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Velasco Gonzalez A, Jingyu L, Buerke B, Görlich D, Ortega-Quintanilla J, Sauerland C, Meier N, Heindel W. Perfusion patterns as a tool for emergency stroke diagnosis: differentiating proximal and distal MCA occlusions. BMJ Neurol Open 2025; 7:e001001. [PMID: 40034652 PMCID: PMC11873334 DOI: 10.1136/bmjno-2024-001001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2024] [Accepted: 01/24/2025] [Indexed: 03/05/2025] Open
Abstract
Background To evaluate the effectiveness of a novel Perfusion Pattern (PP) scale in differentiating between proximal and distal middle cerebral artery (MCA) occlusions in patients with acute ischaemic stroke. Methods This retrospective study included 201 patients with acute ischaemic stroke, categorised into two groups: those with M1 segment occlusions (n=114) and those with distal medium vessel occlusions (n=87). We analysed multimodal stroke CT imaging and clinical data, focusing on the occlusion site, hypoperfusion extent and basal ganglia involvement. Patients with tandem stenosis or multiple acute occlusions were excluded. Perfusion patterns were categorised into three types (PP-1, PP-2 and PP-3) based on the extent of hypoperfusion. Statistical analysis explored associations between the occlusion site, perfusion pattern and collateral status. Results Among the 201 patients (mean age 75±14 years, 86 men), PP-1 was observed in 36.8% of patients (74/201), PP-2 in 27.4% (55/201) and PP-3 in 35.8% (72/201). The distribution of PP varied significantly by occlusion site (p<0.0001). Distal medium vessel occlusions were associated with PP-1 in 78.4% of cases (58/74), while PP-3 was most prevalent in M1 occlusions (90.3%, 65/72). The contingency coefficient revealed that occlusion location had a stronger association with the perfusion pattern (c=0.556) than collateral type (c=0.245). However, 21.6% of M1 occlusions (16/74) showed a PP-1 pattern and 9.7% of distal medium vessel occlusions (7/72) exhibited PP-3. Basal ganglia infarction presence was a reliable indicator of M1 occlusion with a 94% likelihood. Conclusions Perfusion patterns can effectively differentiate between proximal and distal medium vessel MCA occlusions, aiding targeted assessment of CT angiography.
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Affiliation(s)
| | - Liu Jingyu
- Department of Diagnostic and Interventional Radiology, University of Jena, Jena, Germany
| | - Boris Buerke
- Clinic for Radiology, Neuroradiology, University Hospital Münster, Münster, Germany
| | - Dennis Görlich
- Institute of Biostatistics and Clinical Research, University of Münster, Münster, Germany
| | - Joaquin Ortega-Quintanilla
- Department of Radiology, Section of Neuro-Interventional Radiology, Son Espases University Hospital, Palma, Spain
| | - Cristina Sauerland
- Institute of Biostatistics and Clinical Research, University of Münster, Münster, Germany
| | - Norbert Meier
- Clinic for Radiology, Medical Physics, University of Münster, Münster, Germany
| | - Walter Heindel
- Clinic for Radiology, Neuroradiology, University Hospital Münster, Münster, Germany
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De Rubeis G, Chaturvedi S, Kamel H, Meschia J, Pampana E, Saba L. Heterogeneity in measurement of NIHSS: a systematic review and meta-analysis. Neurol Sci 2025; 46:227-237. [PMID: 39240475 DOI: 10.1007/s10072-024-07733-z] [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: 06/06/2024] [Accepted: 08/19/2024] [Indexed: 09/07/2024]
Abstract
BACKGROUND The National Institutes of Health Stroke Scale (NIHSS) is a pivotal clinical tool used to assess patients with acute stroke. However, substantial heterogeneity in the application and interpretation of stroke scale items can occur. This systematic review aimed to elucidate heterogeneity in measuring the NIHSS. MATERIAL AND METHODS A literature search was performed on PubMed/OVID/Cochran's CENTRAL from inception to 2023. The references of the included papers were reviewed for further eligible articles. Clinical characteristic, NIHSS values, and sources of heterogeneity were recorded. Non-human and non-English language articles were excluded. The study quality was assessed using MINORS and GRADE. Meta-analysis and meta-regression were performed using a random-effects model to explore the sources of heterogeneity. RESULTS Twenty-one papers for a total of 818 patients (mean per study: 39 ± 37) and 9696 NIHSS examinations (median per study: 8 [CI95% 2 to 42]) were included. Motor function had a higher ICC agreement (ranging from 0.85 ["Right Leg"] to 0.90 ["Right Arm"]) compared to the remaining items (ranging from 0.58 ["Facial Palsy"] to 0.85 ["Level of consciousness commands"]. The meta-regression showed a low effect size of covariates such as language version, remote evaluation, and retrospective analysis on NIHSS items (e.g., for "Level of consciousness commands," language effect was 0.30 [CI95% 0.20 to 0.48] and for "Visual", the retrospective assessment effect was -0.27 [CI95% -0.51 to -0.03]). CONCLUSION The NIHSS scores showed moderate to excellent inter-rater agreement, with the highest heterogeneity in non-motor function evaluation. Using a non-English version, remote evaluation and retrospective analysis had small effects in terms of heterogeneity in the NIHSS scores.
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Affiliation(s)
- Gianluca De Rubeis
- Department of Diagnostic, UOC of Diagnostic and Interventional Neuroradiology, San Camillo-Forlanini Hospital, Circonvallazione Gianicolense 87, 00152, Rome, Italy.
| | - Seemant Chaturvedi
- Department of Neurology, University of Maryland, and Baltimore VA Hospital, Baltimore, MD, USA
| | - Hooman Kamel
- Clinical and Translational Neuroscience Unit, Department of Neurology and Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - James Meschia
- Department of Neurology, Mayo Clinic, Jacksonville, FL, USA
| | - Enrico Pampana
- Department of Diagnostic, UOC of Diagnostic and Interventional Neuroradiology, San Camillo-Forlanini Hospital, Circonvallazione Gianicolense 87, 00152, Rome, Italy
| | - Luca Saba
- Department of Medical Imaging, Azienda Ospedaliero Universitaria (A.O.U.) of Cagliari-Polo Di Monserrato, Cagliari, Italy
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Raza SA, Rangaraju S. Prognostic Scores for Large Vessel Occlusion Strokes. Neurology 2021; 97:S79-S90. [PMID: 34785607 DOI: 10.1212/wnl.0000000000012797] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Accepted: 10/23/2020] [Indexed: 11/15/2022] Open
Abstract
PURPOSE OF THE REVIEW Endovascular thrombectomy (EVT) for large vessel occlusion strokes (LVOS) presents several treatment challenges. We provide a summary of existing tools for patient selection (pre-EVT tools) and for prognostication of long-term outcomes following reperfusion therapy (post-EVT tools). RECENT FINDINGS Recently published randomized trials demonstrated superiority of EVT over medical therapy alone for LVOS. Uniform patient selection paradigms based on demographic, clinical, and radiographic variables are not completely standardized, leading to variability in patient selection for EVT for LVOS. Post-EVT, an accurate assessment of long-term prognosis is critical in the decision-making process. SUMMARY Prognostic scores can serve as useful adjuncts to facilitate clinical decision-making during early management of patients with ischemic stroke, particularly those with LVOS. The acute management of LVOS comprises rapid clinical assessment, triage, and cerebrovascular imaging, followed by evaluation for candidacy for thrombolysis and EVT. Pre-EVT prognostic tools that accurately predict the likelihood of benefit from EVT may guide reliable, efficient, and cost-effective patient selection. Following EVT, severe stroke deficits and subacute poststroke complications that portend a poor prognosis may warrant invasive therapies. Clinical decisions regarding these treatment options involve careful discussions between providers and patient families, and are also based on prognosis provided by the treating clinician. Reliable post-EVT prognostic tools can facilitate this by providing accurate and objective prognostic information. Several prognostic tools have been developed and validated in the literature, some of which may be applicable in the pre-EVT and post-EVT settings, although clinical utility and application varies. Validation in contemporary datasets as well as implementation and impact studies are needed before these scales can be used to guide clinical decisions for individual patients.
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Affiliation(s)
- Syed Ali Raza
- From the Department of Neurology (S.A.R.), Ochsner Louisiana State University Health Sciences Center, Shreveport; and Department of Neurology (S.R.), Emory University, Atlanta GA
| | - Srikant Rangaraju
- From the Department of Neurology (S.A.R.), Ochsner Louisiana State University Health Sciences Center, Shreveport; and Department of Neurology (S.R.), Emory University, Atlanta GA.
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Weissenborn K, Gruber S, Grosse GM, Gabriel M, Schuppner R, Worthmann H, Abu-Fares O, Götz F. Endovascular Treatment of Acute Ischemic Stroke in Clinical Practice: Analysis of Workflow and Outcome in a Tertiary Care Center. Front Neurol 2021; 12:657345. [PMID: 34163422 PMCID: PMC8215500 DOI: 10.3389/fneur.2021.657345] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 04/27/2021] [Indexed: 11/13/2022] Open
Abstract
Background and Purpose: Pre- and intra-hospital workflow in mechanical recanalization of large cervicocephalic arteries in patients with acute ischemic stroke still needs optimization. In this study, we analyze workflow and outcome in our routine care of stroke patients undergoing mechanical thrombectomy as a precondition for such optimization. Methods: Processes of pre- and intra-hospital management, causes of treatment delay, imaging results (Alberta Stroke Program Early Computed Tomography Score, localization of vessel occlusion), recanalization (modified thrombolysis in cerebral infarction score), and patient outcome (modified Rankin scale at discharge and at the end of inpatient rehabilitation) were analyzed for all patients who underwent mechanical thrombectomy between April 1, 2016, and September 30, 2018, at our site. Results: Finally, data of 282 patients were considered, of whom 150 (53%) had been referred from external hospitals. Recanalization success and patient outcome were similar to randomized controlled thrombectomy studies and registries. Delay in treatment occurred when medical treatment of a hypertensive crisis, epileptic fits, vomiting, or agitation was mandatory but also due to missing prenotification of the hospital emergency staff by the rescue service, multiple mode or repeated brain imaging, and transfer from another hospital. Even transfer from external hospitals located within a 10-km radius of our endovascular treatment center led to a median increase of the onset-to-groin time of ~60 min. Conclusion: The analysis revealed several starting points for an improvement in the workflow of thrombectomy in our center. Analyses of workflow and treatment results should be carried out regularly to identify the potential for optimization of operational procedures and selection criteria for patients who could benefit from endovascular treatment.
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Affiliation(s)
| | - Sam Gruber
- Clinic for Neurology, Hannover Medical School, Hannover, Germany
| | - Gerrit M Grosse
- Clinic for Neurology, Hannover Medical School, Hannover, Germany
| | - Maria Gabriel
- Clinic for Neurology, Hannover Medical School, Hannover, Germany
| | - Ramona Schuppner
- Clinic for Neurology, Hannover Medical School, Hannover, Germany
| | - Hans Worthmann
- Clinic for Neurology, Hannover Medical School, Hannover, Germany
| | - Omar Abu-Fares
- Hannover Medical School, Institute for Diagnostic and Interventional Neuroradiology, Hannover, Germany
| | - Friedrich Götz
- Hannover Medical School, Institute for Diagnostic and Interventional Neuroradiology, Hannover, Germany
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Velasco Gonzalez A, Görlich D, Buerke B, Münnich N, Sauerland C, Rusche T, Faldum A, Heindel W. Predictors of Successful First-Pass Thrombectomy with a Balloon Guide Catheter: Results of a Decision Tree Analysis. Transl Stroke Res 2020; 11:900-909. [PMID: 32447614 PMCID: PMC7496051 DOI: 10.1007/s12975-020-00784-2] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Revised: 01/13/2020] [Accepted: 01/28/2020] [Indexed: 02/06/2023]
Abstract
Complete recanalization after a single retrieval maneuver is an interventional goal in acute ischemic stroke and an independent factor for good clinical outcome. Anatomical biomarkers for predicting clot removal difficulties have not been comprehensively analyzed and await unused. We retrospectively evaluated 200 consecutive patients who suffered acute stroke and occlusion of the anterior circulation and were treated with mechanical thrombectomy through a balloon guide catheter (BGC). The primary objective was to evaluate the influence of carotid tortuosity and BGC positioning on the one-pass Modified Thrombolysis in Cerebral Infarction Scale (mTICI) 3 rate, and secondarily, the influence of communicating arteries on the angiographic results. After the first-pass mTICI 3, recanalization fell from 51 to 13%. The regression models and decision tree (supervised machine learning) results concurred: carotid tortuosity was the main constraint on efficacy, reducing the likelihood of mTICI 3 after one pass to 30%. BGC positioning was relevant only in carotid arteries without elongation: BGCs located in the distal internal carotid artery (ICA) had a 70% probability of complete recanalization after one pass, dropping to 43% if located in the proximal ICA. These findings demonstrate that first-pass mTICI 3 is influenced by anatomical and interventional factors capable of being anticipated, enabling the BGC technique to be adapted to patient’s anatomy to enhance effectivity.
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Affiliation(s)
- Aglaé Velasco Gonzalez
- Department of Clinical Radiology, Institute of Clinical Radiology and Neuroradiology, University Hospital of Muenster, Albert-Schweitzer-Campus 1, Building A1, 48149 Muenster, Germany
| | - Dennis Görlich
- Institute of Biostatistics and Clinical Research, University of Muenster, Schmeddingstraße 56, 48149 Muenster, Germany
| | - Boris Buerke
- Department of Clinical Radiology, Institute of Clinical Radiology and Neuroradiology, University Hospital of Muenster, Albert-Schweitzer-Campus 1, Building A1, 48149 Muenster, Germany
| | - Nico Münnich
- Department of Clinical Radiology, Institute of Clinical Radiology and Neuroradiology, University Hospital of Muenster, Albert-Schweitzer-Campus 1, Building A1, 48149 Muenster, Germany
| | - Cristina Sauerland
- Institute of Biostatistics and Clinical Research, University of Muenster, Schmeddingstraße 56, 48149 Muenster, Germany
| | - Thilo Rusche
- Department of Clinical Radiology, Institute of Clinical Radiology and Neuroradiology, University Hospital of Muenster, Albert-Schweitzer-Campus 1, Building A1, 48149 Muenster, Germany
| | - Andreas Faldum
- Institute of Biostatistics and Clinical Research, University of Muenster, Schmeddingstraße 56, 48149 Muenster, Germany
| | - Walter Heindel
- Department of Clinical Radiology, Institute of Clinical Radiology and Neuroradiology, University Hospital of Muenster, Albert-Schweitzer-Campus 1, Building A1, 48149 Muenster, Germany
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Fargen KM, Leslie-Mazwi TM, Chen M, Hirsch JA. Physician, know thyself: implicit and explicit decision-making for mechanical thrombectomy in stroke. J Neurointerv Surg 2020; 12:952-956. [DOI: 10.1136/neurintsurg-2020-015973] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Revised: 03/27/2020] [Accepted: 03/30/2020] [Indexed: 01/02/2023]
Abstract
Few clinical situations in medical practice are as time-sensitive and and have such profound ramifications as selection of patients with acute stroke for mechanical thrombectomy (MT). Emergent large vessel occlusion has become a treatable disease with minimal numbers needed to treat to achieve a functional, long-term neurologic outcome. However, MT carries risk and many patients who are appropriately reperfused continue to have significant neurologic deficits and disability despite a successful procedure. The decision to offer or withhold MT can be complex. Frequently decisions must be made based on incomplete information or emergently while the physician is awoken from sleep or distracted while performing other procedures. A growing number of studies have examined cognitive errors and biases as they pertain to patient diagnosis and treatment in medicine. Dual process theory identifies two decision-making processes as system 1 ('implicit') and system 2 ('explicit') and describes the patterns through which decisions are formulated. The implicit system is the default pathway as it requires little effort or focus, uses mental short cuts, and is rapid; however, this pathway is subject to considerable bias and error. This manuscript reviews the mechanisms underlying the way in which physician decisions about MT are made, specifically highlighting prominent biases that may affect judgment, and reviews other important principles, such as confidence in decisions, aggressiveness to pursue MT, and strategies to improve decisions.
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Velasco Gonzalez A, Buerke B, Görlich D, Fobker M, Rusche T, Sauerland C, Meier N, Jeibmann A, McCarthy R, Kugel H, Sporns P, Faldum A, Paulus W, Heindel W. Clot Analog Attenuation in Non-contrast CT Predicts Histology: an Experimental Study Using Machine Learning. Transl Stroke Res 2020; 11:940-949. [PMID: 31933117 DOI: 10.1007/s12975-019-00766-z] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 12/01/2019] [Accepted: 12/04/2019] [Indexed: 12/12/2022]
Abstract
Exact histological clot composition remains unknown. The purpose of this study was to identify the best imaging variables to be extrapolated on clot composition and clarify variability in the imaging of thrombi by non-contrast CT. Using a CT-phantom and covering a wide range of histologies, we analyzed 80 clot analogs with respect to X-ray attenuation at 24 and 48 h after production. The mean, maximum, and minimum HU values for the axial and coronal reconstructions were recorded. Each thrombus underwent a corresponding histological analysis, together with a laboratory analysis of water and iron contents. Decision trees, a type of supervised machine learning, were used to select the primary variable altering attenuation and the best parameter for predicting histology. The decision trees selected red blood cells (RBCs) for correlation with all attenuation parameters (p < 0.001). Conversely, maximum attenuation on axial CT offered the greatest accuracy for discriminating up to four groups of clot histology (p < 0.001). Similar RBC-rich thrombi displayed variable imaging associated with different iron (p = 0.023) and white blood cell contents (p = 0.019). Water content varied among the different histologies but did not in itself account for the differences in attenuation. Independent factors determining clot attenuation were the RBCs (β = 0.33, CI = 0.219-0.441, p < 0.001) followed by the iron content (β = 0.005, CI = 0.0002-0.009, p = 0.042). Our findings suggest that it is possible to extract more and valuable information from NCCT that can be extrapolated to provide insights into clot histological and chemical composition.
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Affiliation(s)
- Aglae Velasco Gonzalez
- Department of Clinical Radiology, Neuroradiology, University Hospital Muenster, Albert-Schweitzer-Campus 1, Gebäude A1, 48149, Muenster, Germany.
| | - Boris Buerke
- Department of Clinical Radiology, Neuroradiology, University Hospital Muenster, Albert-Schweitzer-Campus 1, Gebäude A1, 48149, Muenster, Germany
| | - Dennis Görlich
- Institute of Biostatistics and Clinical Research, University of Muenster, Schmeddingstraße 56, 48149, Muenster, Germany
| | - Manfred Fobker
- Center for Laboratory Medicine, University Hospital Muenster, Albert-Schweitzer-Campus 1, Gebäude A1, 48149, Muenster, Germany
| | - Thilo Rusche
- Department of Clinical Radiology, Neuroradiology, University Hospital Muenster, Albert-Schweitzer-Campus 1, Gebäude A1, 48149, Muenster, Germany
| | - Cristina Sauerland
- Institute of Biostatistics and Clinical Research, University of Muenster, Schmeddingstraße 56, 48149, Muenster, Germany
| | - Norbert Meier
- Department of Clinical Radiology, Medical Physics, University Hospital Muenster, Albert-Schweitzer-Campus 1, Gebäude A1, 48149, Muenster, Germany
| | - Astrid Jeibmann
- Institute of Neuropathology, University Hospital Muenster, Pottkamp 2, 48149, Muenster, Germany
| | - Ray McCarthy
- Cerenovus, Galway Neuro Technology Centre, Mervue Business Park, Galway, Ireland
| | - Harald Kugel
- Department of Clinical Radiology, Medical Physics, University Hospital Muenster, Albert-Schweitzer-Campus 1, Gebäude A1, 48149, Muenster, Germany
| | - Peter Sporns
- Department of Clinical Radiology, Neuroradiology, University Hospital Muenster, Albert-Schweitzer-Campus 1, Gebäude A1, 48149, Muenster, Germany
| | - Andreas Faldum
- Institute of Biostatistics and Clinical Research, University of Muenster, Schmeddingstraße 56, 48149, Muenster, Germany
| | - Werner Paulus
- Institute of Neuropathology, University Hospital Muenster, Pottkamp 2, 48149, Muenster, Germany
| | - Walter Heindel
- Department of Clinical Radiology, Neuroradiology, University Hospital Muenster, Albert-Schweitzer-Campus 1, Gebäude A1, 48149, Muenster, Germany
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