disability, in addition to intracerebral hemorrhage (ICH), mortality, and discharge NIHSS (National Institutes of Health Stroke Scale score), adjusting for age, sex, baseline NIHSS, and comorbidities, and verified using propensity score weighting (PSW) and matching for differences in treatment assignment. We stratified by age group (<65, 65-74, 75-84, ≥85 years) to explore outcome heterogeneity with vs without premorbid disability. Results Among 22,405 patients with ischemic stroke who received thrombolysis/EVT/both, 1,712 (7.6%) had prestroke mRS ≥ 3. Patients with prestroke disability were less likely to receive EVT vs those without (10.1% vs 20.7%, aOR: 0.30, 95% CI 0.24-0.36). When treated, they had longer door-to-arterial puncture times (median: 75 minutes, IQR: 58-100 vs 54, IQR: 27-77, adjusted difference: 12.5, 95% CI 2.68-22.3). Patients with prestroke disability receiving thrombolysis/EVT/both had worse ΔmRS (adjusted rate ratio, aIRR on PSW: 1.57, 95% CI 1.43-1.72), rates of 3-month mRS 5-6, discharge NIHSS, and mortality (aOR-PSW [mortality]: 2.54, 95% CI 1.92-3.34), while ICH did not significantly differ. 32.1% of patients with prestroke disability receiving thrombolysis/EVT/both successfully returned to prestroke state, but this proportion ranged from 19.6% for those older than 85 years to 66.0% for those younger than 65 years. Regardless of premorbid disability, EVT was associated with better outcomes including lower ΔmRS (aIRR-PSW: 0.87, 95% CI 0.83-0.91) and mortality, with no interaction of treatment effect by premorbid disability status (e.g., mortality pinteraction = 0.73). EVT recipients with premorbid disability did not differ significantly for several outcomes including ΔmRS (aIRR: 0.99, 95% CI 0.84-1.17) but were more likely to have 3-month mRS 5-6 (70.1% vs 39.5% without premorbid disability, aOR: 1.85, 95% CI 1.12-3.04). Discussion Patients with premorbid disability were less likely to receive EVT, had slower treatment times, and had worse outcomes compared with patients without premorbid disability. However, regardless of premorbid disability, patients fared better with EVT vs medical management and one-third with prestroke disability returned to their prestroke status.
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Affiliation(s)
- Aravind Ganesh
- Calgary Stroke Program (AG, MDH), Departments of Clinical Neurosciences and Community Health Sciences, the Hotchkiss Brain Institute, and the O'Brien Institute for Public Health, University of Calgary Cumming School of Medicine, Canada; Department of Neurology (OV, MB), University Hospital Ostrava, Ostrava; International Clinical Research Centre (OV, IK, RM), Stroke Research Program, St. Anne's University Hospital, Brno; Faculty of Medicine (OV, MB), University Ostrava; Department of Neurology (OV), Charles University, 2nd Medical Faculty; Institute of Health Information and Statistics of the Czech Republic (AT), Prague; Department of Radiology (RP), 2nd Faculty of Medicine, Charles University in Prague and Motol University Hospital; Department of Radiology (FC), Masaryk Hospital, Usti nad Labem; Department of Neurology (MN), Hospital Ceske Budejovice; Neurocenter (LJ), Regional Hospital Liberec; Department of Neurology (RH), Military University Hospital; Department of Neurology (MK), Na Homolce Hospital, Prague; Department of Neurology (PS, VR), Faculty of Medicine in Pilsen, Charles University in Prague; Department of Neurology (JF), Charles University, First Faculty of Medicine and General University Hospital, Prague; Masaryk Hospital Usti nad Labem - KZ a.s. (DC), Department of Neurology-Comprehensive Stroke Center; Department of Neurology (RJ), University Hospital Brno and Faculty of Medicine Masaryk University, Brno; Department of Neurology (DV), AGEL Research and Training Institute, Ostrava Vitkovice Hospital, Czech Republic; Departments of Radiology and Medicine (MDH), University of Calgary Cumming School of Medicine, Alberta, Canada; and Department of Neurology (RM), Tomas Bata Regional Hospital Zlin, Czech Republic
| | - Ondrej Volny
- Calgary Stroke Program (AG, MDH), Departments of Clinical Neurosciences and Community Health Sciences, the Hotchkiss Brain Institute, and the O'Brien Institute for Public Health, University of Calgary Cumming School of Medicine, Canada; Department of Neurology (OV, MB), University Hospital Ostrava, Ostrava; International Clinical Research Centre (OV, IK, RM), Stroke Research Program, St. Anne's University Hospital, Brno; Faculty of Medicine (OV, MB), University Ostrava; Department of Neurology (OV), Charles University, 2nd Medical Faculty; Institute of Health Information and Statistics of the Czech Republic (AT), Prague; Department of Radiology (RP), 2nd Faculty of Medicine, Charles University in Prague and Motol University Hospital; Department of Radiology (FC), Masaryk Hospital, Usti nad Labem; Department of Neurology (MN), Hospital Ceske Budejovice; Neurocenter (LJ), Regional Hospital Liberec; Department of Neurology (RH), Military University Hospital; Department of Neurology (MK), Na Homolce Hospital, Prague; Department of Neurology (PS, VR), Faculty of Medicine in Pilsen, Charles University in Prague; Department of Neurology (JF), Charles University, First Faculty of Medicine and General University Hospital, Prague; Masaryk Hospital Usti nad Labem - KZ a.s. (DC), Department of Neurology-Comprehensive Stroke Center; Department of Neurology (RJ), University Hospital Brno and Faculty of Medicine Masaryk University, Brno; Department of Neurology (DV), AGEL Research and Training Institute, Ostrava Vitkovice Hospital, Czech Republic; Departments of Radiology and Medicine (MDH), University of Calgary Cumming School of Medicine, Alberta, Canada; and Department of Neurology (RM), Tomas Bata Regional Hospital Zlin, Czech Republic
| | - Ingrid Kovacova
- Calgary Stroke Program (AG, MDH), Departments of Clinical Neurosciences and Community Health Sciences, the Hotchkiss Brain Institute, and the O'Brien Institute for Public Health, University of Calgary Cumming School of Medicine, Canada; Department of Neurology (OV, MB), University Hospital Ostrava, Ostrava; International Clinical Research Centre (OV, IK, RM), Stroke Research Program, St. Anne's University Hospital, Brno; Faculty of Medicine (OV, MB), University Ostrava; Department of Neurology (OV), Charles University, 2nd Medical Faculty; Institute of Health Information and Statistics of the Czech Republic (AT), Prague; Department of Radiology (RP), 2nd Faculty of Medicine, Charles University in Prague and Motol University Hospital; Department of Radiology (FC), Masaryk Hospital, Usti nad Labem; Department of Neurology (MN), Hospital Ceske Budejovice; Neurocenter (LJ), Regional Hospital Liberec; Department of Neurology (RH), Military University Hospital; Department of Neurology (MK), Na Homolce Hospital, Prague; Department of Neurology (PS, VR), Faculty of Medicine in Pilsen, Charles University in Prague; Department of Neurology (JF), Charles University, First Faculty of Medicine and General University Hospital, Prague; Masaryk Hospital Usti nad Labem - KZ a.s. (DC), Department of Neurology-Comprehensive Stroke Center; Department of Neurology (RJ), University Hospital Brno and Faculty of Medicine Masaryk University, Brno; Department of Neurology (DV), AGEL Research and Training Institute, Ostrava Vitkovice Hospital, Czech Republic; Departments of Radiology and Medicine (MDH), University of Calgary Cumming School of Medicine, Alberta, Canada; and Department of Neurology (RM), Tomas Bata Regional Hospital Zlin, Czech Republic
| | - Aleš Tomek
- Calgary Stroke Program (AG, MDH), Departments of Clinical Neurosciences and Community Health Sciences, the Hotchkiss Brain Institute, and the O'Brien Institute for Public Health, University of Calgary Cumming School of Medicine, Canada; Department of Neurology (OV, MB), University Hospital Ostrava, Ostrava; International Clinical Research Centre (OV, IK, RM), Stroke Research Program, St. Anne's University Hospital, Brno; Faculty of Medicine (OV, MB), University Ostrava; Department of Neurology (OV), Charles University, 2nd Medical Faculty; Institute of Health Information and Statistics of the Czech Republic (AT), Prague; Department of Radiology (RP), 2nd Faculty of Medicine, Charles University in Prague and Motol University Hospital; Department of Radiology (FC), Masaryk Hospital, Usti nad Labem; Department of Neurology (MN), Hospital Ceske Budejovice; Neurocenter (LJ), Regional Hospital Liberec; Department of Neurology (RH), Military University Hospital; Department of Neurology (MK), Na Homolce Hospital, Prague; Department of Neurology (PS, VR), Faculty of Medicine in Pilsen, Charles University in Prague; Department of Neurology (JF), Charles University, First Faculty of Medicine and General University Hospital, Prague; Masaryk Hospital Usti nad Labem - KZ a.s. (DC), Department of Neurology-Comprehensive Stroke Center; Department of Neurology (RJ), University Hospital Brno and Faculty of Medicine Masaryk University, Brno; Department of Neurology (DV), AGEL Research and Training Institute, Ostrava Vitkovice Hospital, Czech Republic; Departments of Radiology and Medicine (MDH), University of Calgary Cumming School of Medicine, Alberta, Canada; and Department of Neurology (RM), Tomas Bata Regional Hospital Zlin, Czech Republic
| | - Michal Bar
- Calgary Stroke Program (AG, MDH), Departments of Clinical Neurosciences and Community Health Sciences, the Hotchkiss Brain Institute, and the O'Brien Institute for Public Health, University of Calgary Cumming School of Medicine, Canada; Department of Neurology (OV, MB), University Hospital Ostrava, Ostrava; International Clinical Research Centre (OV, IK, RM), Stroke Research Program, St. Anne's University Hospital, Brno; Faculty of Medicine (OV, MB), University Ostrava; Department of Neurology (OV), Charles University, 2nd Medical Faculty; Institute of Health Information and Statistics of the Czech Republic (AT), Prague; Department of Radiology (RP), 2nd Faculty of Medicine, Charles University in Prague and Motol University Hospital; Department of Radiology (FC), Masaryk Hospital, Usti nad Labem; Department of Neurology (MN), Hospital Ceske Budejovice; Neurocenter (LJ), Regional Hospital Liberec; Department of Neurology (RH), Military University Hospital; Department of Neurology (MK), Na Homolce Hospital, Prague; Department of Neurology (PS, VR), Faculty of Medicine in Pilsen, Charles University in Prague; Department of Neurology (JF), Charles University, First Faculty of Medicine and General University Hospital, Prague; Masaryk Hospital Usti nad Labem - KZ a.s. (DC), Department of Neurology-Comprehensive Stroke Center; Department of Neurology (RJ), University Hospital Brno and Faculty of Medicine Masaryk University, Brno; Department of Neurology (DV), AGEL Research and Training Institute, Ostrava Vitkovice Hospital, Czech Republic; Departments of Radiology and Medicine (MDH), University of Calgary Cumming School of Medicine, Alberta, Canada; and Department of Neurology (RM), Tomas Bata Regional Hospital Zlin, Czech Republic
| | - Radek Pádr
- Calgary Stroke Program (AG, MDH), Departments of Clinical Neurosciences and Community Health Sciences, the Hotchkiss Brain Institute, and the O'Brien Institute for Public Health, University of Calgary Cumming School of Medicine, Canada; Department of Neurology (OV, MB), University Hospital Ostrava, Ostrava; International Clinical Research Centre (OV, IK, RM), Stroke Research Program, St. Anne's University Hospital, Brno; Faculty of Medicine (OV, MB), University Ostrava; Department of Neurology (OV), Charles University, 2nd Medical Faculty; Institute of Health Information and Statistics of the Czech Republic (AT), Prague; Department of Radiology (RP), 2nd Faculty of Medicine, Charles University in Prague and Motol University Hospital; Department of Radiology (FC), Masaryk Hospital, Usti nad Labem; Department of Neurology (MN), Hospital Ceske Budejovice; Neurocenter (LJ), Regional Hospital Liberec; Department of Neurology (RH), Military University Hospital; Department of Neurology (MK), Na Homolce Hospital, Prague; Department of Neurology (PS, VR), Faculty of Medicine in Pilsen, Charles University in Prague; Department of Neurology (JF), Charles University, First Faculty of Medicine and General University Hospital, Prague; Masaryk Hospital Usti nad Labem - KZ a.s. (DC), Department of Neurology-Comprehensive Stroke Center; Department of Neurology (RJ), University Hospital Brno and Faculty of Medicine Masaryk University, Brno; Department of Neurology (DV), AGEL Research and Training Institute, Ostrava Vitkovice Hospital, Czech Republic; Departments of Radiology and Medicine (MDH), University of Calgary Cumming School of Medicine, Alberta, Canada; and Department of Neurology (RM), Tomas Bata Regional Hospital Zlin, Czech Republic
| | - Filip Cihlar
- Calgary Stroke Program (AG, MDH), Departments of Clinical Neurosciences and Community Health Sciences, the Hotchkiss Brain Institute, and the O'Brien Institute for Public Health, University of Calgary Cumming School of Medicine, Canada; Department of Neurology (OV, MB), University Hospital Ostrava, Ostrava; International Clinical Research Centre (OV, IK, RM), Stroke Research Program, St. Anne's University Hospital, Brno; Faculty of Medicine (OV, MB), University Ostrava; Department of Neurology (OV), Charles University, 2nd Medical Faculty; Institute of Health Information and Statistics of the Czech Republic (AT), Prague; Department of Radiology (RP), 2nd Faculty of Medicine, Charles University in Prague and Motol University Hospital; Department of Radiology (FC), Masaryk Hospital, Usti nad Labem; Department of Neurology (MN), Hospital Ceske Budejovice; Neurocenter (LJ), Regional Hospital Liberec; Department of Neurology (RH), Military University Hospital; Department of Neurology (MK), Na Homolce Hospital, Prague; Department of Neurology (PS, VR), Faculty of Medicine in Pilsen, Charles University in Prague; Department of Neurology (JF), Charles University, First Faculty of Medicine and General University Hospital, Prague; Masaryk Hospital Usti nad Labem - KZ a.s. (DC), Department of Neurology-Comprehensive Stroke Center; Department of Neurology (RJ), University Hospital Brno and Faculty of Medicine Masaryk University, Brno; Department of Neurology (DV), AGEL Research and Training Institute, Ostrava Vitkovice Hospital, Czech Republic; Departments of Radiology and Medicine (MDH), University of Calgary Cumming School of Medicine, Alberta, Canada; and Department of Neurology (RM), Tomas Bata Regional Hospital Zlin, Czech Republic
| | - Miroslava Nevsimalova
- Calgary Stroke Program (AG, MDH), Departments of Clinical Neurosciences and Community Health Sciences, the Hotchkiss Brain Institute, and the O'Brien Institute for Public Health, University of Calgary Cumming School of Medicine, Canada; Department of Neurology (OV, MB), University Hospital Ostrava, Ostrava; International Clinical Research Centre (OV, IK, RM), Stroke Research Program, St. Anne's University Hospital, Brno; Faculty of Medicine (OV, MB), University Ostrava; Department of Neurology (OV), Charles University, 2nd Medical Faculty; Institute of Health Information and Statistics of the Czech Republic (AT), Prague; Department of Radiology (RP), 2nd Faculty of Medicine, Charles University in Prague and Motol University Hospital; Department of Radiology (FC), Masaryk Hospital, Usti nad Labem; Department of Neurology (MN), Hospital Ceske Budejovice; Neurocenter (LJ), Regional Hospital Liberec; Department of Neurology (RH), Military University Hospital; Department of Neurology (MK), Na Homolce Hospital, Prague; Department of Neurology (PS, VR), Faculty of Medicine in Pilsen, Charles University in Prague; Department of Neurology (JF), Charles University, First Faculty of Medicine and General University Hospital, Prague; Masaryk Hospital Usti nad Labem - KZ a.s. (DC), Department of Neurology-Comprehensive Stroke Center; Department of Neurology (RJ), University Hospital Brno and Faculty of Medicine Masaryk University, Brno; Department of Neurology (DV), AGEL Research and Training Institute, Ostrava Vitkovice Hospital, Czech Republic; Departments of Radiology and Medicine (MDH), University of Calgary Cumming School of Medicine, Alberta, Canada; and Department of Neurology (RM), Tomas Bata Regional Hospital Zlin, Czech Republic
| | - Lubomir Jurak
- Calgary Stroke Program (AG, MDH), Departments of Clinical Neurosciences and Community Health Sciences, the Hotchkiss Brain Institute, and the O'Brien Institute for Public Health, University of Calgary Cumming School of Medicine, Canada; Department of Neurology (OV, MB), University Hospital Ostrava, Ostrava; International Clinical Research Centre (OV, IK, RM), Stroke Research Program, St. Anne's University Hospital, Brno; Faculty of Medicine (OV, MB), University Ostrava; Department of Neurology (OV), Charles University, 2nd Medical Faculty; Institute of Health Information and Statistics of the Czech Republic (AT), Prague; Department of Radiology (RP), 2nd Faculty of Medicine, Charles University in Prague and Motol University Hospital; Department of Radiology (FC), Masaryk Hospital, Usti nad Labem; Department of Neurology (MN), Hospital Ceske Budejovice; Neurocenter (LJ), Regional Hospital Liberec; Department of Neurology (RH), Military University Hospital; Department of Neurology (MK), Na Homolce Hospital, Prague; Department of Neurology (PS, VR), Faculty of Medicine in Pilsen, Charles University in Prague; Department of Neurology (JF), Charles University, First Faculty of Medicine and General University Hospital, Prague; Masaryk Hospital Usti nad Labem - KZ a.s. (DC), Department of Neurology-Comprehensive Stroke Center; Department of Neurology (RJ), University Hospital Brno and Faculty of Medicine Masaryk University, Brno; Department of Neurology (DV), AGEL Research and Training Institute, Ostrava Vitkovice Hospital, Czech Republic; Departments of Radiology and Medicine (MDH), University of Calgary Cumming School of Medicine, Alberta, Canada; and Department of Neurology (RM), Tomas Bata Regional Hospital Zlin, Czech Republic
| | - Roman Havlicek
- Calgary Stroke Program (AG, MDH), Departments of Clinical Neurosciences and Community Health Sciences, the Hotchkiss Brain Institute, and the O'Brien Institute for Public Health, University of Calgary Cumming School of Medicine, Canada; Department of Neurology (OV, MB), University Hospital Ostrava, Ostrava; International Clinical Research Centre (OV, IK, RM), Stroke Research Program, St. Anne's University Hospital, Brno; Faculty of Medicine (OV, MB), University Ostrava; Department of Neurology (OV), Charles University, 2nd Medical Faculty; Institute of Health Information and Statistics of the Czech Republic (AT), Prague; Department of Radiology (RP), 2nd Faculty of Medicine, Charles University in Prague and Motol University Hospital; Department of Radiology (FC), Masaryk Hospital, Usti nad Labem; Department of Neurology (MN), Hospital Ceske Budejovice; Neurocenter (LJ), Regional Hospital Liberec; Department of Neurology (RH), Military University Hospital; Department of Neurology (MK), Na Homolce Hospital, Prague; Department of Neurology (PS, VR), Faculty of Medicine in Pilsen, Charles University in Prague; Department of Neurology (JF), Charles University, First Faculty of Medicine and General University Hospital, Prague; Masaryk Hospital Usti nad Labem - KZ a.s. (DC), Department of Neurology-Comprehensive Stroke Center; Department of Neurology (RJ), University Hospital Brno and Faculty of Medicine Masaryk University, Brno; Department of Neurology (DV), AGEL Research and Training Institute, Ostrava Vitkovice Hospital, Czech Republic; Departments of Radiology and Medicine (MDH), University of Calgary Cumming School of Medicine, Alberta, Canada; and Department of Neurology (RM), Tomas Bata Regional Hospital Zlin, Czech Republic
| | - Martin Kovar
- Calgary Stroke Program (AG, MDH), Departments of Clinical Neurosciences and Community Health Sciences, the Hotchkiss Brain Institute, and the O'Brien Institute for Public Health, University of Calgary Cumming School of Medicine, Canada; Department of Neurology (OV, MB), University Hospital Ostrava, Ostrava; International Clinical Research Centre (OV, IK, RM), Stroke Research Program, St. Anne's University Hospital, Brno; Faculty of Medicine (OV, MB), University Ostrava; Department of Neurology (OV), Charles University, 2nd Medical Faculty; Institute of Health Information and Statistics of the Czech Republic (AT), Prague; Department of Radiology (RP), 2nd Faculty of Medicine, Charles University in Prague and Motol University Hospital; Department of Radiology (FC), Masaryk Hospital, Usti nad Labem; Department of Neurology (MN), Hospital Ceske Budejovice; Neurocenter (LJ), Regional Hospital Liberec; Department of Neurology (RH), Military University Hospital; Department of Neurology (MK), Na Homolce Hospital, Prague; Department of Neurology (PS, VR), Faculty of Medicine in Pilsen, Charles University in Prague; Department of Neurology (JF), Charles University, First Faculty of Medicine and General University Hospital, Prague; Masaryk Hospital Usti nad Labem - KZ a.s. (DC), Department of Neurology-Comprehensive Stroke Center; Department of Neurology (RJ), University Hospital Brno and Faculty of Medicine Masaryk University, Brno; Department of Neurology (DV), AGEL Research and Training Institute, Ostrava Vitkovice Hospital, Czech Republic; Departments of Radiology and Medicine (MDH), University of Calgary Cumming School of Medicine, Alberta, Canada; and Department of Neurology (RM), Tomas Bata Regional Hospital Zlin, Czech Republic
| | - Petr Sevcik
- Calgary Stroke Program (AG, MDH), Departments of Clinical Neurosciences and Community Health Sciences, the Hotchkiss Brain Institute, and the O'Brien Institute for Public Health, University of Calgary Cumming School of Medicine, Canada; Department of Neurology (OV, MB), University Hospital Ostrava, Ostrava; International Clinical Research Centre (OV, IK, RM), Stroke Research Program, St. Anne's University Hospital, Brno; Faculty of Medicine (OV, MB), University Ostrava; Department of Neurology (OV), Charles University, 2nd Medical Faculty; Institute of Health Information and Statistics of the Czech Republic (AT), Prague; Department of Radiology (RP), 2nd Faculty of Medicine, Charles University in Prague and Motol University Hospital; Department of Radiology (FC), Masaryk Hospital, Usti nad Labem; Department of Neurology (MN), Hospital Ceske Budejovice; Neurocenter (LJ), Regional Hospital Liberec; Department of Neurology (RH), Military University Hospital; Department of Neurology (MK), Na Homolce Hospital, Prague; Department of Neurology (PS, VR), Faculty of Medicine in Pilsen, Charles University in Prague; Department of Neurology (JF), Charles University, First Faculty of Medicine and General University Hospital, Prague; Masaryk Hospital Usti nad Labem - KZ a.s. (DC), Department of Neurology-Comprehensive Stroke Center; Department of Neurology (RJ), University Hospital Brno and Faculty of Medicine Masaryk University, Brno; Department of Neurology (DV), AGEL Research and Training Institute, Ostrava Vitkovice Hospital, Czech Republic; Departments of Radiology and Medicine (MDH), University of Calgary Cumming School of Medicine, Alberta, Canada; and Department of Neurology (RM), Tomas Bata Regional Hospital Zlin, Czech Republic
| | - Vladimír Rohan
- Calgary Stroke Program (AG, MDH), Departments of Clinical Neurosciences and Community Health Sciences, the Hotchkiss Brain Institute, and the O'Brien Institute for Public Health, University of Calgary Cumming School of Medicine, Canada; Department of Neurology (OV, MB), University Hospital Ostrava, Ostrava; International Clinical Research Centre (OV, IK, RM), Stroke Research Program, St. Anne's University Hospital, Brno; Faculty of Medicine (OV, MB), University Ostrava; Department of Neurology (OV), Charles University, 2nd Medical Faculty; Institute of Health Information and Statistics of the Czech Republic (AT), Prague; Department of Radiology (RP), 2nd Faculty of Medicine, Charles University in Prague and Motol University Hospital; Department of Radiology (FC), Masaryk Hospital, Usti nad Labem; Department of Neurology (MN), Hospital Ceske Budejovice; Neurocenter (LJ), Regional Hospital Liberec; Department of Neurology (RH), Military University Hospital; Department of Neurology (MK), Na Homolce Hospital, Prague; Department of Neurology (PS, VR), Faculty of Medicine in Pilsen, Charles University in Prague; Department of Neurology (JF), Charles University, First Faculty of Medicine and General University Hospital, Prague; Masaryk Hospital Usti nad Labem - KZ a.s. (DC), Department of Neurology-Comprehensive Stroke Center; Department of Neurology (RJ), University Hospital Brno and Faculty of Medicine Masaryk University, Brno; Department of Neurology (DV), AGEL Research and Training Institute, Ostrava Vitkovice Hospital, Czech Republic; Departments of Radiology and Medicine (MDH), University of Calgary Cumming School of Medicine, Alberta, Canada; and Department of Neurology (RM), Tomas Bata Regional Hospital Zlin, Czech Republic
| | - Jan Fiksa
- Calgary Stroke Program (AG, MDH), Departments of Clinical Neurosciences and Community Health Sciences, the Hotchkiss Brain Institute, and the O'Brien Institute for Public Health, University of Calgary Cumming School of Medicine, Canada; Department of Neurology (OV, MB), University Hospital Ostrava, Ostrava; International Clinical Research Centre (OV, IK, RM), Stroke Research Program, St. Anne's University Hospital, Brno; Faculty of Medicine (OV, MB), University Ostrava; Department of Neurology (OV), Charles University, 2nd Medical Faculty; Institute of Health Information and Statistics of the Czech Republic (AT), Prague; Department of Radiology (RP), 2nd Faculty of Medicine, Charles University in Prague and Motol University Hospital; Department of Radiology (FC), Masaryk Hospital, Usti nad Labem; Department of Neurology (MN), Hospital Ceske Budejovice; Neurocenter (LJ), Regional Hospital Liberec; Department of Neurology (RH), Military University Hospital; Department of Neurology (MK), Na Homolce Hospital, Prague; Department of Neurology (PS, VR), Faculty of Medicine in Pilsen, Charles University in Prague; Department of Neurology (JF), Charles University, First Faculty of Medicine and General University Hospital, Prague; Masaryk Hospital Usti nad Labem - KZ a.s. (DC), Department of Neurology-Comprehensive Stroke Center; Department of Neurology (RJ), University Hospital Brno and Faculty of Medicine Masaryk University, Brno; Department of Neurology (DV), AGEL Research and Training Institute, Ostrava Vitkovice Hospital, Czech Republic; Departments of Radiology and Medicine (MDH), University of Calgary Cumming School of Medicine, Alberta, Canada; and Department of Neurology (RM), Tomas Bata Regional Hospital Zlin, Czech Republic
| | - David Cerník
- Calgary Stroke Program (AG, MDH), Departments of Clinical Neurosciences and Community Health Sciences, the Hotchkiss Brain Institute, and the O'Brien Institute for Public Health, University of Calgary Cumming School of Medicine, Canada; Department of Neurology (OV, MB), University Hospital Ostrava, Ostrava; International Clinical Research Centre (OV, IK, RM), Stroke Research Program, St. Anne's University Hospital, Brno; Faculty of Medicine (OV, MB), University Ostrava; Department of Neurology (OV), Charles University, 2nd Medical Faculty; Institute of Health Information and Statistics of the Czech Republic (AT), Prague; Department of Radiology (RP), 2nd Faculty of Medicine, Charles University in Prague and Motol University Hospital; Department of Radiology (FC), Masaryk Hospital, Usti nad Labem; Department of Neurology (MN), Hospital Ceske Budejovice; Neurocenter (LJ), Regional Hospital Liberec; Department of Neurology (RH), Military University Hospital; Department of Neurology (MK), Na Homolce Hospital, Prague; Department of Neurology (PS, VR), Faculty of Medicine in Pilsen, Charles University in Prague; Department of Neurology (JF), Charles University, First Faculty of Medicine and General University Hospital, Prague; Masaryk Hospital Usti nad Labem - KZ a.s. (DC), Department of Neurology-Comprehensive Stroke Center; Department of Neurology (RJ), University Hospital Brno and Faculty of Medicine Masaryk University, Brno; Department of Neurology (DV), AGEL Research and Training Institute, Ostrava Vitkovice Hospital, Czech Republic; Departments of Radiology and Medicine (MDH), University of Calgary Cumming School of Medicine, Alberta, Canada; and Department of Neurology (RM), Tomas Bata Regional Hospital Zlin, Czech Republic
| | - Rene Jura
- Calgary Stroke Program (AG, MDH), Departments of Clinical Neurosciences and Community Health Sciences, the Hotchkiss Brain Institute, and the O'Brien Institute for Public Health, University of Calgary Cumming School of Medicine, Canada; Department of Neurology (OV, MB), University Hospital Ostrava, Ostrava; International Clinical Research Centre (OV, IK, RM), Stroke Research Program, St. Anne's University Hospital, Brno; Faculty of Medicine (OV, MB), University Ostrava; Department of Neurology (OV), Charles University, 2nd Medical Faculty; Institute of Health Information and Statistics of the Czech Republic (AT), Prague; Department of Radiology (RP), 2nd Faculty of Medicine, Charles University in Prague and Motol University Hospital; Department of Radiology (FC), Masaryk Hospital, Usti nad Labem; Department of Neurology (MN), Hospital Ceske Budejovice; Neurocenter (LJ), Regional Hospital Liberec; Department of Neurology (RH), Military University Hospital; Department of Neurology (MK), Na Homolce Hospital, Prague; Department of Neurology (PS, VR), Faculty of Medicine in Pilsen, Charles University in Prague; Department of Neurology (JF), Charles University, First Faculty of Medicine and General University Hospital, Prague; Masaryk Hospital Usti nad Labem - KZ a.s. (DC), Department of Neurology-Comprehensive Stroke Center; Department of Neurology (RJ), University Hospital Brno and Faculty of Medicine Masaryk University, Brno; Department of Neurology (DV), AGEL Research and Training Institute, Ostrava Vitkovice Hospital, Czech Republic; Departments of Radiology and Medicine (MDH), University of Calgary Cumming School of Medicine, Alberta, Canada; and Department of Neurology (RM), Tomas Bata Regional Hospital Zlin, Czech Republic
| | - Daniel Vaclavik
- Calgary Stroke Program (AG, MDH), Departments of Clinical Neurosciences and Community Health Sciences, the Hotchkiss Brain Institute, and the O'Brien Institute for Public Health, University of Calgary Cumming School of Medicine, Canada; Department of Neurology (OV, MB), University Hospital Ostrava, Ostrava; International Clinical Research Centre (OV, IK, RM), Stroke Research Program, St. Anne's University Hospital, Brno; Faculty of Medicine (OV, MB), University Ostrava; Department of Neurology (OV), Charles University, 2nd Medical Faculty; Institute of Health Information and Statistics of the Czech Republic (AT), Prague; Department of Radiology (RP), 2nd Faculty of Medicine, Charles University in Prague and Motol University Hospital; Department of Radiology (FC), Masaryk Hospital, Usti nad Labem; Department of Neurology (MN), Hospital Ceske Budejovice; Neurocenter (LJ), Regional Hospital Liberec; Department of Neurology (RH), Military University Hospital; Department of Neurology (MK), Na Homolce Hospital, Prague; Department of Neurology (PS, VR), Faculty of Medicine in Pilsen, Charles University in Prague; Department of Neurology (JF), Charles University, First Faculty of Medicine and General University Hospital, Prague; Masaryk Hospital Usti nad Labem - KZ a.s. (DC), Department of Neurology-Comprehensive Stroke Center; Department of Neurology (RJ), University Hospital Brno and Faculty of Medicine Masaryk University, Brno; Department of Neurology (DV), AGEL Research and Training Institute, Ostrava Vitkovice Hospital, Czech Republic; Departments of Radiology and Medicine (MDH), University of Calgary Cumming School of Medicine, Alberta, Canada; and Department of Neurology (RM), Tomas Bata Regional Hospital Zlin, Czech Republic
| | - Michael D Hill
- Calgary Stroke Program (AG, MDH), Departments of Clinical Neurosciences and Community Health Sciences, the Hotchkiss Brain Institute, and the O'Brien Institute for Public Health, University of Calgary Cumming School of Medicine, Canada; Department of Neurology (OV, MB), University Hospital Ostrava, Ostrava; International Clinical Research Centre (OV, IK, RM), Stroke Research Program, St. Anne's University Hospital, Brno; Faculty of Medicine (OV, MB), University Ostrava; Department of Neurology (OV), Charles University, 2nd Medical Faculty; Institute of Health Information and Statistics of the Czech Republic (AT), Prague; Department of Radiology (RP), 2nd Faculty of Medicine, Charles University in Prague and Motol University Hospital; Department of Radiology (FC), Masaryk Hospital, Usti nad Labem; Department of Neurology (MN), Hospital Ceske Budejovice; Neurocenter (LJ), Regional Hospital Liberec; Department of Neurology (RH), Military University Hospital; Department of Neurology (MK), Na Homolce Hospital, Prague; Department of Neurology (PS, VR), Faculty of Medicine in Pilsen, Charles University in Prague; Department of Neurology (JF), Charles University, First Faculty of Medicine and General University Hospital, Prague; Masaryk Hospital Usti nad Labem - KZ a.s. (DC), Department of Neurology-Comprehensive Stroke Center; Department of Neurology (RJ), University Hospital Brno and Faculty of Medicine Masaryk University, Brno; Department of Neurology (DV), AGEL Research and Training Institute, Ostrava Vitkovice Hospital, Czech Republic; Departments of Radiology and Medicine (MDH), University of Calgary Cumming School of Medicine, Alberta, Canada; and Department of Neurology (RM), Tomas Bata Regional Hospital Zlin, Czech Republic
| | - Robert Mikulík
- Calgary Stroke Program (AG, MDH), Departments of Clinical Neurosciences and Community Health Sciences, the Hotchkiss Brain Institute, and the O'Brien Institute for Public Health, University of Calgary Cumming School of Medicine, Canada; Department of Neurology (OV, MB), University Hospital Ostrava, Ostrava; International Clinical Research Centre (OV, IK, RM), Stroke Research Program, St. Anne's University Hospital, Brno; Faculty of Medicine (OV, MB), University Ostrava; Department of Neurology (OV), Charles University, 2nd Medical Faculty; Institute of Health Information and Statistics of the Czech Republic (AT), Prague; Department of Radiology (RP), 2nd Faculty of Medicine, Charles University in Prague and Motol University Hospital; Department of Radiology (FC), Masaryk Hospital, Usti nad Labem; Department of Neurology (MN), Hospital Ceske Budejovice; Neurocenter (LJ), Regional Hospital Liberec; Department of Neurology (RH), Military University Hospital; Department of Neurology (MK), Na Homolce Hospital, Prague; Department of Neurology (PS, VR), Faculty of Medicine in Pilsen, Charles University in Prague; Department of Neurology (JF), Charles University, First Faculty of Medicine and General University Hospital, Prague; Masaryk Hospital Usti nad Labem - KZ a.s. (DC), Department of Neurology-Comprehensive Stroke Center; Department of Neurology (RJ), University Hospital Brno and Faculty of Medicine Masaryk University, Brno; Department of Neurology (DV), AGEL Research and Training Institute, Ostrava Vitkovice Hospital, Czech Republic; Departments of Radiology and Medicine (MDH), University of Calgary Cumming School of Medicine, Alberta, Canada; and Department of Neurology (RM), Tomas Bata Regional Hospital Zlin, Czech Republic
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2
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Huang Q, Shou GL, Shi B, Li ML, Zhang S, Han M, Hu FY. Machine learning is an effective method to predict the 3-month prognosis of patients with acute ischemic stroke. Front Neurol 2024; 15:1407152. [PMID: 38938777 PMCID: PMC11210277 DOI: 10.3389/fneur.2024.1407152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Accepted: 05/27/2024] [Indexed: 06/29/2024] Open
Abstract
Background and objectives Upwards of 50% of acute ischemic stroke (AIS) survivors endure varying degrees of disability, with a recurrence rate of 17.7%. Thus, the prediction of outcomes in AIS may be useful for treatment decisions. This study aimed to determine the applicability of a machine learning approach for forecasting early outcomes in AIS patients. Methods A total of 659 patients with new-onset AIS admitted to the Department of Neurology of both the First and Second Affiliated Hospitals of Bengbu Medical University from January 2020 to October 2022 included in the study. The patient' demographic information, medical history, Trial of Org 10,172 in Acute Stroke Treatment (TOAST), National Institute of Health Stroke Scale (NIHSS) and laboratory indicators at 24 h of admission data were collected. The Modified Rankine Scale (mRS) was used to assess the 3-mouth outcome of participants' prognosis. We constructed nine machine learning models based on 18 parameters and compared their accuracies for outcome variables. Results Feature selection through the Least Absolute Shrinkage and Selection Operator cross-validation (Lasso CV) method identified the most critical predictors for early prognosis in AIS patients as white blood cell (WBC), homocysteine (HCY), D-Dimer, baseline NIHSS, fibrinogen degradation product (FDP), and glucose (GLU). Among the nine machine learning models evaluated, the Random Forest model exhibited superior performance in the test set, achieving an Area Under the Curve (AUC) of 0.852, an accuracy rate of 0.818, a sensitivity of 0.654, a specificity of 0.945, and a recall rate of 0.900. Conclusion These findings indicate that RF models utilizing general clinical and laboratory data from the initial 24 h of admission can effectively predict the early prognosis of AIS patients.
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Affiliation(s)
- Qing Huang
- School of Public Health, Bengbu Medical University, Bengbu, Anhui, China
| | - Guang-Li Shou
- Department of Neurology, The Second Affiliated Hospital, Bengbu Medical University, Anhui, China
| | - Bo Shi
- School of Medical Imaging, Bengbu Medical University, Anhui, China
| | - Meng-Lei Li
- Department of Emergency Medicine, The Second Affiliated Hospital, Bengbu Medical University, Anhui, China
| | - Sai Zhang
- School of Medical Imaging, Bengbu Medical University, Anhui, China
| | - Mei Han
- School of Public Health, Bengbu Medical University, Bengbu, Anhui, China
| | - Fu-Yong Hu
- School of Public Health, Bengbu Medical University, Bengbu, Anhui, China
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3
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Jung HS, Lee EJ, Chang DI, Cho HJ, Lee J, Cha JK, Park MS, Yu KH, Jung JM, Ahn SH, Kim DE, Lee JH, Hong KS, Sohn SI, Park KP, Kwon SU, Kim JS, Chang JY, Kim BJ, Kang DW. A Multimodal Ensemble Deep Learning Model for Functional Outcome Prognosis of Stroke Patients. J Stroke 2024; 26:312-320. [PMID: 38836278 PMCID: PMC11164594 DOI: 10.5853/jos.2023.03426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 04/23/2024] [Indexed: 06/06/2024] Open
Abstract
BACKGROUND AND PURPOSE The accurate prediction of functional outcomes in patients with acute ischemic stroke (AIS) is crucial for informed clinical decision-making and optimal resource utilization. As such, this study aimed to construct an ensemble deep learning model that integrates multimodal imaging and clinical data to predict the 90-day functional outcomes after AIS. METHODS We used data from the Korean Stroke Neuroimaging Initiative database, a prospective multicenter stroke registry to construct an ensemble model integrated individual 3D convolutional neural networks for diffusion-weighted imaging and fluid-attenuated inversion recovery (FLAIR), along with a deep neural network for clinical data, to predict 90-day functional independence after AIS using a modified Rankin Scale (mRS) of 3-6. To evaluate the performance of the ensemble model, we compared the area under the curve (AUC) of the proposed method with that of individual models trained on each modality to identify patients with AIS with an mRS score of 3-6. RESULTS Of the 2,606 patients with AIS, 993 (38.1%) achieved an mRS score of 3-6 at 90 days post-stroke. Our model achieved AUC values of 0.830 (standard cross-validation [CV]) and 0.779 (time-based CV), which significantly outperformed the other models relying on single modalities: b-value of 1,000 s/mm2 (P<0.001), apparent diffusion coefficient map (P<0.001), FLAIR (P<0.001), and clinical data (P=0.004). CONCLUSION The integration of multimodal imaging and clinical data resulted in superior prediction of the 90-day functional outcomes in AIS patients compared to the use of a single data modality.
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Affiliation(s)
- Hye-Soo Jung
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Eun-Jae Lee
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Dae-Il Chang
- Department of Neurology, Kyung Hee University Medical Center, Seoul, Korea
| | - Han Jin Cho
- Department of Neurology, Pusan National University Hospital, Busan, Korea
| | - Jun Lee
- Department of Neurology, Yeungnam University Medical Center, Daegu, Korea
| | - Jae-Kwan Cha
- Department of Neurology, Dong-A University Hospital, Busan, Korea
| | - Man-Seok Park
- Department of Neurology, Chonnam National University Hospital, Gwangju, Korea
| | - Kyung Ho Yu
- Department of Neurology, Hallym University Sacred Heart Hospital, Anyang, Korea
| | - Jin-Man Jung
- Department of Neurology, Korea University Ansan Hospital, Ansan, Korea
| | - Seong Hwan Ahn
- Department of Neurology, Chosun University Hospital, Gwangju, Korea
| | - Dong-Eog Kim
- Department of Neurology, Dongguk University Ilsan Hospital, Ilsan, Korea
| | - Ju Hun Lee
- Department of Neurology, Hallym University Kangdong Sacred Heart Hospital, Seoul, Korea
| | - Keun-Sik Hong
- Department of Neurology, Inje University Ilsan Paik Hospital, Ilsan, Korea
| | - Sung-Il Sohn
- Department of Neurology, Keimyung University Medical Center, Daegu, Korea
| | - Kyung-Pil Park
- Department of Neurology, Pusan National University Yangsan Hospital, Yangsan, Korea
| | - Sun U Kwon
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jong S Kim
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jun Young Chang
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Bum Joon Kim
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Dong-Wha Kang
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
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Martín Vicario C, Rodríguez Salas D, Maier A, Hock S, Kuramatsu J, Kallmuenzer B, Thamm F, Taubmann O, Ditt H, Schwab S, Dörfler A, Muehlen I. Uncertainty-aware deep learning for trustworthy prediction of long-term outcome after endovascular thrombectomy. Sci Rep 2024; 14:5544. [PMID: 38448445 PMCID: PMC10917742 DOI: 10.1038/s41598-024-55761-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 02/27/2024] [Indexed: 03/08/2024] Open
Abstract
Acute ischemic stroke (AIS) is a leading global cause of mortality and morbidity. Improving long-term outcome predictions after thrombectomy can enhance treatment quality by supporting clinical decision-making. With the advent of interpretable deep learning methods in recent years, it is now possible to develop trustworthy, high-performing prediction models. This study introduces an uncertainty-aware, graph deep learning model that predicts endovascular thrombectomy outcomes using clinical features and imaging biomarkers. The model targets long-term functional outcomes, defined by the three-month modified Rankin Score (mRS), and mortality rates. A sample of 220 AIS patients in the anterior circulation who underwent endovascular thrombectomy (EVT) was included, with 81 (37%) demonstrating good outcomes (mRS ≤ 2). The performance of the different algorithms evaluated was comparable, with the maximum validation under the curve (AUC) reaching 0.87 using graph convolutional networks (GCN) for mRS prediction and 0.86 using fully connected networks (FCN) for mortality prediction. Moderate performance was obtained at admission (AUC of 0.76 using GCN), which improved to 0.84 post-thrombectomy and to 0.89 a day after stroke. Reliable uncertainty prediction of the model could be demonstrated.
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Affiliation(s)
- Celia Martín Vicario
- Department of Neuroradiology, Friedrich-Alexander University of Erlangen-Nuremberg, University Hospital Erlangen, Erlangen, Germany.
- Pattern Recognition Lab, Friedrich Alexander University, Erlangen, Germany.
| | - Dalia Rodríguez Salas
- Department of Neuroradiology, Friedrich-Alexander University of Erlangen-Nuremberg, University Hospital Erlangen, Erlangen, Germany
- Pattern Recognition Lab, Friedrich Alexander University, Erlangen, Germany
| | - Andreas Maier
- Pattern Recognition Lab, Friedrich Alexander University, Erlangen, Germany
| | - Stefan Hock
- Department of Neuroradiology, Friedrich-Alexander University of Erlangen-Nuremberg, University Hospital Erlangen, Erlangen, Germany
| | - Joji Kuramatsu
- Department of Neurology, Friedrich-Alexander University of Erlangen-Nuremberg, University Hospital Erlangen, Erlangen, Germany
| | - Bernd Kallmuenzer
- Department of Neurology, Friedrich-Alexander University of Erlangen-Nuremberg, University Hospital Erlangen, Erlangen, Germany
| | | | | | | | - Stefan Schwab
- Department of Neurology, Friedrich-Alexander University of Erlangen-Nuremberg, University Hospital Erlangen, Erlangen, Germany
| | - Arnd Dörfler
- Department of Neuroradiology, Friedrich-Alexander University of Erlangen-Nuremberg, University Hospital Erlangen, Erlangen, Germany
| | - Iris Muehlen
- Department of Neuroradiology, Friedrich-Alexander University of Erlangen-Nuremberg, University Hospital Erlangen, Erlangen, Germany
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Hoffman H, Wood J, Cote JR, Jalal MS, Otite FO, Masoud HE, Gould GC. Development and Internal Validation of Machine Learning Models to Predict Mortality and Disability After Mechanical Thrombectomy for Acute Anterior Circulation Large Vessel Occlusion. World Neurosurg 2024; 182:e137-e154. [PMID: 38000670 DOI: 10.1016/j.wneu.2023.11.060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Revised: 11/14/2023] [Accepted: 11/15/2023] [Indexed: 11/26/2023]
Abstract
OBJECTIVE Mechanical thrombectomy (MT) improves outcomes in patients with LVO but many still experience mortality or severe disability. We sought to develop machine learning (ML) models that predict 90-day outcomes after MT for LVO. METHODS Consecutive patients who underwent MT for LVO between 2015-2021 at a Comprehensive Stroke Center were reviewed. Outcomes included 90-day favorable functional status (mRS 0-2), severe disability (mRS 4-6), and mortality. ML models were trained for each outcome using prethrombectomy data (pre) and with thrombectomy data (post). RESULTS Three hundred and fifty seven patients met the inclusion criteria. After model screening and hyperparameter tuning the top performing ML model for each outcome and timepoint was random forest (RF). Using only prethrombectomy features, the AUCs for the RFpre models were 0.73 (95% CI 0.62-0.85) for favorable functional status, 0.77 (95% CI 0.65-0.86) for severe disability, and 0.78 (95% CI 0.64-0.88) for mortality. All of these were better than a standard statistical model except for favorable functional status. Each RF model outperformed Pre, SPAN-100, THRIVE, and HIAT scores (P < 0.0001 for all). The most predictive features were premorbid mRS, age, and NIHSS. Incorporating MT data, the AUCs for the RFpost models were 0.80 (95% CI 0.67-0.90) for favorable functional status, 0.82 (95% CI 0.69-0.91) for severe disability, and 0.71 (95% CI 0.55-0.84) for mortality. CONCLUSIONS RF models accurately predicted 90-day outcomes after MT and performed better than standard statistical and clinical prediction models.
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Affiliation(s)
- Haydn Hoffman
- Department of Neurosurgery, State University of New York Upstate Medical University, Syracuse, New York, USA.
| | - Jacob Wood
- Department of Neurosurgery, State University of New York Upstate Medical University, Syracuse, New York, USA
| | - John R Cote
- Department of Neurosurgery, State University of New York Upstate Medical University, Syracuse, New York, USA
| | - Muhammad S Jalal
- Department of Neurosurgery, State University of New York Upstate Medical University, Syracuse, New York, USA
| | - Fadar O Otite
- Department of Neurology, State University of New York Upstate Medical University, Syracuse, New York, USA
| | - Hesham E Masoud
- Department of Neurology, State University of New York Upstate Medical University, Syracuse, New York, USA
| | - Grahame C Gould
- Department of Neurosurgery, State University of New York Upstate Medical University, Syracuse, New York, USA
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Kurniawan M, Mulya Saputri K, Mesiano T, Yunus RE, Permana AP, Sulistio S, Ginanjar E, Hidayat R, Rasyid A, Harris S. Efficacy of endovascular therapy for stroke in developing country: A single-centre retrospective observational study in Indonesia from 2017 to 2021. Heliyon 2024; 10:e23228. [PMID: 38192863 PMCID: PMC10772374 DOI: 10.1016/j.heliyon.2023.e23228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 11/27/2023] [Accepted: 11/29/2023] [Indexed: 01/10/2024] Open
Abstract
Background Indonesia as a developing nation faces a plethora of challenges in applying endovascular therapy (EVT), mostly due to the lack of physicians specialized in neuro-intervention, high operational cost, and time limitation. The efficacy of EVT in improving functional outcomes of stroke in developing countries has not been previously studied. Methods This retrospective cohort study was conducted at Dr. Cipto Mangunkusumo Hospital (Jakarta, Indonesia) from January 2017 to December 2021. Large vessel occlusion (LVO) diagnosis was established based on a combination of clinical and imaging characteristics. We assessed patients' functional independence on day-90 based on modified Rankin Scale (mRS) between the endovascular treatment group and the conservative group (those receiving intravascular thrombolysis or medical treatment only). Functional independence was defined as mRS ≤2. Results Among 111 stroke patients with LVO, we included 32 patients in the EVT group and 50 patients in the conservative group for this study. Patients with younger age (p = 0.004), lower hypertension rate (p < 0.001), higher intubation rate (p = 0.014), and earlier onset of stroke were observed in the EVT group. The proportion of mRS ≤2 at day-90 in the EVT group was higher than the conservative group (28.1 % vs. 18.0 %; p = 0.280). Patients within mRS ≤2 group had earlier onset-to-puncture time (p = 0.198), onset-to-recanalization time (p = 0.341), lower NIHSS (p = 0.026) and higher ASPECTS (p = 0.001) on admission. In multivariate analysis, ASPECTS (aOR 2.43; 95%CI 1.26-4.70; p = 0.008) defined functional independence in the EVT group. Conclusion The endovascular therapy group had a higher proportion of mRS ≤2 at day-90 than the conservative group despite its statistical insignificance.
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Affiliation(s)
- Mohammad Kurniawan
- Department of Neurology, Dr. Cipto Mangunkusumo Hospital, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia
| | - Kevin Mulya Saputri
- Department of Neurology, Dr. Cipto Mangunkusumo Hospital, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia
| | - Taufik Mesiano
- Department of Neurology, Dr. Cipto Mangunkusumo Hospital, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia
| | - Reyhan E. Yunus
- Department of Radiology, Dr. Cipto Mangunkusumo Hospital, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia
| | - Affan P. Permana
- Department of Neurosurgery, Dr. Cipto Mangunkusumo Hospital, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia
| | - Septo Sulistio
- Department of Emergency Medicine, Dr. Cipto Mangunkusumo Hospital, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia
| | - Eka Ginanjar
- Department of Internal Medicine, Dr. Cipto Mangunkusumo Hospital, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia
| | - Rakhmad Hidayat
- Department of Neurology, Dr. Cipto Mangunkusumo Hospital, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia
| | - Al Rasyid
- Department of Neurology, Dr. Cipto Mangunkusumo Hospital, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia
| | - Salim Harris
- Department of Neurology, Dr. Cipto Mangunkusumo Hospital, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia
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Chen M, Qian D, Wang Y, An J, Meng K, Xu S, Liu S, Sun M, Li M, Pang C. Systematic Review of Machine Learning Applied to the Secondary Prevention of Ischemic Stroke. J Med Syst 2024; 48:8. [PMID: 38165495 DOI: 10.1007/s10916-023-02020-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 11/13/2023] [Indexed: 01/03/2024]
Abstract
Ischemic stroke is a serious disease posing significant threats to human health and life, with the highest absolute and relative risks of a poor prognosis following the first occurrence, and more than 90% of strokes are attributable to modifiable risk factors. Currently, machine learning (ML) is widely used for the prediction of ischemic stroke outcomes. By identifying risk factors, predicting the risk of poor prognosis and thus developing personalized treatment plans, it effectively reduces the probability of poor prognosis, leading to more effective secondary prevention. This review includes 41 studies since 2018 that used ML algorithms to build prognostic prediction models for ischemic stroke, transient ischemic attack (TIA), and acute ischemic stroke (AIS). We analyzed in detail the risk factors used in these studies, the sources and processing methods of the required data, the model building and validation, and their application in different prediction time windows. The results indicate that among the included studies, the top five risk factors in terms of frequency were cardiovascular diseases, age, sex, national institutes of health stroke scale (NIHSS) score, and diabetes. Furthermore, 64% of the studies used single-center data, 65% of studies using imbalanced data did not perform data balancing, 88% of the studies did not utilize external validation datasets for model validation, and 72% of the studies did not provide explanations for their models. Addressing these issues is crucial for enhancing the credibility and effectiveness of the research, consequently improving the development and implementation of secondary prevention measures.
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Affiliation(s)
- Meng Chen
- School of Life Science and Technology, Changchun University of Science and Technology, Jilin Province, Changchun, 130022, People's Republic of China
| | - Dongbao Qian
- School of Life Science and Technology, Changchun University of Science and Technology, Jilin Province, Changchun, 130022, People's Republic of China
| | - Yixuan Wang
- Union Hospital of Jilin University, Jilin Province, Neurosurgery, Changchun, 130033, People's Republic of China
| | - Junyan An
- Union Hospital of Jilin University, Jilin Province, Neurosurgery, Changchun, 130033, People's Republic of China
| | - Ke Meng
- Union Hospital of Jilin University, Jilin Province, Neurosurgery, Changchun, 130033, People's Republic of China
| | - Shuai Xu
- School of Life Science and Technology, Changchun University of Science and Technology, Jilin Province, Changchun, 130022, People's Republic of China
| | - Sheng Liu
- School of Life Science and Technology, Changchun University of Science and Technology, Jilin Province, Changchun, 130022, People's Republic of China
| | - Meiyan Sun
- Union Hospital of Jilin University, Jilin Province, Neurosurgery, Changchun, 130033, People's Republic of China
| | - Miao Li
- School of Life Science and Technology, Changchun University of Science and Technology, Jilin Province, Changchun, 130022, People's Republic of China.
- Union Hospital of Jilin University, Jilin Province, Neurosurgery, Changchun, 130033, People's Republic of China.
| | - Chunying Pang
- School of Life Science and Technology, Changchun University of Science and Technology, Jilin Province, Changchun, 130022, People's Republic of China.
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Baek JH, Kim BM, Kim DJ, Heo JH, Nam HS, Kim YD, Rho MH, Chung PW, Won YS, Chung Y. Preprocedural determination of an occlusion pathomechanism in endovascular treatment of acute stroke: a machine learning-based decision. J Neurointerv Surg 2023; 15:e2-e8. [PMID: 35710314 DOI: 10.1136/neurintsurg-2022-018946] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 05/17/2022] [Indexed: 11/03/2022]
Abstract
OBJECTIVE To evaluate whether an occlusion pathomechanism can be accurately determined by common preprocedural findings through a machine learning-based prediction model (ML-PM). METHODS A total of 476 patients with acute stroke who underwent endovascular treatment were retrospectively included to derive an ML-PM. For external validation, 152 patients from another tertiary stroke center were additionally included. An ML algorithm was trained to classify an occlusion pathomechanism into embolic or intracranial atherosclerosis. Various common preprocedural findings were entered into the model. Model performance was evaluated based on accuracy and area under the receiver operating characteristic curve (AUC). For practical utility, a decision flowchart was devised from an ML-PM with a few key preprocedural findings. Accuracy of the decision flowchart was validated internally and externally. RESULTS An ML-PM could determine an occlusion pathomechanism with an accuracy of 96.9% (AUC=0.95). In the model, CT angiography-determined occlusion type, atrial fibrillation, hyperdense artery sign, and occlusion location were top-ranked contributors. With these four findings only, an ML-PM had an accuracy of 93.8% (AUC=0.92). With a decision flowchart, an occlusion pathomechanism could be determined with an accuracy of 91.2% for the study cohort and 94.7% for the external validation cohort. The decision flowchart was more accurate than single preprocedural findings for determining an occlusion pathomechanism. CONCLUSIONS An ML-PM could accurately determine an occlusion pathomechanism with common preprocedural findings. A decision flowchart consisting of the four most influential findings was clinically applicable and superior to single common preprocedural findings for determining an occlusion pathomechanism.
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Affiliation(s)
- Jang-Hyun Baek
- Department of Neurology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Neurology, Severance Stroke Center, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Byung Moon Kim
- Interventional Neuroradiology, Department of Radiology, Severance Stroke Center, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Dong Joon Kim
- Interventional Neuroradiology, Department of Radiology, Severance Stroke Center, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Ji Hoe Heo
- Department of Neurology, Severance Stroke Center, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Hyo Suk Nam
- Department of Neurology, Severance Stroke Center, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Young Dae Kim
- Department of Neurology, Severance Stroke Center, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Myung Ho Rho
- Department of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Pil-Wook Chung
- Department of Neurology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Yu Sam Won
- Department of Neurosurgery, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Yeongu Chung
- Department of Neurosurgery, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
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Li Q, Chi L, Zhao W, Wu L, Jiao C, Zheng X, Zhang K, Li X. Machine learning prediction of motor function in chronic stroke patients: a systematic review and meta-analysis. Front Neurol 2023; 14:1039794. [PMID: 37388543 PMCID: PMC10299899 DOI: 10.3389/fneur.2023.1039794] [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: 09/08/2022] [Accepted: 05/25/2023] [Indexed: 07/01/2023] Open
Abstract
Background Recent studies have reported that machine learning (ML), with a relatively strong capacity for processing non-linear data and adaptive ability, could improve the accuracy and efficiency of prediction. The article summarizes the published studies on ML models that predict motor function 3-6 months post-stroke. Methods A systematic literature search was conducted in PubMed, Embase, Cochorane and Web of Science as of April 3, 2023 for studies on ML prediction of motor function in stroke patients. The quality of the literature was assessed using the Prediction model Risk Of Bias Assessment Tool (PROBAST). A random-effects model was preferred for meta-analysis using R4.2.0 because of the different variables and parameters. Results A total of 44 studies were included in this meta-analysis, involving 72,368 patients and 136 models. Models were categorized into subgroups according to the predicted outcome Modified Rankin Scale cut-off value and whether they were constructed based on radiomics. C-statistics, sensitivity, and specificity were calculated. The random-effects model showed that the C-statistics of all models were 0.81 (95% CI: 0.79; 0.83) in the training set and 0.82 (95% CI: 0.80; 0.85) in the validation set. According to different Modified Rankin Scale cut-off values, C-statistics of ML models predicting Modified Rankin Scale>2(used most widely) in stroke patients were 0.81 (95% CI: 0.78; 0.84) in the training set, and 0.84 (95% CI: 0.81; 0.87) in the validation set. C-statistics of radiomics-based ML models in the training set and validation set were 0.81 (95% CI: 0.78; 0.84) and 0.87 (95% CI: 0.83; 0.90), respectively. Conclusion ML can be used as an assessment tool for predicting the motor function in patients with 3-6 months of post-stroke. Additionally, the study found that ML models with radiomics as a predictive variable were also demonstrated to have good predictive capabilities. This systematic review provides valuable guidance for the future optimization of ML prediction systems that predict poor motor outcomes in stroke patients. Systematic review registration https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022335260, identifier: CRD42022335260.
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Affiliation(s)
- Qinglin Li
- Second Clinical Medical School, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
| | - Lei Chi
- Department of Acupuncture, The Second Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
| | - Weiying Zhao
- Second Clinical Medical School, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
| | - Lei Wu
- Department of Acupuncture, The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Chuanxu Jiao
- Department of Neurorehabilitation, Taizhou Enze Medical Center Luqiao Hospital, Taizhou, Zhejiang, China
| | - Xue Zheng
- Second Clinical Medical School, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
| | - Kaiyue Zhang
- Second Clinical Medical School, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
| | - Xiaoning Li
- Department of Acupuncture, The Second Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
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Akay EMZ, Hilbert A, Carlisle BG, Madai VI, Mutke MA, Frey D. Artificial Intelligence for Clinical Decision Support in Acute Ischemic Stroke: A Systematic Review. Stroke 2023; 54:1505-1516. [PMID: 37216446 DOI: 10.1161/strokeaha.122.041442] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 02/21/2023] [Indexed: 05/24/2023]
Abstract
BACKGROUND Established randomized trial-based parameters for acute ischemic stroke group patients into generic treatment groups, leading to attempts using various artificial intelligence (AI) methods to directly correlate patient characteristics to outcomes and thereby provide decision support to stroke clinicians. We review AI-based clinical decision support systems in the development stage, specifically regarding methodological robustness and constraints for clinical implementation. METHODS Our systematic review included full-text English language publications proposing a clinical decision support system using AI techniques for direct decision support in acute ischemic stroke cases in adult patients. We (1) describe data and outcomes used in those systems, (2) estimate the systems' benefits compared with traditional stroke diagnosis and treatment, and (3) reported concordance with reporting standards for AI in healthcare. RESULTS One hundred twenty-one studies met our inclusion criteria. Sixty-five were included for full extraction. In our sample, utilized data sources, methods, and reporting practices were highly heterogeneous. CONCLUSIONS Our results suggest significant validity threats, dissonance in reporting practices, and challenges to clinical translation. We outline practical recommendations for the successful implementation of AI research in acute ischemic stroke treatment and diagnosis.
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Affiliation(s)
- Ela Marie Z Akay
- Charité Lab for Artificial Intelligence in Medicine (CLAIM) (E.M.Z.A., A.H., D.F.), Charité Universitätsmedizin Berlin, Germany
| | - Adam Hilbert
- Charité Lab for Artificial Intelligence in Medicine (CLAIM) (E.M.Z.A., A.H., D.F.), Charité Universitätsmedizin Berlin, Germany
| | - Benjamin G Carlisle
- QUEST Center for Responsible Research, Berlin Institute of Health (BIH) (B.G.C., V.I.M.), Charité Universitätsmedizin Berlin, Germany
| | - Vince I Madai
- QUEST Center for Responsible Research, Berlin Institute of Health (BIH) (B.G.C., V.I.M.), Charité Universitätsmedizin Berlin, Germany
- Faculty of Computing, Engineering and the Built Environment, School of Computing and Digital Technology, Birmingham City University, United Kingdom (V.I.M.)
| | - Matthias A Mutke
- Department of Neuroradiology, Heidelberg University Hospital, Germany (M.A.M.)
| | - Dietmar Frey
- Charité Lab for Artificial Intelligence in Medicine (CLAIM) (E.M.Z.A., A.H., D.F.), Charité Universitätsmedizin Berlin, Germany
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11
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Flint AC, Chan SL, Edwards NJ, Rao VA, Klingman JG, Nguyen-Huynh MN, Yan B, Mitchell PJ, Davis SM, Campbell BC, Dippel DW, Roos YB, van Zwam WH, Saver JL, Kidwell CS, Hill MD, Goyal M, Demchuk AM, Bracard S, Bendszus M, Donnan GA, On Behalf Of The Vista-Endovascular Collaboration. Outcome prediction in large vessel occlusion ischemic stroke with or without endovascular stroke treatment: THRIVE-EVT. Int J Stroke 2023; 18:331-337. [PMID: 35319310 DOI: 10.1177/17474930221092262] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
INTRODUCTION The THRIVE score and the THRIVE-c calculation are validated ischemic stroke outcome prediction tools based on patient variables that are readily available at initial presentation. Randomized controlled trials (RCTs) have demonstrated the benefit of endovascular treatment (EVT) for many patients with large vessel occlusion (LVO), and pooled data from these trials allow for adaptation of the THRIVE-c calculation for use in shared clinical decision making regarding EVT. METHODS To extend THRIVE-c for use in the context of EVT, we extracted data from the Virtual International Stroke Trials Archive (VISTA) from 7 RCTs of EVT. Models were built in a randomly selected development cohort using logistic regression that included the predictors from THRIVE-c: age, NIH Stroke Scale (NIHSS) score, presence of hypertension, diabetes mellitus, and/or atrial fibrillation, as well as randomization to EVT and, where available, the Alberta Stroke Program Early CT Score (ASPECTS). RESULTS Good outcome was achieved in 366/787 (46.5%) of subjects randomized to EVT and in 236/795 (29.7%) of subjects randomized to control (P < 0.001), and the improvement in outcome with EVT was seen across age, NIHSS, and THRIVE-c good outcome prediction. Models to predict outcome using THRIVE elements (age, NIHSS, and comorbidities) together with EVT, with or without ASPECTS, had similar performance by ROC analysis in the development and validation cohorts (THRIVE-EVT ROC area under the curve (AUC) = 0.716 in development, 0.727 in validation, P = 0.30; THRIVE-EVT + ASPECTS ROC AUC = 0.718 in development, 0.735 in validation, P = 0.12). CONCLUSION THRIVE-EVT may be used alongside the original THRIVE-c calculation to improve outcome probability estimation for patients with acute ischemic stroke, including patients with or without LVO, and to model the potential improvement in outcomes with EVT for an individual patient based on variables that are available at initial presentation. Online calculators for THRIVE-c estimation are available at www.thrivescore.org and www.mdcalc.com/thrive-score-for-stroke-outcome.
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Affiliation(s)
- Alexander C Flint
- Division of Research and Department of Neuroscience, Kaiser Permanente, Redwood City, CA, USA
| | - Sheila L Chan
- Division of Research and Department of Neuroscience, Kaiser Permanente, Redwood City, CA, USA
| | - Nancy J Edwards
- Division of Research and Department of Neuroscience, Kaiser Permanente, Redwood City, CA, USA
| | - Vivek A Rao
- Division of Research and Department of Neuroscience, Kaiser Permanente, Redwood City, CA, USA
| | | | | | - Bernard Yan
- Melbourne Brain Centre at Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC, Australia
| | - Peter J Mitchell
- Department of Radiology, The University of Melbourne, The Royal Melbourne Hospital, Parkville, VIC, Australia
| | - Stephen M Davis
- Melbourne Brain Centre at Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC, Australia
| | - Bruce Cv Campbell
- Melbourne Brain Centre at Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC, Australia
| | - Diederik W Dippel
- Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Yvo Bwem Roos
- Department of Neurology, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Wim H van Zwam
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Jeffrey L Saver
- Department of Neurology and Comprehensive Stroke Center, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Michael D Hill
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
| | - Mayank Goyal
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
| | - Andrew M Demchuk
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
| | - Serge Bracard
- Department of Neuroradiology, University of Lorraine, Nancy, France
| | - Martin Bendszus
- Department of Neuroradiology, University of Heidelberg, Heidelberg, Germany
| | - Geoffrey A Donnan
- Melbourne Brain Centre at Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC, Australia
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12
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Castonguay AC, Zoghi Z, Zaidat OO, Burgess RE, Zaidi SF, Mueller-Kronast N, Liebeskind DS, Jumaa MA. Predicting Functional Outcome Using 24-Hour Post-Treatment Characteristics: Application of Machine Learning Algorithms in the STRATIS Registry. Ann Neurol 2023; 93:40-49. [PMID: 36214566 PMCID: PMC10091739 DOI: 10.1002/ana.26528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 09/27/2022] [Accepted: 10/05/2022] [Indexed: 02/05/2023]
Abstract
FOR SOCIAL MEDIA: @AliciaCastongu2, @FazalZaidi9, @oozaidat, @Mouhammad_Jumaa OBJECTIVE: Machine learning (ML) algorithms have emerged as powerful predictive tools in the field stroke. Here, we examine the predictive accuracy of ML models for predicting functional outcomes using 24-hour post-treatment characteristics in the Systematic Evaluation of Patients Treated With Neurothrombectomy Devices for Acute Ischemic Stroke (STRATIS) Registry. METHODS ML models, adaptive boost, random forest (RF), classification and regression trees (CART), C5.0 decision tree (C5.0), support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), and logistic regression (LR), and traditional LR models were used to predict 90-day functional outcome (modified Rankin Scale score 0-2). Twenty-four-hour National Institutes of Health Stroke Scale (NIHSS) was examined as a continuous or dichotomous variable in all models. Model accuracy was assessed using the area under characteristic curve (AUC). RESULTS The 24-hour NIHSS score was a top-predictor of functional outcome in all models. ML models using the continuous 24-hour NIHSS scored showed moderate-to-good predictive performance (range mean AUC: 0.76-0.92); however, RF (AUC: 0.92 ± 0.028) outperformed all ML models, except LASSO (AUC: 0.89 ± 0.023, p = 0.0958). Importantly, RF demonstrated a significantly higher predictive value than LR (AUC: 0.87 ± 0.031, p = 0.048) and traditional LR (AUC: 85 ± 0.06, p = 0.035) when using the 24-hour continuous NIHSS score. Predictive accuracy was similar between the 24-hour NIHSS score dichotomous and continuous ML models. INTERPRETATION In this substudy, we found similar predictive accuracy for functional outcome when using the 24-hour NIHSS score as a continuous or dichotomous variable in ML models. ML models had moderate-to-good predictive accuracy, with RF outperforming LR models. External validation of these ML models is warranted. ANN NEUROL 2023;93:40-49.
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Affiliation(s)
| | - Zeinab Zoghi
- ProMedica Stroke Network, ProMedica Toledo Hospital, Toledo, OH
| | | | | | - Syed F Zaidi
- Department of Neurology, University of Toledo, Toledo, OH.,ProMedica Stroke Network, ProMedica Toledo Hospital, Toledo, OH
| | | | - David S Liebeskind
- Department of Neurology, University of California Los Angeles, Los Angeles, CA
| | - Mouhammad A Jumaa
- Department of Neurology, University of Toledo, Toledo, OH.,ProMedica Stroke Network, ProMedica Toledo Hospital, Toledo, OH
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13
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Junttola U, Lahtinen S, Isokangas JM, Hietanen S, Vakkala M, Kaakinen T, Liisanantti J. Long-term mortality after endovascular thrombectomy for stroke. J Stroke Cerebrovasc Dis 2022; 31:106832. [DOI: 10.1016/j.jstrokecerebrovasdis.2022.106832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 09/28/2022] [Accepted: 10/10/2022] [Indexed: 11/06/2022] Open
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Feyen L, Blockhaus C, Katoh M, Haage P, Schaub C, Rohde S. Machine learning based outcome prediction of large vessel occlusion of the anterior circulation prior to thrombectomy in patients with wake-up stroke. Interv Neuroradiol 2022:15910199221135695. [PMID: 36344011 DOI: 10.1177/15910199221135695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2024] Open
Abstract
PURPOSE Outcome prediction of large vessel occlusion of the anterior circulation in patients with wake-up stroke is important to identify patients that will benefit from thrombectomy. Currently, mismatch concepts that require MRI or CT-Perfusion (CTP) are recommended to identify these patients. We evaluated machine learning algorithms in their ability to discriminate between patients with favorable (defined as a modified Rankin Scale (mRS) score of 0-2) and unfavorable (mRS 3-6) outcome and between patients with poor (mRS5-6) and non-poor (mRS 0-4) outcome. METHODS Data of 8395 patients that were treated between 2018 and 2020 from the nationwide registry of the German Society for Neuroradiology was retrospectively analyzed. Five models were trained with clinical variables and Alberta Stroke Program Early CT Score (ASPECTS). The model with the highest accuracy was validated with a test dataset with known stroke onset and with a test dataset that consisted only of wake-up strokes. RESULTS 2419 patients showed poor and 3310 patients showed favorable outcome. The best performing Random Forest model achieved a sensitivity of 0.65, a specificity of 0.81 and an AUC of 0.79 on the test dataset of patients with wake-up stroke in the classification analysis between favorable and unfavorable outcome and a sensitivity of 0.42, a specificity of 0.83 and an AUC of 0.72 in the classification analysis between poor and non-poor outcome. CONCLUSION Machine learning algorithms have the potential to aid in the decision making for thrombectomy in patients with wake-up stroke especially in hospitals, where emergency CTP or MRI imaging is not available.
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Affiliation(s)
- Ludger Feyen
- Department of Diagnostic and Interventional Radiology, 27664Helios Klinikum Krefeld, Krefeld, Germany
- Faculty of Health, School of Medicine, University Witten/Herdecke, Witten, Germany
- Department of Diagnostic and Interventional Radiology, 60865HELIOS University Hospital Wuppertal, Wuppertal, Germany
| | - Christian Blockhaus
- Faculty of Health, School of Medicine, University Witten/Herdecke, Witten, Germany
- Heart Centre Niederrhein, Department of Cardiology, Helios Clinic Krefeld, Krefeld, Germany
| | - Marcus Katoh
- Department of Diagnostic and Interventional Radiology, 27664Helios Klinikum Krefeld, Krefeld, Germany
| | - Patrick Haage
- Faculty of Health, School of Medicine, University Witten/Herdecke, Witten, Germany
- Department of Diagnostic and Interventional Radiology, 60865HELIOS University Hospital Wuppertal, Wuppertal, Germany
| | - Christina Schaub
- Klinik und Poliklinik für Neurologie, 39062University Hospital Bonn, Bonn, Germany
| | - Stefan Rohde
- Faculty of Health, School of Medicine, University Witten/Herdecke, Witten, Germany
- Department of Radiology and Neuroradiology, Klinikum Dortmund, Dortmund, Germany
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15
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Zeng M, Oakden-Rayner L, Bird A, Smith L, Wu Z, Scroop R, Kleinig T, Jannes J, Jenkinson M, Palmer LJ. Pre-thrombectomy prognostic prediction of large-vessel ischemic stroke using machine learning: A systematic review and meta-analysis. Front Neurol 2022; 13:945813. [PMID: 36158960 PMCID: PMC9495610 DOI: 10.3389/fneur.2022.945813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 08/18/2022] [Indexed: 11/20/2022] Open
Abstract
Introduction Machine learning (ML) methods are being increasingly applied to prognostic prediction for stroke patients with large vessel occlusion (LVO) treated with endovascular thrombectomy. This systematic review aims to summarize ML-based pre-thrombectomy prognostic models for LVO stroke and identify key research gaps. Methods Literature searches were performed in Embase, PubMed, Web of Science, and Scopus. Meta-analyses of the area under the receiver operating characteristic curves (AUCs) of ML models were conducted to synthesize model performance. Results Sixteen studies describing 19 models were eligible. The predicted outcomes include functional outcome at 90 days, successful reperfusion, and hemorrhagic transformation. Functional outcome was analyzed by 10 conventional ML models (pooled AUC=0.81, 95% confidence interval [CI]: 0.77–0.85, AUC range: 0.68–0.93) and four deep learning (DL) models (pooled AUC=0.75, 95% CI: 0.70–0.81, AUC range: 0.71–0.81). Successful reperfusion was analyzed by three conventional ML models (pooled AUC=0.72, 95% CI: 0.56–0.88, AUC range: 0.55–0.88) and one DL model (AUC=0.65, 95% CI: 0.62–0.68). Conclusions Conventional ML and DL models have shown variable performance in predicting post-treatment outcomes of LVO without generally demonstrating superiority compared to existing prognostic scores. Most models were developed using small datasets, lacked solid external validation, and at high risk of potential bias. There is considerable scope to improve study design and model performance. The application of ML and DL methods to improve the prediction of prognosis in LVO stroke, while promising, remains nascent. Systematic review registration https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021266524, identifier CRD42021266524
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Affiliation(s)
- Minyan Zeng
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia
- School of Public Health, University of Adelaide, Adelaide, SA, Australia
- *Correspondence: Minyan Zeng
| | - Lauren Oakden-Rayner
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia
- School of Public Health, University of Adelaide, Adelaide, SA, Australia
- Department of Radiology, Royal Adelaide Hospital, Adelaide, SA, Australia
| | - Alix Bird
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia
- School of Public Health, University of Adelaide, Adelaide, SA, Australia
| | - Luke Smith
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia
- School of Public Health, University of Adelaide, Adelaide, SA, Australia
| | - Zimu Wu
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Rebecca Scroop
- Department of Radiology, Royal Adelaide Hospital, Adelaide, SA, Australia
- Faculty Health and Medical Science, School of Medicine, University of Adelaide, Adelaide, SA, Australia
| | - Timothy Kleinig
- Faculty Health and Medical Science, School of Medicine, University of Adelaide, Adelaide, SA, Australia
- Department of Neurology, Royal Adelaide Hospital, Adelaide, SA, Australia
| | - Jim Jannes
- Faculty Health and Medical Science, School of Medicine, University of Adelaide, Adelaide, SA, Australia
- Department of Neurology, Royal Adelaide Hospital, Adelaide, SA, Australia
| | - Mark Jenkinson
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia
- Functional Magnetic Resonance Imaging of the Brain Centre, University of Oxford, Oxford, United Kingdom
| | - Lyle J. Palmer
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia
- School of Public Health, University of Adelaide, Adelaide, SA, Australia
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Meinel TR, Lerch C, Fischer U, Beyeler M, Mujanovic A, Kurmann C, Siepen B, Scutelnic A, Müller M, Goeldlin M, Belachew NF, Dobrocky T, Gralla J, Seiffge D, Jung S, Arnold M, Wiest R, Meier R, Kaesmacher J. Multivariable Prediction Model for Futile Recanalization Therapies in Patients With Acute Ischemic Stroke. Neurology 2022; 99:e1009-e1018. [PMID: 35803722 PMCID: PMC9519255 DOI: 10.1212/wnl.0000000000200815] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 04/19/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Very poor outcome despite IV thrombolysis (IVT) and mechanical thrombectomy (MT) occurs in approximately 1 of 4 patients with ischemic stroke and is associated with a high logistic and economic burden. We aimed to develop and validate a multivariable prognostic model to identify futile recanalization therapies (FRTs) in patients undergoing those therapies. METHODS Patients from a prospectively collected observational registry of a single academic stroke center treated with MT and/or IVT were included. The data set was split into a training (N = 1,808, 80%) and internal validation (N = 453, 20%) cohort. We used gradient boosted decision tree machine learning models after k-nearest neighbor imputation of 32 variables available at admission to predict FRT defined as modified Rankin scale 5-6 at 3 months. We report feature importance, ability for discrimination, calibration, and decision curve analysis. RESULTS A total of 2,261 patients with a median (interquartile range) age of 75 years (64-83 years), 46% female, median NIH Stroke Scale 9 (4-17), 34% IVT alone, 41% MT alone, and 25% bridging were included. Overall, 539 (24%) had FRT, more often in MT alone (34%) as compared with IVT alone (11%). Feature importance identified clinical variables (stroke severity, age, active cancer, prestroke disability), laboratory values (glucose, C-reactive protein, creatinine), imaging biomarkers (white matter hyperintensities), and onset-to-admission time as the most important predictors. The final model was discriminatory for predicting 3-month FRT (area under the curve 0.87, 95% CI 0.87-0.88) and had good calibration (Brier 0.12, 0.11-0.12). Overall performance was moderate (F1-score 0.63 ± 0.004), and decision curve analyses suggested higher mean net benefit at lower thresholds of treatment (up to 0.8). CONCLUSIONS This FRT prediction model can help inform shared decision making and identify the most relevant features in the emergency setting. Although it might be particularly useful in low resource healthcare settings, incorporation of further multifaceted variables is necessary to further increase the predictive performance.
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Affiliation(s)
- Thomas Raphael Meinel
- From the Department of Neurology (T.M., C.L., M.B., B.S., A.S., M.M., M.G., D.S., S.J., M.A.), University Hospital Bern, Inselspital, University of Bern; Department of Neurology and Stroke Center (U.F.), University Hospital Basel and University of Basel; University Institute of Diagnostic and Interventional Neuroradiology (A.M., C.K., N.F.B., T.D., J.G., R.W., R.M., J.K.), Support Center for Advanced Neuroimaging (R.W., R.M., J.K.), and Department of Diagnostic, Paediatric and Interventional Radiology (J.K.), University Hospital Bern, Inselspital, University of Bern, Switzerland.
| | - Christine Lerch
- From the Department of Neurology (T.M., C.L., M.B., B.S., A.S., M.M., M.G., D.S., S.J., M.A.), University Hospital Bern, Inselspital, University of Bern; Department of Neurology and Stroke Center (U.F.), University Hospital Basel and University of Basel; University Institute of Diagnostic and Interventional Neuroradiology (A.M., C.K., N.F.B., T.D., J.G., R.W., R.M., J.K.), Support Center for Advanced Neuroimaging (R.W., R.M., J.K.), and Department of Diagnostic, Paediatric and Interventional Radiology (J.K.), University Hospital Bern, Inselspital, University of Bern, Switzerland
| | - Urs Fischer
- From the Department of Neurology (T.M., C.L., M.B., B.S., A.S., M.M., M.G., D.S., S.J., M.A.), University Hospital Bern, Inselspital, University of Bern; Department of Neurology and Stroke Center (U.F.), University Hospital Basel and University of Basel; University Institute of Diagnostic and Interventional Neuroradiology (A.M., C.K., N.F.B., T.D., J.G., R.W., R.M., J.K.), Support Center for Advanced Neuroimaging (R.W., R.M., J.K.), and Department of Diagnostic, Paediatric and Interventional Radiology (J.K.), University Hospital Bern, Inselspital, University of Bern, Switzerland
| | - Morin Beyeler
- From the Department of Neurology (T.M., C.L., M.B., B.S., A.S., M.M., M.G., D.S., S.J., M.A.), University Hospital Bern, Inselspital, University of Bern; Department of Neurology and Stroke Center (U.F.), University Hospital Basel and University of Basel; University Institute of Diagnostic and Interventional Neuroradiology (A.M., C.K., N.F.B., T.D., J.G., R.W., R.M., J.K.), Support Center for Advanced Neuroimaging (R.W., R.M., J.K.), and Department of Diagnostic, Paediatric and Interventional Radiology (J.K.), University Hospital Bern, Inselspital, University of Bern, Switzerland
| | - Adnan Mujanovic
- From the Department of Neurology (T.M., C.L., M.B., B.S., A.S., M.M., M.G., D.S., S.J., M.A.), University Hospital Bern, Inselspital, University of Bern; Department of Neurology and Stroke Center (U.F.), University Hospital Basel and University of Basel; University Institute of Diagnostic and Interventional Neuroradiology (A.M., C.K., N.F.B., T.D., J.G., R.W., R.M., J.K.), Support Center for Advanced Neuroimaging (R.W., R.M., J.K.), and Department of Diagnostic, Paediatric and Interventional Radiology (J.K.), University Hospital Bern, Inselspital, University of Bern, Switzerland
| | - Christoph Kurmann
- From the Department of Neurology (T.M., C.L., M.B., B.S., A.S., M.M., M.G., D.S., S.J., M.A.), University Hospital Bern, Inselspital, University of Bern; Department of Neurology and Stroke Center (U.F.), University Hospital Basel and University of Basel; University Institute of Diagnostic and Interventional Neuroradiology (A.M., C.K., N.F.B., T.D., J.G., R.W., R.M., J.K.), Support Center for Advanced Neuroimaging (R.W., R.M., J.K.), and Department of Diagnostic, Paediatric and Interventional Radiology (J.K.), University Hospital Bern, Inselspital, University of Bern, Switzerland
| | - Bernhard Siepen
- From the Department of Neurology (T.M., C.L., M.B., B.S., A.S., M.M., M.G., D.S., S.J., M.A.), University Hospital Bern, Inselspital, University of Bern; Department of Neurology and Stroke Center (U.F.), University Hospital Basel and University of Basel; University Institute of Diagnostic and Interventional Neuroradiology (A.M., C.K., N.F.B., T.D., J.G., R.W., R.M., J.K.), Support Center for Advanced Neuroimaging (R.W., R.M., J.K.), and Department of Diagnostic, Paediatric and Interventional Radiology (J.K.), University Hospital Bern, Inselspital, University of Bern, Switzerland
| | - Adrian Scutelnic
- From the Department of Neurology (T.M., C.L., M.B., B.S., A.S., M.M., M.G., D.S., S.J., M.A.), University Hospital Bern, Inselspital, University of Bern; Department of Neurology and Stroke Center (U.F.), University Hospital Basel and University of Basel; University Institute of Diagnostic and Interventional Neuroradiology (A.M., C.K., N.F.B., T.D., J.G., R.W., R.M., J.K.), Support Center for Advanced Neuroimaging (R.W., R.M., J.K.), and Department of Diagnostic, Paediatric and Interventional Radiology (J.K.), University Hospital Bern, Inselspital, University of Bern, Switzerland
| | - Madlaine Müller
- From the Department of Neurology (T.M., C.L., M.B., B.S., A.S., M.M., M.G., D.S., S.J., M.A.), University Hospital Bern, Inselspital, University of Bern; Department of Neurology and Stroke Center (U.F.), University Hospital Basel and University of Basel; University Institute of Diagnostic and Interventional Neuroradiology (A.M., C.K., N.F.B., T.D., J.G., R.W., R.M., J.K.), Support Center for Advanced Neuroimaging (R.W., R.M., J.K.), and Department of Diagnostic, Paediatric and Interventional Radiology (J.K.), University Hospital Bern, Inselspital, University of Bern, Switzerland
| | - Martina Goeldlin
- From the Department of Neurology (T.M., C.L., M.B., B.S., A.S., M.M., M.G., D.S., S.J., M.A.), University Hospital Bern, Inselspital, University of Bern; Department of Neurology and Stroke Center (U.F.), University Hospital Basel and University of Basel; University Institute of Diagnostic and Interventional Neuroradiology (A.M., C.K., N.F.B., T.D., J.G., R.W., R.M., J.K.), Support Center for Advanced Neuroimaging (R.W., R.M., J.K.), and Department of Diagnostic, Paediatric and Interventional Radiology (J.K.), University Hospital Bern, Inselspital, University of Bern, Switzerland
| | - Nebiyat Filate Belachew
- From the Department of Neurology (T.M., C.L., M.B., B.S., A.S., M.M., M.G., D.S., S.J., M.A.), University Hospital Bern, Inselspital, University of Bern; Department of Neurology and Stroke Center (U.F.), University Hospital Basel and University of Basel; University Institute of Diagnostic and Interventional Neuroradiology (A.M., C.K., N.F.B., T.D., J.G., R.W., R.M., J.K.), Support Center for Advanced Neuroimaging (R.W., R.M., J.K.), and Department of Diagnostic, Paediatric and Interventional Radiology (J.K.), University Hospital Bern, Inselspital, University of Bern, Switzerland
| | - Tomas Dobrocky
- From the Department of Neurology (T.M., C.L., M.B., B.S., A.S., M.M., M.G., D.S., S.J., M.A.), University Hospital Bern, Inselspital, University of Bern; Department of Neurology and Stroke Center (U.F.), University Hospital Basel and University of Basel; University Institute of Diagnostic and Interventional Neuroradiology (A.M., C.K., N.F.B., T.D., J.G., R.W., R.M., J.K.), Support Center for Advanced Neuroimaging (R.W., R.M., J.K.), and Department of Diagnostic, Paediatric and Interventional Radiology (J.K.), University Hospital Bern, Inselspital, University of Bern, Switzerland
| | - Jan Gralla
- From the Department of Neurology (T.M., C.L., M.B., B.S., A.S., M.M., M.G., D.S., S.J., M.A.), University Hospital Bern, Inselspital, University of Bern; Department of Neurology and Stroke Center (U.F.), University Hospital Basel and University of Basel; University Institute of Diagnostic and Interventional Neuroradiology (A.M., C.K., N.F.B., T.D., J.G., R.W., R.M., J.K.), Support Center for Advanced Neuroimaging (R.W., R.M., J.K.), and Department of Diagnostic, Paediatric and Interventional Radiology (J.K.), University Hospital Bern, Inselspital, University of Bern, Switzerland
| | - David Seiffge
- From the Department of Neurology (T.M., C.L., M.B., B.S., A.S., M.M., M.G., D.S., S.J., M.A.), University Hospital Bern, Inselspital, University of Bern; Department of Neurology and Stroke Center (U.F.), University Hospital Basel and University of Basel; University Institute of Diagnostic and Interventional Neuroradiology (A.M., C.K., N.F.B., T.D., J.G., R.W., R.M., J.K.), Support Center for Advanced Neuroimaging (R.W., R.M., J.K.), and Department of Diagnostic, Paediatric and Interventional Radiology (J.K.), University Hospital Bern, Inselspital, University of Bern, Switzerland
| | - Simon Jung
- From the Department of Neurology (T.M., C.L., M.B., B.S., A.S., M.M., M.G., D.S., S.J., M.A.), University Hospital Bern, Inselspital, University of Bern; Department of Neurology and Stroke Center (U.F.), University Hospital Basel and University of Basel; University Institute of Diagnostic and Interventional Neuroradiology (A.M., C.K., N.F.B., T.D., J.G., R.W., R.M., J.K.), Support Center for Advanced Neuroimaging (R.W., R.M., J.K.), and Department of Diagnostic, Paediatric and Interventional Radiology (J.K.), University Hospital Bern, Inselspital, University of Bern, Switzerland
| | - Marcel Arnold
- From the Department of Neurology (T.M., C.L., M.B., B.S., A.S., M.M., M.G., D.S., S.J., M.A.), University Hospital Bern, Inselspital, University of Bern; Department of Neurology and Stroke Center (U.F.), University Hospital Basel and University of Basel; University Institute of Diagnostic and Interventional Neuroradiology (A.M., C.K., N.F.B., T.D., J.G., R.W., R.M., J.K.), Support Center for Advanced Neuroimaging (R.W., R.M., J.K.), and Department of Diagnostic, Paediatric and Interventional Radiology (J.K.), University Hospital Bern, Inselspital, University of Bern, Switzerland
| | - Roland Wiest
- From the Department of Neurology (T.M., C.L., M.B., B.S., A.S., M.M., M.G., D.S., S.J., M.A.), University Hospital Bern, Inselspital, University of Bern; Department of Neurology and Stroke Center (U.F.), University Hospital Basel and University of Basel; University Institute of Diagnostic and Interventional Neuroradiology (A.M., C.K., N.F.B., T.D., J.G., R.W., R.M., J.K.), Support Center for Advanced Neuroimaging (R.W., R.M., J.K.), and Department of Diagnostic, Paediatric and Interventional Radiology (J.K.), University Hospital Bern, Inselspital, University of Bern, Switzerland
| | - Raphael Meier
- From the Department of Neurology (T.M., C.L., M.B., B.S., A.S., M.M., M.G., D.S., S.J., M.A.), University Hospital Bern, Inselspital, University of Bern; Department of Neurology and Stroke Center (U.F.), University Hospital Basel and University of Basel; University Institute of Diagnostic and Interventional Neuroradiology (A.M., C.K., N.F.B., T.D., J.G., R.W., R.M., J.K.), Support Center for Advanced Neuroimaging (R.W., R.M., J.K.), and Department of Diagnostic, Paediatric and Interventional Radiology (J.K.), University Hospital Bern, Inselspital, University of Bern, Switzerland
| | - Johannes Kaesmacher
- From the Department of Neurology (T.M., C.L., M.B., B.S., A.S., M.M., M.G., D.S., S.J., M.A.), University Hospital Bern, Inselspital, University of Bern; Department of Neurology and Stroke Center (U.F.), University Hospital Basel and University of Basel; University Institute of Diagnostic and Interventional Neuroradiology (A.M., C.K., N.F.B., T.D., J.G., R.W., R.M., J.K.), Support Center for Advanced Neuroimaging (R.W., R.M., J.K.), and Department of Diagnostic, Paediatric and Interventional Radiology (J.K.), University Hospital Bern, Inselspital, University of Bern, Switzerland
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Hatami N, Cho TH, Mechtouff L, Eker OF, Rousseau D, Frindel C. CNN-LSTM Based Multimodal MRI and Clinical Data Fusion for Predicting Functional Outcome in Stroke Patients. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:3430-3434. [PMID: 36085793 DOI: 10.1109/embc48229.2022.9871735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Clinical outcome prediction plays an important role in stroke patient management. From a machine learning point-of-view, one of the main challenges is dealing with heterogeneous data at patient admission, i.e. the image data which are multidimensional and the clinical data which are scalars. In this paper, a multimodal convolutional neural network - long short-term memory (CNN-LSTM) based ensemble model is proposed. For each MR image module, a dedicated network provides preliminary prediction of the clinical outcome using the modified Rankin scale (mRS). The final mRS score is obtained by merging the preliminary probabilities of each module dedicated to a specific type of MR image weighted by the clinical metadata, here age or the National Institutes of Health Stroke Scale (NIHSS). The experimental results demonstrate that the proposed model surpasses the baselines and offers an original way to automatically encode the spatio-temporal context of MR images in a deep learning architecture. The highest AUC (0.77) was achieved for the proposed model with NIHSS. Clinical Relevance- - We present the first deep learning approach predicting the clinical outcome of stroke patients treated by mechanical thrombectomy which integrates imaging data at the voxel level with key clinical metadata. Combining clinical and imaging data to evaluate the potential benefit from therapy closely mirrors the clinical decision process. Our promising results suggest our predictive model could assist in acute stroke management.
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Use of Machine Learning Algorithms to Predict the Outcomes of Mechanical Thrombectomy in Acute Ischemic Stroke Patients With an Extended Therapeutic Time Window. J Comput Assist Tomogr 2022; 46:775-780. [PMID: 35675699 DOI: 10.1097/rct.0000000000001341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVE The aim of this study was to evaluate the performance of machine learning (ML) algorithms in predicting the functional outcome of mechanical thrombectomy (MT) outside the 6-hour therapeutic time window in patients with acute ischemic stroke (AIS). METHODS One hundred seventy-seven consecutive AIS patients with large-vessel occlusion in the anterior circulation who underwent MT in the extended time window were enrolled. Clinical, neuroimaging, and treatment variables that could be obtained quickly in the real-world emergency settings were collected. Four machine learning algorithms (random forests, regularized logistic regression, support vector machine, and naive Bayes) were used to predict good outcomes (modified Rankin Scale scores of 0-2) at 90 days by using (1) only variables at admission and (2) both baseline and treatment variables. The performance of each model was evaluated using receiver operating characteristic (ROC) curve analysis. Feature importance was ranked using random forest algorithms. RESULTS Eighty patients (45.2%) had a favorable 90-day outcome. Machine learning models including baseline clinical and neuroimaging characteristics predicted 90-day modified Rankin Scale with an area under the ROC curve of 0.80-0.81, sensitivity of 0.60-0.71 and specificity of 0.71-0.76. Further inclusion the treatment variables significantly improved the predictive performance (mean area under the ROC curve, 0.89-0.90; sensitivity, 0.77-0.85; specificity, 0.75-0.87). The most important characteristics for predicting 90-day outcomes were age, hypoperfusion intensity ratio at admission, and National Institutes of Health Stroke Scale score at 24 hours after MT. CONCLUSIONS Machine learning algorithms may facilitate prediction of 90-day functional outcomes in AIS patients with an extended therapeutic time window.
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Mutke MA, Madai VI, Hilbert A, Zihni E, Potreck A, Weyland CS, Möhlenbruch MA, Heiland S, Ringleb PA, Nagel S, Bendszus M, Frey D. Comparing Poor and Favorable Outcome Prediction With Machine Learning After Mechanical Thrombectomy in Acute Ischemic Stroke. Front Neurol 2022; 13:737667. [PMID: 35693017 PMCID: PMC9184444 DOI: 10.3389/fneur.2022.737667] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 03/28/2022] [Indexed: 11/13/2022] Open
Abstract
Background and PurposeOutcome prediction after mechanical thrombectomy (MT) in patients with acute ischemic stroke (AIS) and large vessel occlusion (LVO) is commonly performed by focusing on favorable outcome (modified Rankin Scale, mRS 0–2) after 3 months but poor outcome representing severe disability and mortality (mRS 5 and 6) might be of equal importance for clinical decision-making.MethodsWe retrospectively analyzed patients with AIS and LVO undergoing MT from 2009 to 2018. Prognostic variables were grouped in baseline clinical (A), MRI-derived variables including mismatch [apparent diffusion coefficient (ADC) and time-to-maximum (Tmax) lesion volume] (B), and variables reflecting speed and extent of reperfusion (C) [modified treatment in cerebral ischemia (mTICI) score and time from onset to mTICI]. Three different scenarios were analyzed: (1) baseline clinical parameters only, (2) baseline clinical and MRI-derived parameters, and (3) all baseline clinical, imaging-derived, and reperfusion-associated parameters. For each scenario, we assessed prediction for favorable and poor outcome with seven different machine learning algorithms.ResultsIn 210 patients, prediction of favorable outcome was improved after including speed and extent of recanalization [highest area under the curve (AUC) 0.73] compared to using baseline clinical variables only (highest AUC 0.67). Prediction of poor outcome remained stable by using baseline clinical variables only (highest AUC 0.71) and did not improve further by additional variables. Prediction of favorable and poor outcomes was not improved by adding MR-mismatch variables. Most important baseline clinical variables for both outcomes were age, National Institutes of Health Stroke Scale, and premorbid mRS.ConclusionsOur results suggest that a prediction of poor outcome after AIS and MT could be made based on clinical baseline variables only. Speed and extent of MT did improve prediction for a favorable outcome but is not relevant for poor outcome. An MR mismatch with small ischemic core and larger penumbral tissue showed no predictive importance.
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Affiliation(s)
- Matthias A. Mutke
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- *Correspondence: Matthias A. Mutke
| | - Vince I. Madai
- Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
- QUEST (Quality, Ethics, Open Science, Translation) Center for Responsible Research at Berlin Institute of Health, Charité Universitätsmedizin Berlin, Berlin, Germany
- School of Computing and Digital Technology, Faculty of Computing, Engineering and the Built Environment, Birmingham City University, Birmingham, United Kingdom
| | - Adam Hilbert
- Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Esra Zihni
- Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
- School of Computing, Technological University Dublin, Dublin, Ireland
| | - Arne Potreck
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Charlotte S. Weyland
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Sabine Heiland
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Peter A. Ringleb
- Department of Neurology, Heidelberg University Hospital, Heidelberg, Germany
| | - Simon Nagel
- Department of Neurology, Heidelberg University Hospital, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Dietmar Frey
- Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
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Qu S, Zhou M, Jiao S, Zhang Z, Xue K, Long J, Zha F, Chen Y, Li J, Yang Q, Wang Y. Optimizing acute stroke outcome prediction models: Comparison of generalized regression neural networks and logistic regressions. PLoS One 2022; 17:e0267747. [PMID: 35544482 PMCID: PMC9094516 DOI: 10.1371/journal.pone.0267747] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Accepted: 04/16/2022] [Indexed: 11/18/2022] Open
Abstract
Background Generalized regression neural network (GRNN) and logistic regression (LR) are extensively used in the medical field; however, the better model for predicting stroke outcome has not been established. The primary goal of this study was to compare the accuracies of GRNN and LR models to identify the most optimal model for the prediction of acute stroke outcome, as well as explore useful biomarkers for predicting the prognosis of acute stroke patients. Method In a single-center study, 216 (80% for the training set and 20% for the test set) acute stroke patients admitted to the Shenzhen Second People’s Hospital between December 2019 to June 2021 were retrospectively recruited. The functional outcomes of the patients were measured using Barthel Index (BI) on discharge. A training set was used to optimize the GRNN and LR models. The test set was utilized to validate and compare the performances of GRNN and LR in predicting acute stroke outcome based on the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, and the Kappa value. Result The LR analysis showed that age, the National Institute Health Stroke Scale score, BI index, hemoglobin, and albumin were independently associated with stroke outcome. After validating in test set using these variables, we found that the GRNN model showed a better performance based on AUROC (0.931 vs 0.702), sensitivity (0.933 vs 0.700), specificity (0.889 vs 0.722), accuracy (0.896 vs 0.729), and the Kappa value (0.775 vs 0.416) than the LR model. Conclusion Overall, the GRNN model demonstrated superior performance to the LR model in predicting the prognosis of acute stroke patients. In addition to its advantage in not affected by implicit interactions and complex relationship in the data. Thus, we suggested that GRNN could be served as the optimal statistical model for acute stroke outcome prediction. Simultaneously, prospective validation based on more variables of the GRNN model for the prediction is required in future studies.
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Affiliation(s)
- Sheng Qu
- Department of Rehabilitation, Shenzhen Second People’s Hospital, The First Affiliated Hospital of Shenzhen University Health Science Centre, Shenzhen, China
| | - Mingchao Zhou
- Department of Rehabilitation, Shenzhen Second People’s Hospital, The First Affiliated Hospital of Shenzhen University Health Science Centre, Shenzhen, China
| | - Shengxiu Jiao
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Shandong, China
| | - Zeyu Zhang
- School of Rehabilitation Sciences, The Shandong University of Traditional Chinese Medicine, Shandong, China
| | - Kaiwen Xue
- School of Rehabilitation Sciences, The Shandong University of Traditional Chinese Medicine, Shandong, China
| | - Jianjun Long
- Department of Rehabilitation, Shenzhen Second People’s Hospital, The First Affiliated Hospital of Shenzhen University Health Science Centre, Shenzhen, China
- School of Rehabilitation Sciences, The Shandong University of Traditional Chinese Medicine, Shandong, China
| | - Fubing Zha
- Department of Rehabilitation, Shenzhen Second People’s Hospital, The First Affiliated Hospital of Shenzhen University Health Science Centre, Shenzhen, China
| | - Yuan Chen
- Department of Rehabilitation, Shenzhen Second People’s Hospital, The First Affiliated Hospital of Shenzhen University Health Science Centre, Shenzhen, China
| | - Jiehui Li
- School of Rehabilitation Sciences, The Shandong University of Traditional Chinese Medicine, Shandong, China
| | - Qingqing Yang
- School of Rehabilitation Sciences, The Shandong University of Traditional Chinese Medicine, Shandong, China
| | - Yulong Wang
- Department of Rehabilitation, Shenzhen Second People’s Hospital, The First Affiliated Hospital of Shenzhen University Health Science Centre, Shenzhen, China
- School of Rehabilitation Sciences, The Shandong University of Traditional Chinese Medicine, Shandong, China
- * E-mail:
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21
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Zihni E, McGarry BL, Kelleher JD. Moving Toward Explainable Decisions of Artificial Intelligence Models for the Prediction of Functional Outcomes of Ischemic Stroke Patients. Digit Health 2022. [DOI: 10.36255/exon-publications-digital-health-explainable-decisions] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
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22
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Koopman MS, Hoving JW, Kappelhof M, Berkhemer OA, Beenen LFM, van Zwam WH, de Jong HWAM, Dankbaar JW, Dippel DWJ, Coutinho JM, Marquering HA, Emmer BJ, Majoie CBLM. Association of Ischemic Core Imaging Biomarkers With Post-Thrombectomy Clinical Outcomes in the MR CLEAN Registry. Front Neurol 2022; 12:771367. [PMID: 35082746 PMCID: PMC8784730 DOI: 10.3389/fneur.2021.771367] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 11/30/2021] [Indexed: 12/04/2022] Open
Abstract
Background: A considerable proportion of acute ischemic stroke patients treated with endovascular thrombectomy (EVT) are dead or severely disabled at 3 months despite successful reperfusion. Ischemic core imaging biomarkers may help to identify patients who are more likely to have a poor outcome after endovascular thrombectomy (EVT) despite successful reperfusion. We studied the association of CT perfusion-(CTP), CT angiography-(CTA), and non-contrast CT-(NCCT) based imaging markers with poor outcome in patients who underwent EVT in daily clinical practice. Methods: We included EVT-treated patients (July 2016–November 2017) with an anterior circulation occlusion from the Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands (MR CLEAN) Registry with available baseline CTP, CTA, and NCCT. We used multivariable binary and ordinal logistic regression to analyze the association of CTP ischemic core volume, CTA-Collateral Score (CTA-CS), and Alberta Stroke Program Early CT Score (ASPECTS) with poor outcome (modified Rankin Scale score (mRS) 5-6) and likelihood of having a lower score on the mRS at 90 days. Results: In 201 patients, median core volume was 13 (IQR 5-41) mL. Median ASPECTS was 9 (IQR 8-10). Most patients had grade 2 (83/201; 42%) or grade 3 (28/201; 14%) collaterals. CTP ischemic core volume was associated with poor outcome [aOR per 10 mL 1.02 (95%CI 1.01–1.04)] and lower likelihood of having a lower score on the mRS at 90 days [aOR per 10 mL 0.85 (95% CI 0.78–0.93)]. In multivariable analysis, neither CTA-CS nor ASPECTS were significantly associated with poor outcome or the likelihood of having a lower mRS. Conclusion: In our population of patients treated with EVT in daily clinical practice, CTP ischemic core volume is associated with poor outcome and lower likelihood of shift toward better outcome in contrast to either CTA-CS or ASPECTS.
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Affiliation(s)
- Miou S Koopman
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - Jan W Hoving
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - Manon Kappelhof
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - Olvert A Berkhemer
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands.,Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Ludo F M Beenen
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - Wim H van Zwam
- Department of Radiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center+, Maastricht, Netherlands
| | - Hugo W A M de Jong
- Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Jan Willem Dankbaar
- Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Diederik W J Dippel
- Department of Neurology, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Jonathan M Coutinho
- Department of Neurology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - Henk A Marquering
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands.,Department of Biomedical Engineering and Physics, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - Bart J Emmer
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - Charles B L M Majoie
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
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23
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Kappelhof N, Ramos LA, Kappelhof M, van Os HJA, Chalos V, van Kranendonk KR, Kruyt ND, Roos YBWEM, van Zwam WH, van der Schaaf IC, van Walderveen MAA, Wermer MJH, van Oostenbrugge RJ, Lingsma H, Dippel D, Majoie CBLM, Marquering HA. Evolutionary algorithms and decision trees for predicting poor outcome after endovascular treatment for acute ischemic stroke. Comput Biol Med 2021; 133:104414. [PMID: 33962154 DOI: 10.1016/j.compbiomed.2021.104414] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 04/09/2021] [Accepted: 04/15/2021] [Indexed: 11/28/2022]
Abstract
Despite the large overall beneficial effects of endovascular treatment in patients with acute ischemic stroke, severe disability or death still occurs in almost one-third of patients. These patients, who might not benefit from treatment, have been previously identified with traditional logistic regression models, which may oversimplify relations between characteristics and outcome, or machine learning techniques, which may be difficult to interpret. We developed and evaluated a novel evolutionary algorithm for fuzzy decision trees to accurately identify patients with poor outcome after endovascular treatment, which was defined as having a modified Rankin Scale score (mRS) higher or equal to 5. The created decision trees have the benefit of being comprehensible, easily interpretable models, making its predictions easy to explain to patients and practitioners. Insights in the reason for the predicted outcome can encourage acceptance and adaptation in practice and help manage expectations after treatment. We compared our proposed method to CART, the benchmark decision tree algorithm, on classification accuracy and interpretability. The fuzzy decision tree significantly outperformed CART: using 5-fold cross-validation with on average 1090 patients in the training set and 273 patients in the test set, the fuzzy decision tree misclassified on average 77 (standard deviation of 7) patients compared to 83 (±7) using CART. The mean number of nodes (decision and leaf nodes) in the fuzzy decision tree was 11 (±2) compared to 26 (±1) for CART decision trees. With an average accuracy of 72% and much fewer nodes than CART, the developed evolutionary algorithm for fuzzy decision trees might be used to gain insights into the predictive value of patient characteristics and can contribute to the development of more accurate medical outcome prediction methods with improved clarity for practitioners and patients.
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Affiliation(s)
- N Kappelhof
- Department of Biomedical Engineering and Physics, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - L A Ramos
- Department of Biomedical Engineering and Physics, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands; Department of Clinical Epidemiology and Biostatistics, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
| | - M Kappelhof
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - H J A van Os
- Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands
| | - V Chalos
- Department of Neurology, Erasmus MC - University Medical Center, Rotterdam, the Netherlands; Department of Public Health, Erasmus MC - University Medical Center, Rotterdam, the Netherlands; Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Center, Rotterdam, the Netherlands
| | - K R van Kranendonk
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - N D Kruyt
- Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands
| | - Y B W E M Roos
- Department of Neurology, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - W H van Zwam
- Department of Radiology, Cardiovascular Research Institute Maastricht, Maastricht University Medical Center, Maastricht, the Netherlands
| | - I C van der Schaaf
- Department of Radiology, University Medical Centre, Utrecht, the Netherlands
| | - M A A van Walderveen
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - M J H Wermer
- Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands
| | - R J van Oostenbrugge
- Department of Neurology, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Hester Lingsma
- Department of Public Health, Erasmus MC - University Medical Center, Rotterdam, the Netherlands
| | - Diederik Dippel
- Department of Neurology, Erasmus MC - University Medical Center, Rotterdam, the Netherlands
| | - C B L M Majoie
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - H A Marquering
- Department of Biomedical Engineering and Physics, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands; Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
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