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Straccia A, Barbour MC, Chassagne F, Bass D, Barros G, Leotta D, Sheehan F, Sharma D, Levitt MR, Aliseda A. Numerical Modeling of Flow in the Cerebral Vasculature: Understanding Changes in Collateral Flow Directions in the Circle of Willis for a Cohort of Vasospasm Patients Through Image-Based Computational Fluid Dynamics. Ann Biomed Eng 2024; 52:2417-2439. [PMID: 38758460 PMCID: PMC11329356 DOI: 10.1007/s10439-024-03533-w] [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: 03/13/2024] [Accepted: 04/30/2024] [Indexed: 05/18/2024]
Abstract
The Circle of Willis (CoW) is a ring-like network of blood vessels that perfuses the brain. Flow in the collateral pathways that connect major arterial inputs in the CoW change dynamically in response to vessel narrowing or occlusion. Vasospasm is an involuntary constriction of blood vessels following subarachnoid hemorrhage (SAH), which can lead to stroke. This study investigated interactions between localization of vasospasm in the CoW, vasospasm severity, anatomical variations, and changes in collateral flow directions. Patient-specific computational fluid dynamics (CFD) simulations were created for 25 vasospasm patients. Computed tomographic angiography scans were segmented capturing the anatomical variation and stenosis due to vasospasm. Transcranial Doppler ultrasound measurements of velocity were used to define boundary conditions. Digital subtraction angiography was analyzed to determine the directions and magnitudes of collateral flows as well as vasospasm severity in each vessel. Percent changes in resistance and viscous dissipation were analyzed to quantify vasospasm severity and localization of vasospasm in a specific region of the CoW. Angiographic severity correlated well with percent changes in resistance and viscous dissipation across all cerebral vessels. Changes in flow direction were observed in collateral pathways of some patients with localized vasospasm, while no significant changes in flow direction were observed in others. CFD simulations can be leveraged to quantify the localization and severity of vasospasm in SAH patients. These factors as well as anatomical variation may lead to changes in collateral flow directions. Future work could relate localization and vasospasm severity to clinical outcomes like the development of infarct.
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Affiliation(s)
- Angela Straccia
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA.
| | - Michael C Barbour
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
| | | | - David Bass
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | - Guilherme Barros
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | - Daniel Leotta
- Applied Physics Laboratory, University of Washington, Seattle, WA, USA
| | - Florence Sheehan
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - Deepak Sharma
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA, USA
| | - Michael R Levitt
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | - Alberto Aliseda
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
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Liao J, Misaki K, Sakamoto J. Impact Exploration of Spatiotemporal Feature Derivation and Selection on Machine Learning-Based Predictive Models for Post-Embolization Cerebral Aneurysm Recanalization. Cardiovasc Eng Technol 2024; 15:394-404. [PMID: 38782877 DOI: 10.1007/s13239-024-00721-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 02/04/2024] [Indexed: 05/25/2024]
Abstract
PURPOSE To enhance the performance of machine learning (ML) models for the post-embolization recanalization of cerebral aneurysms, we evaluated the impact of hemodynamic feature derivation and selection method on six ML algorithms. METHODS We utilized computational fluid dynamics (CFD) to simulate hemodynamics in 66 cerebral aneurysms from 65 patients, including 57 stable and nine recanalized aneurysms. We derived a total of 107 features for each aneurysm, encompassing four clinical features, 12 morphological features, and 91 hemodynamic features. To investigate the influence of feature derivation and selection methods on the ML models, we employed two derivation methods, simplified and fully derived, in combination with four selection methods: all features, statistically significant analysis, stepwise multivariate logistic regression analysis (stepwise-LR), and recursive feature elimination (RFE). Model performance was assessed using the area under the receiver operating characteristic curve (AUROC) and precision-recall curve (AUPRC) on both the training and testing datasets. RESULTS The AUROC values on the testing dataset exhibited a wide-ranging spectrum, spanning from 0.373 to 0.863. Fully derived features and the RFE selection method demonstrated superior performance in intra-model comparisons. The multi-layer perceptron (MLP) model, trained with RFE-selected fully derived features, achieved the best performance on the testing dataset, with an AUROC value of 0.863 (95% CI: 0.684- 1.000). CONCLUSION Our study demonstrated the importance of feature derivation and selection in determining the performance of ML models. This enabled the development of accurate decision-making models without the need to invade the patient.
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Affiliation(s)
- Jing Liao
- Division of Transdisciplinary Sciences, Graduate School of Frontier Science Initiative, Kanazawa University, Ishikawa, Japan.
| | - Kouichi Misaki
- Department of Neurosurgery, Kanazawa University, Ishikawa, Japan
| | - Jiro Sakamoto
- Division of Mechanical Science and Engineering, Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Ishikawa, Japan
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Ratkunas V, Misiulis E, Lapinskiene I, Skarbalius G, Navakas R, Dziugys A, Barkauskiene A, Preiksaitis A, Serpytis M, Rocka S, Lukosevicius S, Iesmantas T, Alzbutas R, Sengupta J, Petkus V. Cerebrospinal fluid volume as an early radiological factor for clinical course prediction after aneurysmal subarachnoid hemorrhage. A pilot study. Eur J Radiol 2024; 176:111483. [PMID: 38705051 DOI: 10.1016/j.ejrad.2024.111483] [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: 02/13/2024] [Revised: 03/29/2024] [Accepted: 04/27/2024] [Indexed: 05/07/2024]
Abstract
BACKGROUND The pathological mechanisms following aneurysmal subarachnoid hemorrhage (SAH) are poorly understood. Limited clinical evidence exists on the association between cerebrospinal fluid (CSF) volume and the risk of delayed cerebral ischemia (DCI) or cerebral vasospasm (CV). In this study, we raised the hypothesis that the amount of CSF or its ratio to hemorrhage blood volume, as determined from non-contrast Computed Tomography (NCCT) images taken on admission, could be a significant predictor for CV and DCI. METHODS The pilot study included a retrospective analysis of NCCT scans of 49 SAH patients taken shortly after an aneurysm rupture (33 males, 16 females, mean age 56.4 ± 15 years). The SynthStrip and Slicer3D software tools were used to extract radiological factors - CSF, brain, and hemorrhage volumes from the NCCT images. The "pure" CSF volume (VCSF) was estimated in the range of [-15, 15] Hounsfield units (HU). RESULTS VCSF was negatively associated with the risk of CV occurrence (p = 0.0049) and DCI (p = 0.0069), but was not associated with patients' outcomes. The hemorrhage volume (VSAH) was positively associated with an unfavorable outcome (p = 0.0032) but was not associated with CV/DCI. The ratio VSAH/VCSF was positively associated with, both, DCI (p = 0.031) and unfavorable outcome (p = 0.002). The CSF volume normalized by the brain volume showed the highest characteristics for DCI prediction (AUC = 0.791, sensitivity = 0.80, specificity = 0.812) and CV prediction (AUC = 0.769, sensitivity = 0.812, specificity = 0.70). CONCLUSION It was demonstrated that "pure" CSF volume retrieved from the initial NCCT images of SAH patients (including CV, Non-CV, DCI, Non-DCI groups) is a more significant predictor of DCI and CV compared to other routinely used radiological biomarkers. VCSF could be used to predict clinical course as well as to personalize the management of SAH patients. Larger multicenter clinical trials should be performed to test the added value of the proposed methodology.
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Affiliation(s)
- Vytenis Ratkunas
- Department of Radiology, Lithuanian University of Health Sciences, Eiveniu st. 2, Kaunas 50009, Lithuania
| | - Edgaras Misiulis
- Laboratory of Heat-Equipment Research and Testing, Lithuanian Energy Institute, Breslaujos st. 3, Kaunas 44403, Lithuania.
| | - Indre Lapinskiene
- Faculty of Medicine, Vilnius University, M. K. Ciurlionio st. 21, Vilnius 03101, Lithuania
| | - Gediminas Skarbalius
- Laboratory of Heat-Equipment Research and Testing, Lithuanian Energy Institute, Breslaujos st. 3, Kaunas 44403, Lithuania
| | - Robertas Navakas
- Laboratory of Heat-Equipment Research and Testing, Lithuanian Energy Institute, Breslaujos st. 3, Kaunas 44403, Lithuania
| | - Algis Dziugys
- Laboratory of Heat-Equipment Research and Testing, Lithuanian Energy Institute, Breslaujos st. 3, Kaunas 44403, Lithuania
| | - Alina Barkauskiene
- Center for Radiology and Nuclear Medicine, Vilnius University Hospital Santaros Klinikos, Santariskiu st. 2, Vilnius 08661, Lithuania
| | - Aidanas Preiksaitis
- Faculty of Medicine, Vilnius University, M. K. Ciurlionio st. 21, Vilnius 03101, Lithuania
| | - Mindaugas Serpytis
- Faculty of Medicine, Vilnius University, M. K. Ciurlionio st. 21, Vilnius 03101, Lithuania
| | - Saulius Rocka
- Faculty of Medicine, Vilnius University, M. K. Ciurlionio st. 21, Vilnius 03101, Lithuania
| | - Saulius Lukosevicius
- Department of Radiology, Lithuanian University of Health Sciences, Eiveniu st. 2, Kaunas 50009, Lithuania
| | - Tomas Iesmantas
- Kaunas University of Technology, K. Donelaičio st. 73, Kaunas 44249, Lithuania
| | - Robertas Alzbutas
- Kaunas University of Technology, K. Donelaičio st. 73, Kaunas 44249, Lithuania
| | - Jewel Sengupta
- Kaunas University of Technology, K. Donelaičio st. 73, Kaunas 44249, Lithuania
| | - Vytautas Petkus
- Kaunas University of Technology, K. Donelaičio st. 73, Kaunas 44249, Lithuania
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Berli S, Barbagallo M, Keller E, Esposito G, Pagnamenta A, Brandi G. Sex-Related Differences in Mortality, Delayed Cerebral Ischemia, and Functional Outcomes in Patients with Aneurysmal Subarachnoid Hemorrhage: A Systematic Review and Meta-Analysis. J Clin Med 2024; 13:2781. [PMID: 38792323 PMCID: PMC11122382 DOI: 10.3390/jcm13102781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 04/30/2024] [Accepted: 05/06/2024] [Indexed: 05/26/2024] Open
Abstract
Background/Objective: Sex-related differences among patients with aneurysmal subarachnoid hemorrhage (aSAH) and their potential clinical implications have been insufficiently investigated. To address this knowledge gap, we conduct a comprehensive systematic review and meta-analysis. Methods: Sex-specific differences in patients with aSAH, including mortality, delayed cerebral ischemia (DCI), and functional outcomes were assessed. The functional outcome was dichotomized into favorable or unfavorable based on the modified Rankin Scale (mRS), Glasgow Outcome Scale (GOS), and Glasgow Outcome Scale Extended (GOSE). Results: Overall, 2823 studies were identified in EMBASE, MEDLINE, PubMed, and by manual search on 14 February 2024. After an initial assessment, 74 studies were included in the meta-analysis. In the analysis of mortality, including 18,534 aSAH patients, no statistically significant differences could be detected (risk ratio (RR) 0.99; 95% CI, 0.90-1.09; p = 0.91). In contrast, the risk analysis for DCI, including 23,864 aSAH patients, showed an 11% relative risk reduction in DCI in males versus females (RR, 0.89; 95% CI, 0.81-0.97; p = 0.01). The functional outcome analysis (favorable vs. unfavorable), including 7739 aSAH patients, showed a tendency towards better functional outcomes in men than women; however, this did not reach statistical significance (RR, 1.02; 95% CI, 0.98-1.07; p = 0.34). Conclusions: In conclusion, the available data suggest that sex/gender may play a significant role in the risk of DCI in patients with aSAH, emphasizing the need for sex-specific management strategies.
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Affiliation(s)
- Sarah Berli
- Faculty of Medicine, University of Zurich, 8032 Zurich, Switzerland
- Neurocritical Care Unit, Department of Neurosurgery, Institute for Intensive Care Medicine, University Hospital Zurich, 8091 Zurich, Switzerland
| | - Massimo Barbagallo
- Neurocritical Care Unit, Department of Neurosurgery, Institute for Intensive Care Medicine, University Hospital Zurich, 8091 Zurich, Switzerland
| | - Emanuela Keller
- Faculty of Medicine, University of Zurich, 8032 Zurich, Switzerland
- Neurocritical Care Unit, Department of Neurosurgery, Institute for Intensive Care Medicine, University Hospital Zurich, 8091 Zurich, Switzerland
- Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland
| | - Giuseppe Esposito
- Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland
- Department of Neurosurgery, University Hospital Zurich, 8091 Zurich, Switzerland
| | - Alberto Pagnamenta
- Clinical Trial Unit, Ente Ospedaliero Cantonale, 6900 Lugano, Switzerland
- Department of Intensive Care, Ente Ospedaliero Cantonale, 6900 Lugano, Switzerland
- Division of Pneumology, University of Geneva, 1211 Geneva, Switzerland
| | - Giovanna Brandi
- Faculty of Medicine, University of Zurich, 8032 Zurich, Switzerland
- Neurocritical Care Unit, Department of Neurosurgery, Institute for Intensive Care Medicine, University Hospital Zurich, 8091 Zurich, Switzerland
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Liao J, Misaki K, Uno T, Nambu I, Kamide T, Chen Z, Nakada M, Sakamoto J. Fluid dynamic analysis in predicting the recanalization of intracranial aneurysms after coil embolization - A study of spatiotemporal characteristics. Heliyon 2024; 10:e22801. [PMID: 38226254 PMCID: PMC10788401 DOI: 10.1016/j.heliyon.2023.e22801] [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: 04/25/2023] [Revised: 10/09/2023] [Accepted: 11/20/2023] [Indexed: 01/17/2024] Open
Abstract
Purpose Hemodynamics play a key role in the management of cerebral aneurysm recanalization after coil embolization; however, the most reliable hemodynamic parameter remains unknown. Previous studies have explored the use of both spatiotemporally averaged and maximal definitions for hemodynamic parameters, based on computational fluid dynamics (CFD) analysis, to build predictive models for aneurysmal recanalization. In this study, we aimed to assess the influence of different spatiotemporal characteristics of hemodynamic parameters on predictive performance. Methods Hemodynamics were simulated using CFD for 66 cerebral aneurysms from 65 patients. We evaluated 14 types of spatiotemporal definitions for two hemodynamic parameters in the pre-coiling model and five in virtual post-coiling model (VM) created by cutting the aneurysm from the pre-coiling model. A total of 91 spatiotemporal hemodynamic features were derived and utilized to develop univariate predictor (UP) and multivariate logistic regression (LR) models. The model's performance was assessed using two metrics: the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). Results Different spatiotemporal hemodynamic features exhibited a wide range of AUROC values ranging from 0.224 to 0.747, with 22 feature pairs showing a significant difference in AUROC value (P-value <0.05), despite being derived from the same hemodynamic parameter. PDave,q1 was identified as the strongest UP with AUROC/AUPRC values of 0.747/0.385, yielding sensitivity and specificity value of 0.889 and 0.614 at the optimal cut-off value, respectively. The LR model further improved the prediction performance, having AUROC/AUPRC values of 0.890/0.903. At the optimal cut-off value, the LR model achieved a specificity of 0.877, sensitivity of 0.719, outperforming the UP model. Conclusion Our research indicated that the characteristics of hemodynamic parameters in terms of space and time had a significant impact on the development of predictive model. Our findings suggest that LR model based on spatiotemporal hemodynamic features could be clinically useful in predicting recanalization after coil embolization in patients, without the need for invasive procedures.
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Affiliation(s)
- Jing Liao
- Division of Transdisciplinary Sciences, Graduate School of Frontier Science Initiative, Kanazawa University, Ishikawa, Japan
| | - Kouichi Misaki
- Department of Neurosurgery, Kanazawa University, Ishikawa, Japan
| | - Tekehiro Uno
- Department of Neurosurgery, Kanazawa University, Ishikawa, Japan
| | - Iku Nambu
- Department of Neurosurgery, Kanazawa University, Ishikawa, Japan
| | - Tomoya Kamide
- Department of Neurosurgery, Kanazawa University, Ishikawa, Japan
| | - Zhuoqing Chen
- Department of Nuclear Medicine, Kanazawa University, Ishikawa, Japan
| | | | - Jiro Sakamoto
- Division of Mechanical Science and Engineering, Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Ishikawa, Japan
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Nafees Ahmed S, Prakasam P. A systematic review on intracranial aneurysm and hemorrhage detection using machine learning and deep learning techniques. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2023; 183:1-16. [PMID: 37499766 DOI: 10.1016/j.pbiomolbio.2023.07.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 07/05/2023] [Accepted: 07/15/2023] [Indexed: 07/29/2023]
Abstract
The risk of discovering an intracranial aneurysm during the initial screening and follow-up screening are reported as around 11%, and 7% respectively (Zuurbie et al., 2023) to these mass effects, unruptured aneurysms frequently generate symptoms, however, the real hazard occurs when an aneurysm ruptures and results in a cerebral hemorrhage known as a subarachnoid hemorrhage. The objective is to study the multiple kinds of hemorrhage and aneurysm detection problems and develop machine and deep learning models to recognise them. Due to its early stage, subarachnoid hemorrhage, the most typical symptom after aneurysm rupture, is an important medical condition. It frequently results in severe neurological emergencies or even death. Although most aneurysms are asymptomatic and won't burst, because of their unpredictable growth, even small aneurysms are susceptible. A timely diagnosis is essential to prevent early mortality because a large percentage of hemorrhage cases present can be fatal. Physiological/imaging markers and the degree of the subarachnoid hemorrhage can be used as indicators for potential early treatments in hemorrhage. The hemodynamic pathomechanisms and microcellular environment should remain a priority for academics and medical professionals. There is still disagreement about how and when to care for aneurysms that have not ruptured despite studies reporting on the risk of rupture and outcomes. We are optimistic that with the progress in our understanding of the pathophysiology of hemorrhages and aneurysms and the advancement of artificial intelligence has made it feasible to conduct analyses with a high degree of precision, effectiveness and reliability.
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Affiliation(s)
- S Nafees Ahmed
- School of Electronics Engineering, Vellore Institute of Technology, Vellore, India.
| | - P Prakasam
- School of Electronics Engineering, Vellore Institute of Technology, Vellore, India.
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Saigal K, Patel AB, Lucke-Wold B. Artificial Intelligence and Neurosurgery: Tracking Antiplatelet Response Patterns for Endovascular Intervention. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:1714. [PMID: 37893432 PMCID: PMC10608122 DOI: 10.3390/medicina59101714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 09/22/2023] [Accepted: 09/22/2023] [Indexed: 10/29/2023]
Abstract
Platelets play a critical role in blood clotting and the development of arterial blockages. Antiplatelet therapy is vital for preventing recurring events in conditions like coronary artery disease and strokes. However, there is a lack of comprehensive guidelines for using antiplatelet agents in elective neurosurgery. Continuing therapy during surgery poses a bleeding risk, while discontinuing it before surgery increases the risk of thrombosis. Discontinuation is recommended in neurosurgical settings but carries an elevated risk of ischemic events. Conversely, maintaining antithrombotic therapy may increase bleeding and the need for transfusions, leading to a poor prognosis. Artificial intelligence (AI) holds promise in making difficult decisions regarding antiplatelet therapy. This paper discusses current clinical guidelines and supported regimens for antiplatelet therapy in neurosurgery. It also explores methodologies like P2Y12 reaction units (PRU) monitoring and thromboelastography (TEG) mapping for monitoring the use of antiplatelet regimens as well as their limitations. The paper explores the potential of AI to overcome such limitations associated with PRU monitoring and TEG mapping. It highlights various studies in the field of cardiovascular and neuroendovascular surgery which use AI prediction models to forecast adverse outcomes such as ischemia and bleeding, offering assistance in decision-making for antiplatelet therapy. In addition, the use of AI to improve patient adherence to antiplatelet regimens is also considered. Overall, this research aims to provide insights into the use of antiplatelet therapy and the role of AI in optimizing treatment plans in neurosurgical settings.
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Affiliation(s)
- Khushi Saigal
- College of Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Anmol Bharat Patel
- College of Medicine, University of Miami—Miller School of Medicine, Miami, FL 33136, USA;
| | - Brandon Lucke-Wold
- Department of Neurosurgery, University of Florida, Gainesville, FL 32608, USA
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Petersen NH, Sheth KN, Jha RM. Precision Medicine in Neurocritical Care for Cerebrovascular Disease Cases. Stroke 2023; 54:1392-1402. [PMID: 36789774 PMCID: PMC10348371 DOI: 10.1161/strokeaha.122.036402] [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: 04/16/2022] [Accepted: 12/22/2022] [Indexed: 02/16/2023]
Abstract
Scientific advances have informed many aspects of acute stroke care but have also highlighted the complexity and heterogeneity of cerebrovascular diseases. While practice guidelines are essential in supporting the clinical decision-making process, they may not capture the nuances of individual cases. Personalized stroke care in ICU has traditionally relied on integrating clinical examinations, neuroimaging studies, and physiologic monitoring to develop a treatment plan tailored to the individual patient. However, to realize the potential of precision medicine in stroke, we need advances and evidence in several critical areas, including data capture, clinical phenotyping, serum biomarker development, neuromonitoring, and physiology-based treatment targets. Mathematical tools are being developed to analyze the multitude of data and provide clinicians with real-time information and personalized treatment targets for the critical care management of patients with cerebrovascular diseases. This review summarizes research advances in these areas and outlines principles for translating precision medicine into clinical practice.
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Affiliation(s)
- Nils H Petersen
- Departments of Neurology (N.H.P., K.N.S., R.M.J.), Yale University School of Medicine, New Haven, CT
| | - Kevin N Sheth
- Departments of Neurology (N.H.P., K.N.S., R.M.J.), Yale University School of Medicine, New Haven, CT
- Neurosurgery (K.N.S., R.M.J.), Yale University School of Medicine, New Haven, CT
- Departments of Neurology, Neurosurgery and Translational Neuroscience, Barrow Neurological Institute, Phoenix, AZ (K.N.S., R.M.J.)
| | - Ruchira M Jha
- Departments of Neurology (N.H.P., K.N.S., R.M.J.), Yale University School of Medicine, New Haven, CT
- Neurosurgery (K.N.S., R.M.J.), Yale University School of Medicine, New Haven, CT
- Departments of Neurology, Neurosurgery and Translational Neuroscience, Barrow Neurological Institute, Phoenix, AZ (K.N.S., R.M.J.)
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Rehman S, Phan HT, Chandra RV, Gall S. Is sex a predictor for delayed cerebral ischaemia (DCI) and hydrocephalus after aneurysmal subarachnoid haemorrhage (aSAH)? A systematic review and meta-analysis. Acta Neurochir (Wien) 2023; 165:199-210. [PMID: 36333624 PMCID: PMC9840585 DOI: 10.1007/s00701-022-05399-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 09/19/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVES DCI and hydrocephalus are the most common complications that predict poor outcomes after aSAH. The relationship between sex, DCI and hydrocephalus are not well established; thus, we aimed to examine sex differences in DCI and hydrocephalus following aSAH in a systematic review and meta-analysis. METHODS A systematic search was conducted using the PubMed, Scopus and Medline databases from inception to August 2022 to identify cohort, case control, case series and clinical studies reporting sex and DCI, acute and chronic shunt-dependent hydrocephalus (SDHC). Random-effects meta-analysis was used to pool estimates for available studies. RESULTS There were 56 studies with crude estimates for DCI and meta-analysis showed that women had a greater risk for DCI than men (OR 1.24, 95% CI 1.11-1.39). The meta-analysis for adjusted estimates for 9 studies also showed an association between sex and DCI (OR 1.61, 95% CI 1.27-2.05). For acute hydrocephalus, only 9 studies were included, and meta-analysis of unadjusted estimates showed no association with sex (OR 0.95, 95%CI 0.78-1.16). For SDHC, a meta-analysis of crude estimates from 53 studies showed that women had a somewhat greater risk of developing chronic hydrocephalus compared to men (OR 1.14, 95% CI 0.99-1.31). In meta-analysis for adjusted estimates from 5 studies, no association of sex with SDHC was observed (OR 0.87, 95% CI 0.57-1.33). CONCLUSIONS Female sex is associated with the development of DCI; however, an association between sex and hydrocephalus was not detected. Strategies to target females to reduce the development of DCI may decrease overall morbidity and mortality after aSAH.
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Affiliation(s)
- Sabah Rehman
- Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia
| | - Hoang T Phan
- Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia
| | - Ronil V Chandra
- NeuroInterventional Radiology, Monash Health, Melbourne, VIC, Australia
- School of Clinical Sciences at Monash Health, Monash University, Melbourne, VIC, Australia
| | - Seana Gall
- Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia.
- Monash University, Melbourne, VIC, Australia.
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Megjhani M, Weiss M, Kwon SB, Ford J, Nametz D, Kastenholz N, Fogel H, Velazquez A, Roh D, Agarwal S, Connolly ES, Claassen J, Schubert GA, Park S. Vector Angle Analysis of Multimodal Neuromonitoring Data for Continuous Prediction of Delayed Cerebral Ischemia. Neurocrit Care 2022; 37:230-236. [DOI: 10.1007/s12028-022-01481-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 02/28/2022] [Indexed: 10/18/2022]
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Prediction and Risk Assessment Models for Subarachnoid Hemorrhage: A Systematic Review on Case Studies. BIOMED RESEARCH INTERNATIONAL 2022; 2022:5416726. [PMID: 35111845 PMCID: PMC8802084 DOI: 10.1155/2022/5416726] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 12/01/2021] [Accepted: 12/08/2021] [Indexed: 01/09/2023]
Abstract
Subarachnoid hemorrhage (SAH) is one of the major health issues known to society and has a higher mortality rate. The clinical factors with computed tomography (CT), magnetic resonance image (MRI), and electroencephalography (EEG) data were used to evaluate the performance of the developed method. In this paper, various methods such as statistical analysis, logistic regression, machine learning, and deep learning methods were used in the prediction and detection of SAH which are reviewed. The advantages and limitations of SAH prediction and risk assessment methods are also being reviewed. Most of the existing methods were evaluated on the collected dataset for the SAH prediction. In some researches, deep learning methods were applied, which resulted in higher performance in the prediction process. EEG data were applied in the existing methods for the prediction process, and these methods demonstrated higher performance. However, the existing methods have the limitations of overfitting problems, imbalance data problems, and lower efficiency in feature analysis. The artificial neural network (ANN) and support vector machine (SVM) methods have been applied for the prediction process, and considerably higher performance is achieved by using this method.
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Alexopoulos G, Zhang J, Karampelas I, Khan M, Quadri N, Patel M, Patel N, Almajali M, Mattei TA, Kemp J, Coppens J, Mercier P. Applied forecasting for delayed cerebral ischemia prediction post subarachnoid hemorrhage: Methodological fallacies. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2021.100817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
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13
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Artificial Intelligence: A Shifting Paradigm in Cardio-Cerebrovascular Medicine. J Clin Med 2021; 10:jcm10235710. [PMID: 34884412 PMCID: PMC8658222 DOI: 10.3390/jcm10235710] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 12/02/2021] [Indexed: 12/21/2022] Open
Abstract
The future of healthcare is an organic blend of technology, innovation, and human connection. As artificial intelligence (AI) is gradually becoming a go-to technology in healthcare to improve efficiency and outcomes, we must understand our limitations. We should realize that our goal is not only to provide faster and more efficient care, but also to deliver an integrated solution to ensure that the care is fair and not biased to a group of sub-population. In this context, the field of cardio-cerebrovascular diseases, which encompasses a wide range of conditions-from heart failure to stroke-has made some advances to provide assistive tools to care providers. This article aimed to provide an overall thematic review of recent development focusing on various AI applications in cardio-cerebrovascular diseases to identify gaps and potential areas of improvement. If well designed, technological engines have the potential to improve healthcare access and equitability while reducing overall costs, diagnostic errors, and disparity in a system that affects patients and providers and strives for efficiency.
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Danilov GV, Shifrin MA, Kotik KV, Ishankulov TA, Orlov YN, Kulikov AS, Potapov AA. Artificial Intelligence Technologies in Neurosurgery: a Systematic Literature Review Using Topic Modeling. Part II: Research Objectives and Perspectives. Sovrem Tekhnologii Med 2021; 12:111-118. [PMID: 34796024 PMCID: PMC8596229 DOI: 10.17691/stm2020.12.6.12] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Indexed: 12/29/2022] Open
Abstract
The current increase in the number of publications on the use of artificial intelligence (AI) technologies in neurosurgery indicates a new trend in clinical neuroscience. The aim of the study was to conduct a systematic literature review to highlight the main directions and trends in the use of AI in neurosurgery.
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Affiliation(s)
- G V Danilov
- Scientific Board Secretary; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4 Tverskaya-Yamskaya St., Moscow, 125047, Russia; Head of the Laboratory of Biomedical Informatics and Artificial Intelligence; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4 Tverskaya-Yamskaya St., Moscow, 125047, Russia
| | - M A Shifrin
- Scientific Consultant, Laboratory of Biomedical Informatics and Artificial Intelligence; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4 Tverskaya-Yamskaya St., Moscow, 125047, Russia
| | - K V Kotik
- Physics Engineer, Laboratory of Biomedical Informatics and Artificial Intelligence; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4 Tverskaya-Yamskaya St., Moscow, 125047, Russia
| | - T A Ishankulov
- Engineer, Laboratory of Biomedical Informatics and Artificial Intelligence; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4 Tverskaya-Yamskaya St., Moscow, 125047, Russia
| | - Yu N Orlov
- Head of the Department of Computational Physics and Kinetic Equations; Keldysh Institute of Applied Mathematics, Russian Academy of Sciences, 4 Miusskaya Sq., Moscow, 125047, Russia
| | - A S Kulikov
- Staff Anesthesiologist; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4 Tverskaya-Yamskaya St., Moscow, 125047, Russia
| | - A A Potapov
- Professor, Academician of the Russian Academy of Sciences, Chief Scientific Supervisor N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4 Tverskaya-Yamskaya St., Moscow, 125047, Russia
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de Jong G, Aquarius R, Sanaan B, Bartels RHMA, Grotenhuis JA, Henssen DJHA, Boogaarts HD. Prediction Models in Aneurysmal Subarachnoid Hemorrhage: Forecasting Clinical Outcome With Artificial Intelligence. Neurosurgery 2021; 88:E427-E434. [PMID: 33548918 DOI: 10.1093/neuros/nyaa581] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Accepted: 11/15/2020] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Predicting outcome after aneurysmal subarachnoid hemorrhage (aSAH) is known to be challenging and complex. Machine learning approaches, of which feedforward artificial neural networks (ffANNs) are the most widely used, could contribute to the patient-specific outcome prediction. OBJECTIVE To investigate the prediction capacity of an ffANN for the patient-specific clinical outcome and the occurrence of delayed cerebral ischemia (DCI) and compare those results with the predictions of 2 internationally used scoring systems. METHODS A prospective database was used to predict (1) death during hospitalization (ie, mortality) (n = 451), (2) unfavorable modified Rankin Scale (mRS) at 6 mo (n = 413), and (3) the occurrence of DCI (n = 362). Additionally, the predictive capacities of the ffANN were compared to those of Subarachnoid Haemorrhage International Trialists (SAHIT) and VASOGRADE to predict clinical outcome and occurrence of DCI. RESULTS The area under the curve (AUC) of the ffANN showed to be 88%, 85%, and 72% for predicting mortality, an unfavorable mRS, and the occurrence of DCI, respectively. Sensitivity/specificity rates of the ffANN for mortality, unfavorable mRS, and the occurrence of DCI were 82%/80%, 94%/80%, and 74%/68%. The ffANN and SAHIT calculator showed similar AUCs for predicting personalized outcome. The presented ffANN and VASOGRADE were found to perform equally with regard to personalized prediction of occurrence of DCI. CONCLUSION The presented ffANN showed equal performance when compared with VASOGRADE and SAHIT scoring systems while using less individual cases. The web interface launched simultaneously with the publication of this manuscript allows for usage of the ffANN-based prediction tool for individual data (https://nutshell-tool.com/).
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Affiliation(s)
- Guido de Jong
- Department of Neurosurgery, Radboud University Medical Center, Nijmegen, the Netherlands
| | - René Aquarius
- Department of Neurosurgery, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Barof Sanaan
- Department of Neurosurgery, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Ronald H M A Bartels
- Department of Neurosurgery, Radboud University Medical Center, Nijmegen, the Netherlands
| | - J André Grotenhuis
- Department of Neurosurgery, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Dylan J H A Henssen
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands.,Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Hieronymus D Boogaarts
- Department of Neurosurgery, Radboud University Medical Center, Nijmegen, the Netherlands
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16
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Katsuki M, Kawamura S, Koh A. Easily Created Prediction Model Using Automated Artificial Intelligence Framework (Prediction One, Sony Network Communications Inc., Tokyo, Japan) for Subarachnoid Hemorrhage Outcomes Treated by Coiling and Delayed Cerebral Ischemia. Cureus 2021; 13:e15695. [PMID: 34277282 PMCID: PMC8281789 DOI: 10.7759/cureus.15695] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/16/2021] [Indexed: 01/28/2023] Open
Abstract
Introduction Reliable prediction models of subarachnoid hemorrhage (SAH) outcomes and delayed cerebral ischemia (DCI) are needed to decide the treatment strategy. Automated artificial intelligence (AutoAI) is attractive, but there are few reports on AutoAI-based models for SAH functional outcomes and DCI. We herein made models using an AutoAI framework, Prediction One (Sony Network Communications Inc., Tokyo, Japan), and compared it to other previous statistical prediction scores. Methods We used an open dataset of 298 SAH patients, who were with non-severe neurological grade and treated by coiling. Modified Rankin Scale 0-3 at six months was defined as a favorable functional outcome and DCI occurrence as another outcome. We randomly divided them into a 248-patient training dataset and a 50-patient test dataset. Prediction One made the model using training dataset with 5-fold cross-validation. We evaluated the model using the test dataset and compared the area under the curves (AUCs) of the created models. Those of the modified SAFIRE score and the Fisher computed tomography (CT) scale to predict the outcomes. Results The AUCs of the AutoAI-based models for functional outcome in the training and test dataset were 0.994 and 0.801, and those for the DCI occurrence were 0.969 and 0.650. AUCs for functional outcome calculated using modified SAFIRE score were 0.844 and 0.892. Those for the DCI occurrence calculated using the Fisher CT scale were 0.577 and 0.544. Conclusions We easily and quickly made AutoAI-based prediction models. The models' AUCs were not inferior to the previous prediction models despite the easiness.
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Affiliation(s)
- Masahito Katsuki
- Department of Neurosurgery, Iwaki City Medical Center, Iwaki, JPN
- Department of Neurosurgery, Itoigawa General Hospital, Itoigawa, JPN
| | - Shin Kawamura
- Department of Neurosurgery, Itoigawa General Hospital, Itoigawa, JPN
| | - Akihito Koh
- Department of Neurosurgery, Itoigawa General Hospital, Itoigawa, JPN
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17
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Megjhani M, Terilli K, Weiss M, Savarraj J, Chen LH, Alkhachroum A, Roh DJ, Agarwal S, Connolly ES, Velazquez A, Boehme A, Claassen J, Choi HA, Schubert GA, Park S. Dynamic Detection of Delayed Cerebral Ischemia: A Study in 3 Centers. Stroke 2021; 52:1370-1379. [PMID: 33596676 DOI: 10.1161/strokeaha.120.032546] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
BACKGROUND AND PURPOSE Delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage negatively impacts long-term recovery but is often detected too late to prevent damage. We aim to develop hourly risk scores using routinely collected clinical data to detect DCI. METHODS A DCI classification model was trained using vital sign measurements (heart rate, blood pressure, respiratory rate, and oxygen saturation) and demographics routinely collected for clinical care. Twenty-two time-varying physiological measures were computed including mean, SD, and cross-correlation of heart rate time series with each of the other vitals. Classification was achieved using an ensemble approach with L2-regularized logistic regression, random forest, and support vector machines models. Classifier performance was determined by area under the receiver operating characteristic curves and confusion matrices. Hourly DCI risk scores were generated as the posterior probability at time t using the Ensemble classifier on cohorts recruited at 2 external institutions (n=38 and 40). RESULTS Three hundred ten patients were included in the training model (median, 54 years old [interquartile range, 45-65]; 80.2% women, 28.4% Hunt and Hess scale 4-5, 38.7% Modified Fisher Scale 3-4); 101 (33%) developed DCI with a median onset day 6 (interquartile range, 5-8). Classification accuracy before DCI onset was 0.83 (interquartile range, 0.76-0.83) area under the receiver operating characteristic curve. Risk scores applied to external institution datasets correctly predicted 64% and 91% of DCI events as early as 12 hours before clinical detection, with 2.7 and 1.6 true alerts for every false alert. CONCLUSIONS An hourly risk score for DCI derived from routine vital signs may have the potential to alert clinicians to DCI, which could reduce neurological injury.
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Affiliation(s)
- Murad Megjhani
- Department of Neurology (M.M., K.T., H.C., D.J.R., S.A., A.V., A.B., J.C., S.P.), Columbia University Irving Medical Center, New York
| | - Kalijah Terilli
- Department of Neurology (M.M., K.T., H.C., D.J.R., S.A., A.V., A.B., J.C., S.P.), Columbia University Irving Medical Center, New York
| | - Miriam Weiss
- Department of Neurosurgery, RWTH Aachen University, Germany (M.W., G.A.S.)
| | - Jude Savarraj
- Department of Neurology, McGovern Medical School, UT Health, Houston, TX (J.S., H.A.C.)
| | - Li Hui Chen
- Department of Neurology (M.M., K.T., H.C., D.J.R., S.A., A.V., A.B., J.C., S.P.), Columbia University Irving Medical Center, New York
| | | | - David J Roh
- Department of Neurology (M.M., K.T., H.C., D.J.R., S.A., A.V., A.B., J.C., S.P.), Columbia University Irving Medical Center, New York
| | - Sachin Agarwal
- Department of Neurology (M.M., K.T., H.C., D.J.R., S.A., A.V., A.B., J.C., S.P.), Columbia University Irving Medical Center, New York
| | - E Sander Connolly
- Department of Neurosurgery (E.S.C.), Columbia University Irving Medical Center, New York
| | - Angela Velazquez
- Department of Neurology (M.M., K.T., H.C., D.J.R., S.A., A.V., A.B., J.C., S.P.), Columbia University Irving Medical Center, New York
| | - Amelia Boehme
- Department of Neurology (M.M., K.T., H.C., D.J.R., S.A., A.V., A.B., J.C., S.P.), Columbia University Irving Medical Center, New York
| | - Jan Claassen
- Department of Neurology (M.M., K.T., H.C., D.J.R., S.A., A.V., A.B., J.C., S.P.), Columbia University Irving Medical Center, New York
| | - HuiMahn A Choi
- Department of Neurology, McGovern Medical School, UT Health, Houston, TX (J.S., H.A.C.)
| | - Gerrit A Schubert
- Department of Neurosurgery, RWTH Aachen University, Germany (M.W., G.A.S.)
| | - Soojin Park
- Department of Neurology (M.M., K.T., H.C., D.J.R., S.A., A.V., A.B., J.C., S.P.), Columbia University Irving Medical Center, New York
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18
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Savarraj JPJ, Hergenroeder GW, Zhu L, Chang T, Park S, Megjhani M, Vahidy FS, Zhao Z, Kitagawa RS, Choi HA. Machine Learning to Predict Delayed Cerebral Ischemia and Outcomes in Subarachnoid Hemorrhage. Neurology 2021; 96:e553-e562. [PMID: 33184232 PMCID: PMC7905786 DOI: 10.1212/wnl.0000000000011211] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Accepted: 09/21/2020] [Indexed: 01/22/2023] Open
Abstract
OBJECTIVE To determine whether machine learning (ML) algorithms can improve the prediction of delayed cerebral ischemia (DCI) and functional outcomes after subarachnoid hemorrhage (SAH). METHODS ML models and standard models (SMs) were trained to predict DCI and functional outcomes with data collected within 3 days of admission. Functional outcomes at discharge and at 3 months were quantified using the modified Rankin Scale (mRS) for neurologic disability (dichotomized as good [mRS ≤ 3] vs poor [mRS ≥ 4] outcomes). Concurrently, clinicians prospectively prognosticated 3-month outcomes of patients. The performance of ML, SMs, and clinicians were retrospectively compared. RESULTS DCI status, discharge, and 3-month outcomes were available for 399, 393, and 240 participants, respectively. Prospective clinician (an attending, a fellow, and a nurse) prognostication of 3-month outcomes was available for 90 participants. ML models yielded predictions with the following area under the receiver operating characteristic curve (AUC) scores: 0.75 ± 0.07 (95% confidence interval [CI] 0.64-0.84) for DCI, 0.85 ± 0.05 (95% CI 0.75-0.92) for discharge outcome, and 0.89 ± 0.03 (95% CI 0.81-0.94) for 3-month outcome. ML outperformed SMs, improving AUC by 0.20 (95% CI -0.02 to 0.4) for DCI, by 0.07 ± 0.03 (95% CI -0.0018 to 0.14) for discharge outcomes, and by 0.14 (95% CI 0.03-0.24) for 3-month outcomes and matched physician's performance in predicting 3-month outcomes. CONCLUSION ML models significantly outperform SMs in predicting DCI and functional outcomes and has the potential to improve SAH management.
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Affiliation(s)
- Jude P J Savarraj
- From the Departments of Neurosurgery (J.P.J.S., G.W.H., T.C., R.S.K., A.C.), Internal Medicine (L.Z.), and Neurology (F.S.V.), McGovern Medical School, Center for Precision Health, School of Biomedical Informatics (Z.Z.), and Human Genetics Center, School of Public Health (Z.Z.), The University of Texas Health Science Center at Houston; and Department of Neurology (S.P., M.M.), Columbia University, NY
| | - Georgene W Hergenroeder
- From the Departments of Neurosurgery (J.P.J.S., G.W.H., T.C., R.S.K., A.C.), Internal Medicine (L.Z.), and Neurology (F.S.V.), McGovern Medical School, Center for Precision Health, School of Biomedical Informatics (Z.Z.), and Human Genetics Center, School of Public Health (Z.Z.), The University of Texas Health Science Center at Houston; and Department of Neurology (S.P., M.M.), Columbia University, NY
| | - Liang Zhu
- From the Departments of Neurosurgery (J.P.J.S., G.W.H., T.C., R.S.K., A.C.), Internal Medicine (L.Z.), and Neurology (F.S.V.), McGovern Medical School, Center for Precision Health, School of Biomedical Informatics (Z.Z.), and Human Genetics Center, School of Public Health (Z.Z.), The University of Texas Health Science Center at Houston; and Department of Neurology (S.P., M.M.), Columbia University, NY
| | - Tiffany Chang
- From the Departments of Neurosurgery (J.P.J.S., G.W.H., T.C., R.S.K., A.C.), Internal Medicine (L.Z.), and Neurology (F.S.V.), McGovern Medical School, Center for Precision Health, School of Biomedical Informatics (Z.Z.), and Human Genetics Center, School of Public Health (Z.Z.), The University of Texas Health Science Center at Houston; and Department of Neurology (S.P., M.M.), Columbia University, NY
| | - Soojin Park
- From the Departments of Neurosurgery (J.P.J.S., G.W.H., T.C., R.S.K., A.C.), Internal Medicine (L.Z.), and Neurology (F.S.V.), McGovern Medical School, Center for Precision Health, School of Biomedical Informatics (Z.Z.), and Human Genetics Center, School of Public Health (Z.Z.), The University of Texas Health Science Center at Houston; and Department of Neurology (S.P., M.M.), Columbia University, NY
| | - Murad Megjhani
- From the Departments of Neurosurgery (J.P.J.S., G.W.H., T.C., R.S.K., A.C.), Internal Medicine (L.Z.), and Neurology (F.S.V.), McGovern Medical School, Center for Precision Health, School of Biomedical Informatics (Z.Z.), and Human Genetics Center, School of Public Health (Z.Z.), The University of Texas Health Science Center at Houston; and Department of Neurology (S.P., M.M.), Columbia University, NY
| | - Farhaan S Vahidy
- From the Departments of Neurosurgery (J.P.J.S., G.W.H., T.C., R.S.K., A.C.), Internal Medicine (L.Z.), and Neurology (F.S.V.), McGovern Medical School, Center for Precision Health, School of Biomedical Informatics (Z.Z.), and Human Genetics Center, School of Public Health (Z.Z.), The University of Texas Health Science Center at Houston; and Department of Neurology (S.P., M.M.), Columbia University, NY
| | - Zhongming Zhao
- From the Departments of Neurosurgery (J.P.J.S., G.W.H., T.C., R.S.K., A.C.), Internal Medicine (L.Z.), and Neurology (F.S.V.), McGovern Medical School, Center for Precision Health, School of Biomedical Informatics (Z.Z.), and Human Genetics Center, School of Public Health (Z.Z.), The University of Texas Health Science Center at Houston; and Department of Neurology (S.P., M.M.), Columbia University, NY
| | - Ryan S Kitagawa
- From the Departments of Neurosurgery (J.P.J.S., G.W.H., T.C., R.S.K., A.C.), Internal Medicine (L.Z.), and Neurology (F.S.V.), McGovern Medical School, Center for Precision Health, School of Biomedical Informatics (Z.Z.), and Human Genetics Center, School of Public Health (Z.Z.), The University of Texas Health Science Center at Houston; and Department of Neurology (S.P., M.M.), Columbia University, NY
| | - H Alex Choi
- From the Departments of Neurosurgery (J.P.J.S., G.W.H., T.C., R.S.K., A.C.), Internal Medicine (L.Z.), and Neurology (F.S.V.), McGovern Medical School, Center for Precision Health, School of Biomedical Informatics (Z.Z.), and Human Genetics Center, School of Public Health (Z.Z.), The University of Texas Health Science Center at Houston; and Department of Neurology (S.P., M.M.), Columbia University, NY.
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Sahinovic MM, Vos JJ, Scheeren TWL. Journal of Clinical Monitoring and Computing 2019 end of year summary: monitoring tissue oxygenation and perfusion and its autoregulation. J Clin Monit Comput 2020; 34:389-395. [PMID: 32277310 PMCID: PMC7205776 DOI: 10.1007/s10877-020-00504-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Accepted: 03/25/2020] [Indexed: 12/30/2022]
Abstract
Tissue perfusion monitoring is increasingly being employed clinically in a non-invasive fashion. In this end-of-year summary of the Journal of Clinical Monitoring and Computing, we take a closer look at the papers published recently on this subject in the journal. Most of these papers focus on monitoring cerebral perfusion (and associated hemodynamics), using either transcranial doppler measurements or near-infrared spectroscopy. Given the importance of cerebral autoregulation in the analyses performed in most of the studies discussed here, this end-of-year summary also includes a short description of cerebral hemodynamic physiology and its autoregulation. Finally, we review articles on somatic tissue oxygenation and its possible association with outcome.
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Affiliation(s)
- M M Sahinovic
- Department of Anesthesiology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO Box 30001, 9700RB, Groningen, Netherlands
| | - J J Vos
- Department of Anesthesiology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO Box 30001, 9700RB, Groningen, Netherlands
| | - T W L Scheeren
- Department of Anesthesiology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO Box 30001, 9700RB, Groningen, Netherlands.
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20
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21
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Yavuz TT, Claassen J, Kleinberg S. Lagged Correlations among Physiological Variables as Indicators of Consciousness in Stroke Patients. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2020; 2019:942-951. [PMID: 32308891 PMCID: PMC7153151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Consciousness is a highly significant indicator of an ICU patient's condition but there is still no method to automatically measure it. Instead, time consuming and subjective assessments are used. However, many brain and physiologic variables are measured continuously in neurological ICU, and could be used as indicators for consciousness. Since many biological variables are highly correlated to maintain homeostasis, we examine whether changes in time lags between correlated variables may relate to changes in consciousness. We introduce new methods to identify changes in the time lag of correlations, which better handle noisy multimodal physiological data and fluctuating lags. On neurological ICU data from subarachnoid hemorrhage patients, we find that correlations among variables related to brain physiology or respiration have significantly longer lags inpatients with decreased levels of consciousness than in patients with higher levels of consciousness. This suggests that physiological data could potentially be used to automatically assess consciousness.
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22
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Bennis FC, Teeuwen B, Zeiler FA, Elting JW, van der Naalt J, Bonizzi P, Delhaas T, Aries MJ. Improving Prediction of Favourable Outcome After 6 Months in Patients with Severe Traumatic Brain Injury Using Physiological Cerebral Parameters in a Multivariable Logistic Regression Model. Neurocrit Care 2020; 33:542-551. [PMID: 32056131 PMCID: PMC7505885 DOI: 10.1007/s12028-020-00930-6] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Background/Objective Current severe traumatic brain injury (TBI) outcome prediction models calculate the chance of unfavourable outcome after 6 months based on parameters measured at admission. We aimed to improve current models with the addition of continuously measured neuromonitoring data within the first 24 h after intensive care unit neuromonitoring. Methods Forty-five severe TBI patients with intracranial pressure/cerebral perfusion pressure monitoring from two teaching hospitals covering the period May 2012 to January 2019 were analysed. Fourteen high-frequency physiological parameters were selected over multiple time periods after the start of neuromonitoring (0–6 h, 0–12 h, 0–18 h, 0–24 h). Besides systemic physiological parameters and extended Corticosteroid Randomisation after Significant Head Injury (CRASH) score, we added estimates of (dynamic) cerebral volume, cerebral compliance and cerebrovascular pressure reactivity indices to the model. A logistic regression model was trained for each time period on selected parameters to predict outcome after 6 months. The parameters were selected using forward feature selection. Each model was validated by leave-one-out cross-validation. Results A logistic regression model using CRASH as the sole parameter resulted in an area under the curve (AUC) of 0.76. For each time period, an increased AUC was found using up to 5 additional parameters. The highest AUC (0.90) was found for the 0–6 h period using 5 parameters that describe mean arterial blood pressure and physiological cerebral indices. Conclusions Current TBI outcome prediction models can be improved by the addition of neuromonitoring bedside parameters measured continuously within the first 24 h after the start of neuromonitoring. As these factors might be modifiable by treatment during the admission, testing in a larger (multicenter) data set is warranted. Electronic supplementary material The online version of this article (10.1007/s12028-020-00930-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Frank C Bennis
- Department of Biomedical Engineering, Maastricht University, PO Box 616, 6200 MD, Maastricht, The Netherlands. .,MHeNS School for Mental Health and Neuroscience, Maastricht University, PO Box 616, 6200 MD, Maastricht, The Netherlands. .,CARIM School for Cardiovascular Diseases, Maastricht University, PO Box 616, 6200 MD, Maastricht, The Netherlands.
| | - Bibi Teeuwen
- Department of Biomedical Engineering, Maastricht University, PO Box 616, 6200 MD, Maastricht, The Netherlands
| | - Frederick A Zeiler
- Section of Neurosurgery, Department of Surgery, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Canada.,Department of Human Anatomy and Cell Science, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Canada.,Biomedical Engineering, Faculty of Engineering, University of Manitoba, Winnipeg, Canada.,Division of Anaesthesia, Department of Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
| | - Jan Willem Elting
- Department of Clinical Neurophysiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.,Department of Neurology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Joukje van der Naalt
- Department of Neurology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Pietro Bonizzi
- Department of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands
| | - Tammo Delhaas
- Department of Biomedical Engineering, Maastricht University, PO Box 616, 6200 MD, Maastricht, The Netherlands.,CARIM School for Cardiovascular Diseases, Maastricht University, PO Box 616, 6200 MD, Maastricht, The Netherlands
| | - Marcel J Aries
- MHeNS School for Mental Health and Neuroscience, Maastricht University, PO Box 616, 6200 MD, Maastricht, The Netherlands.,Department of Intensive Care, Maastricht University Medical Center, Maastricht University, Maastricht, The Netherlands
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Placek MM, Smielewski P, Wachel P, Budohoski KP, Czosnyka M, Kasprowicz M. Can interhemispheric desynchronization of cerebral blood flow anticipate upcoming vasospasm in aneurysmal subarachnoid haemorrhage patients? J Neurosci Methods 2019; 325:108358. [PMID: 31306719 DOI: 10.1016/j.jneumeth.2019.108358] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2018] [Revised: 06/12/2019] [Accepted: 07/11/2019] [Indexed: 11/24/2022]
Abstract
BACKGROUND Asymmetry of cerebral autoregulation (CA) was demonstrated in patients after aneurysmal subarachnoid haemorrhage (aSAH). A classical method for CA assessment requires simultaneous measurement of both arterial blood pressure (ABP) and cerebral blood flow velocity (CBFV). In this study, we have proposed a cerebral blood flow asymmetry index based only on CBFV and analysed its association with the occurrence of vasospasm after aSAH. NEW METHOD The phase shifts (PS) between slow oscillations in left and right CBFV (side-to-side PS) and between ABP and CBFV (CBFV-ABP PS) were estimated using multichannel matching pursuit (MMP) and cross-spectral analysis. RESULTS We retrospectively analysed data collected from 45 aSAH patients (26 with vasospasm). Data were analysed up to 7th day after aSAH unless the vasospasm was detected earlier. A progressive asymmetry, manifested by a gradual increase in side-to-side PS on consecutive days after aSAH, was observed in patients who developed vasospasm (Radj2 = 0.14, p = 0.009). In these patients, early side-to-side PS was more positive than in patients without vasospasm (2.8° ± 5.6° vs -1.7° ± 5.7°, p = 0.011). No such a difference was found in CBFV-ABP PS. Patients with positive side-to-side PS were more likely to develop vasospasm than patients with negative side-to-side PS (21/7 vs 5/12, p = 0.0047). COMPARISON WITH EXISTING METHOD MMP, in contrast to the spectral approach, accounts for non-stationarity of analysed signals. MMP applied to the PS estimation reflects the cerebral blood flow asymmetry in aSAH better than the spectral analysis. CONCLUSIONS Changes in side-to-side PS might be helpful to identify patients who are at risk of vasospasm.
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Affiliation(s)
- Michał M Placek
- Department of Biomedical Engineering, Faculty of Fundamental Problems of Technology, Wrocław University of Science and Technology, Wybrzeże S. Wyspiańskiego 27, 50-370 Wrocław, Poland; Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Hills Road, Cambridge CB2 0QQ, United Kingdom.
| | - Peter Smielewski
- Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Hills Road, Cambridge CB2 0QQ, United Kingdom
| | - Paweł Wachel
- Department of Control Systems and Mechatronics, Faculty of Electronics, Wrocław University of Science and Technology, Wybrzeże S. Wyspiańskiego 27, 50-370 Wrocław, Poland
| | - Karol P Budohoski
- Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Hills Road, Cambridge CB2 0QQ, United Kingdom
| | - Marek Czosnyka
- Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Hills Road, Cambridge CB2 0QQ, United Kingdom
| | - Magdalena Kasprowicz
- Department of Biomedical Engineering, Faculty of Fundamental Problems of Technology, Wrocław University of Science and Technology, Wybrzeże S. Wyspiańskiego 27, 50-370 Wrocław, Poland
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