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Chen S, Yang X, Gu H, Wang Y, Xu Z, Jiang Y, Wang Y. Predictive etiological classification of acute ischemic stroke through interpretable machine learning algorithms: a multicenter, prospective cohort study. BMC Med Res Methodol 2024; 24:199. [PMID: 39256656 PMCID: PMC11384709 DOI: 10.1186/s12874-024-02331-1] [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: 11/24/2023] [Accepted: 09/05/2024] [Indexed: 09/12/2024] Open
Abstract
BACKGROUND The prognosis, recurrence rates, and secondary prevention strategies varied significantly among different subtypes of acute ischemic stroke (AIS). Machine learning (ML) techniques can uncover intricate, non-linear relationships within medical data, enabling the identification of factors associated with etiological classification. However, there is currently a lack of research utilizing ML algorithms for predicting AIS etiology. OBJECTIVE We aimed to use interpretable ML algorithms to develop AIS etiology prediction models, identify critical factors in etiology classification, and enhance existing clinical categorization. METHODS This study involved patients with the Third China National Stroke Registry (CNSR-III). Nine models, which included Natural Gradient Boosting (NGBoost), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), Random Forest (RF), Light Gradient Boosting Machine (LGBM), Gradient Boosting Decision Tree (GBDT), Adaptive Boosting (AdaBoost), Support Vector Machine (SVM), and logistic regression (LR), were employed to predict large artery atherosclerosis (LAA), small vessel occlusion (SVO), and cardioembolism (CE) using an 80:20 randomly split training and test set. We designed an SFS-XGB with 10-fold cross-validation for feature selection. The primary evaluation metrics for the models included the area under the receiver operating characteristic curve (AUC) for discrimination and the Brier score (or calibration plots) for calibration. RESULTS A total of 5,213 patients were included, comprising 2,471 (47.4%) with LAA, 2,153 (41.3%) with SVO, and 589 (11.3%) with CE. In both LAA and SVO models, the AUC values of the ML models were significantly higher than that of the LR model (P < 0.001). The optimal model for predicting SVO (AUC [RF model] = 0.932) outperformed the optimal LAA model (AUC [NGB model] = 0.917) and the optimal CE model (AUC [LGBM model] = 0.846). Each model displayed relatively satisfactory calibration. Further analysis showed that the optimal CE model could identify potential CE patients in the undetermined etiology (SUE) group, accounting for 1,900 out of 4,156 (45.7%). CONCLUSIONS The ML algorithm effectively classified patients with LAA, SVO, and CE, demonstrating superior classification performance compared to the LR model. The optimal ML model can identify potential CE patients among SUE patients. These newly identified predictive factors may complement the existing etiological classification system, enabling clinicians to promptly categorize stroke patients' etiology and initiate optimal strategies for secondary prevention.
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Affiliation(s)
- Siding Chen
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, No.119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China
- China National Clinical Research Center for Neurological Diseases, No.119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China
- Changping Laboratory, Beijing, China
| | - Xiaomeng Yang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, No.119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China
| | - Hongqiu Gu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, No.119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China
- China National Clinical Research Center for Neurological Diseases, No.119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China
| | - Yanzhao Wang
- School of Statistics, Renmin University of China, No. 59 Zhongguancun Street, Haidian District, Beijing, 100872, China
| | - Zhe Xu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, No.119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China
- China National Clinical Research Center for Neurological Diseases, No.119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China
| | - Yong Jiang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, No.119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China.
- China National Clinical Research Center for Neurological Diseases, No.119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China.
- Changping Laboratory, Beijing, China.
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University & Capital Medical University, Beijing, 100091, China.
| | - Yongjun Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, No.119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China.
- China National Clinical Research Center for Neurological Diseases, No.119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China.
- Changping Laboratory, Beijing, China.
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
- Clinical Center for Precision Medicine in Stroke, Capital Medical University, Beijing, China.
- Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.
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Micali G, Corallo F, Pagano M, Giambò FM, Duca A, D’Aleo P, Anselmo A, Bramanti A, Garofano M, Mazzon E, Bramanti P, Cappadona I. Artificial Intelligence and Heart-Brain Connections: A Narrative Review on Algorithms Utilization in Clinical Practice. Healthcare (Basel) 2024; 12:1380. [PMID: 39057522 PMCID: PMC11276532 DOI: 10.3390/healthcare12141380] [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: 06/18/2024] [Revised: 07/04/2024] [Accepted: 07/08/2024] [Indexed: 07/28/2024] Open
Abstract
Cardiovascular and neurological diseases are a major cause of mortality and morbidity worldwide. Such diseases require careful monitoring to effectively manage their progression. Artificial intelligence (AI) offers valuable tools for this purpose through its ability to analyse data and identify predictive patterns. This review evaluated the application of AI in cardiac and neurological diseases for their clinical impact on the general population. We reviewed studies on the application of AI in the neurological and cardiological fields. Our search was performed on the PubMed, Web of Science, Embase and Cochrane library databases. Of the initial 5862 studies, 23 studies met the inclusion criteria. The studies showed that the most commonly used algorithms in these clinical fields are Random Forest and Artificial Neural Network, followed by logistic regression and Support-Vector Machines. In addition, an ECG-AI algorithm based on convolutional neural networks has been developed and has been widely used in several studies for the detection of atrial fibrillation with good accuracy. AI has great potential to support physicians in interpretation, diagnosis, risk assessment and disease management.
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Affiliation(s)
- Giuseppe Micali
- IRCCS Centro Neurolesi Bonino-Pulejo, Via Palermo, S.S. 113, C.da Casazza, 98124 Messina, Italy; (G.M.)
| | - Francesco Corallo
- IRCCS Centro Neurolesi Bonino-Pulejo, Via Palermo, S.S. 113, C.da Casazza, 98124 Messina, Italy; (G.M.)
| | - Maria Pagano
- IRCCS Centro Neurolesi Bonino-Pulejo, Via Palermo, S.S. 113, C.da Casazza, 98124 Messina, Italy; (G.M.)
| | - Fabio Mauro Giambò
- IRCCS Centro Neurolesi Bonino-Pulejo, Via Palermo, S.S. 113, C.da Casazza, 98124 Messina, Italy; (G.M.)
| | - Antonio Duca
- IRCCS Centro Neurolesi Bonino-Pulejo, Via Palermo, S.S. 113, C.da Casazza, 98124 Messina, Italy; (G.M.)
| | - Piercataldo D’Aleo
- IRCCS Centro Neurolesi Bonino-Pulejo, Via Palermo, S.S. 113, C.da Casazza, 98124 Messina, Italy; (G.M.)
| | - Anna Anselmo
- IRCCS Centro Neurolesi Bonino-Pulejo, Via Palermo, S.S. 113, C.da Casazza, 98124 Messina, Italy; (G.M.)
| | - Alessia Bramanti
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
| | - Marina Garofano
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
| | - Emanuela Mazzon
- IRCCS Centro Neurolesi Bonino-Pulejo, Via Palermo, S.S. 113, C.da Casazza, 98124 Messina, Italy; (G.M.)
| | - Placido Bramanti
- IRCCS Centro Neurolesi Bonino-Pulejo, Via Palermo, S.S. 113, C.da Casazza, 98124 Messina, Italy; (G.M.)
- Faculty of Psychology, Università degli Studi eCampus, Via Isimbardi 10, 22060 Novedrate, Italy
| | - Irene Cappadona
- IRCCS Centro Neurolesi Bonino-Pulejo, Via Palermo, S.S. 113, C.da Casazza, 98124 Messina, Italy; (G.M.)
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Lee HJ, Schwamm LH, Sansing LH, Kamel H, de Havenon A, Turner AC, Sheth KN, Krishnaswamy S, Brandt C, Zhao H, Krumholz H, Sharma R. StrokeClassifier: ischemic stroke etiology classification by ensemble consensus modeling using electronic health records. NPJ Digit Med 2024; 7:130. [PMID: 38760474 PMCID: PMC11101464 DOI: 10.1038/s41746-024-01120-w] [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: 10/02/2023] [Accepted: 04/23/2024] [Indexed: 05/19/2024] Open
Abstract
Determining acute ischemic stroke (AIS) etiology is fundamental to secondary stroke prevention efforts but can be diagnostically challenging. We trained and validated an automated classification tool, StrokeClassifier, using electronic health record (EHR) text from 2039 non-cryptogenic AIS patients at 2 academic hospitals to predict the 4-level outcome of stroke etiology adjudicated by agreement of at least 2 board-certified vascular neurologists' review of the EHR. StrokeClassifier is an ensemble consensus meta-model of 9 machine learning classifiers applied to features extracted from discharge summary texts by natural language processing. StrokeClassifier was externally validated in 406 discharge summaries from the MIMIC-III dataset reviewed by a vascular neurologist to ascertain stroke etiology. Compared with vascular neurologists' diagnoses, StrokeClassifier achieved the mean cross-validated accuracy of 0.74 and weighted F1 of 0.74 for multi-class classification. In MIMIC-III, its accuracy and weighted F1 were 0.70 and 0.71, respectively. In binary classification, the two metrics ranged from 0.77 to 0.96. The top 5 features contributing to stroke etiology prediction were atrial fibrillation, age, middle cerebral artery occlusion, internal carotid artery occlusion, and frontal stroke location. We designed a certainty heuristic to grade the confidence of StrokeClassifier's diagnosis as non-cryptogenic by the degree of consensus among the 9 classifiers and applied it to 788 cryptogenic patients, reducing cryptogenic diagnoses from 25.2% to 7.2%. StrokeClassifier is a validated artificial intelligence tool that rivals the performance of vascular neurologists in classifying ischemic stroke etiology. With further training, StrokeClassifier may have downstream applications including its use as a clinical decision support system.
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Affiliation(s)
- Ho-Joon Lee
- Department of Genetics and Yale Center for Genome Analysis, Yale School of Medicine, New Haven, CT, USA.
| | - Lee H Schwamm
- Department of Neurology and Comprehensive Stroke Center, Massachusetts General Hospital and Harvard Medical School Boston, Boston, MA, USA
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Lauren H Sansing
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Hooman Kamel
- Department of Neurology, Weill Cornell Medicine, New York City, NY, USA
| | - Adam de Havenon
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Ashby C Turner
- Department of Neurology and Comprehensive Stroke Center, Massachusetts General Hospital and Harvard Medical School Boston, Boston, MA, USA
| | - Kevin N Sheth
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Smita Krishnaswamy
- Departments of Genetics and Computer Science, Yale School of Medicine, New Haven, CT, USA
| | - Cynthia Brandt
- Department of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA
| | - Hongyu Zhao
- Departments of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Harlan Krumholz
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Richa Sharma
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA.
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Ryu WS, Schellingerhout D, Lee H, Lee KJ, Kim CK, Kim BJ, Chung JW, Lim JS, Kim JT, Kim DH, Cha JK, Sunwoo L, Kim D, Suh SI, Bang OY, Bae HJ, Kim DE. Deep Learning-Based Automatic Classification of Ischemic Stroke Subtype Using Diffusion-Weighted Images. J Stroke 2024; 26:300-311. [PMID: 38836277 PMCID: PMC11164582 DOI: 10.5853/jos.2024.00535] [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: 02/06/2024] [Revised: 04/04/2024] [Accepted: 05/07/2024] [Indexed: 06/06/2024] Open
Abstract
BACKGROUND AND PURPOSE Accurate classification of ischemic stroke subtype is important for effective secondary prevention of stroke. We used diffusion-weighted image (DWI) and atrial fibrillation (AF) data to train a deep learning algorithm to classify stroke subtype. METHODS Model development was done in 2,988 patients with ischemic stroke from three centers by using U-net for infarct segmentation and EfficientNetV2 for subtype classification. Experienced neurologists (n=5) determined subtypes for external test datasets, while establishing a consensus for clinical trial datasets. Automatically segmented infarcts were fed into the model (DWI-only algorithm). Subsequently, another model was trained, with AF included as a categorical variable (DWI+AF algorithm). These models were tested: (1) internally against the opinion of the labeling experts, (2) against fresh external DWI data, and (3) against clinical trial dataset. RESULTS In the training-and-validation datasets, the mean (±standard deviation) age was 68.0±12.5 (61.1% male). In internal testing, compared with the experts, the DWI-only and the DWI+AF algorithms respectively achieved moderate (65.3%) and near-strong (79.1%) agreement. In external testing, both algorithms again showed good agreements (59.3%-60.7% and 73.7%-74.0%, respectively). In the clinical trial dataset, compared with the expert consensus, percentage agreements and Cohen's kappa were respectively 58.1% and 0.34 for the DWI-only vs. 72.9% and 0.57 for the DWI+AF algorithms. The corresponding values between experts were comparable (76.0% and 0.61) to the DWI+AF algorithm. CONCLUSION Our model trained on a large dataset of DWI (both with or without AF information) was able to classify ischemic stroke subtypes comparable to a consensus of stroke experts.
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Affiliation(s)
- Wi-Sun Ryu
- Department of Neurology, Dongguk University Ilsan Hospital, Goyang, Korea
- Artificial Intelligence Research Center, JLK Inc., Seoul, Korea
| | - Dawid Schellingerhout
- Department of Neuroradiology and Imaging Physics, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
| | - Hoyoun Lee
- Artificial Intelligence Research Center, JLK Inc., Seoul, Korea
| | - Keon-Joo Lee
- Department of Neurology, Korea University Guro Hospital, Seoul, Korea
| | - Chi Kyung Kim
- Department of Neurology, Korea University Guro Hospital, Seoul, Korea
| | - Beom Joon Kim
- Department of Neurology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Jong-Won Chung
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Jae-Sung Lim
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Joon-Tae Kim
- Department of Neurology, Chonnam National University Hospital, Chonnam National University Medical School, Gwangju, Korea
| | - Dae-Hyun Kim
- Department of Neurology, Dong-A University Hospital, Busan, Korea
| | - Jae-Kwan Cha
- Department of Neurology, Dong-A University Hospital, Busan, Korea
| | - Leonard Sunwoo
- Department of Radiology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Dongmin Kim
- Artificial Intelligence Research Center, JLK Inc., Seoul, Korea
| | - Sang-Il Suh
- Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Korea
| | - Oh Young Bang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Hee-Joon Bae
- Department of Neurology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Dong-Eog Kim
- Department of Neurology, Dongguk University Ilsan Hospital, Goyang, Korea
- National Priority Research Center for Stroke, Goyang, Korea
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5
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Saba L, Cau R, Spinato G, Suri JS, Melis M, De Rubeis G, Antignani P, Gupta A. Carotid stenosis and cryptogenic stroke. J Vasc Surg 2024; 79:1119-1131. [PMID: 38190926 DOI: 10.1016/j.jvs.2024.01.004] [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: 12/13/2023] [Revised: 12/30/2023] [Accepted: 01/04/2024] [Indexed: 01/10/2024]
Abstract
OBJECTIVES Cryptogenic stroke represents a type of ischemic stroke with an unknown origin, presenting a significant challenge in both stroke management and prevention. According to the Trial of Org 10,172 in Acute Stroke Treatment criteria, a stroke is categorized as being caused by large artery atherosclerosis only when there is >50% luminal narrowing of the ipsilateral internal carotid artery. However, nonstenosing carotid artery plaques can be an underlying cause of ischemic stroke. Indeed, emerging evidence documents that some features of plaque vulnerability may act as an independent risk factor, regardless of the degree of stenosis, in precipitating cerebrovascular events. This review, drawing from an array of imaging-based studies, explores the predictive values of carotid imaging modalities in the detection of nonstenosing carotid plaque (<50%), that could be the cause of a cerebrovascular event when some features of vulnerability are present. METHODS Google Scholar, Scopus, and PubMed were searched for articles on cryptogenic stroke and those reporting the association between cryptogenic stroke and imaging features of carotid plaque vulnerability. RESULTS Despite extensive diagnostic evaluations, the etiology of a considerable proportion of strokes remains undetermined, contributing to the recurrence rate and persistent morbidity in affected individuals. Advances in imaging modalities, such as magnetic resonance imaging, computed tomography scans, and ultrasound examination, facilitate more accurate detection of nonstenosing carotid artery plaque and allow better stratification of stroke risk, leading to a more tailored treatment strategy. CONCLUSIONS Early detection of nonstenosing carotid plaque with features of vulnerability through carotid imaging techniques impacts the clinical management of cryptogenic stroke, resulting in refined stroke subtype classification and improved patient management. Additional research is required to validate these findings and recommend the integration of these state-of-the-art imaging methodologies into standard diagnostic protocols to improve stroke management and prevention.
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Affiliation(s)
- Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy.
| | - Riccardo Cau
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy
| | - Giacomo Spinato
- Department of Neurosciences, Section of Otolaryngology and Regional Centre for Head and Neck Cancer, University of Padova, Treviso, Italy
| | - Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA
| | - Marta Melis
- Department of Neurology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy
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Khaliq M, Shaikh I, Soman S. Editorial on an autoencoder algorithm for the prediction of stroke patients with left ventricular thrombus (LVT). J Neurol Sci 2024; 458:122928. [PMID: 38367487 DOI: 10.1016/j.jns.2024.122928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 02/06/2024] [Indexed: 02/19/2024]
Affiliation(s)
- Muhammad Khaliq
- Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Ibraheem Shaikh
- Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Salil Soman
- Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.
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Fleitmann M, Uzunova H, Pallenberg R, Stroth AM, Gerlach J, Fürschke A, Barkhausen J, Bischof A, Handels H. Artificial Intelligence-Based Prediction of Contrast Medium Doses for Computed Tomography Angiography Using Optimized Clinical Parameter Sets. Methods Inf Med 2024. [PMID: 38262476 DOI: 10.1055/s-0044-1778694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2024]
Abstract
OBJECTIVES In this paper, an artificial intelligence-based algorithm for predicting the optimal contrast medium dose for computed tomography (CT) angiography of the aorta is presented and evaluated in a clinical study. The prediction of the contrast dose reduction is modelled as a classification problem using the image contrast as the main feature. METHODS This classification is performed by random decision forests (RDF) and k-nearest-neighbor methods (KNN). For the selection of optimal parameter subsets all possible combinations of the 22 clinical parameters (age, blood pressure, etc.) are considered using the classification accuracy and precision of the KNN classifier and RDF as quality criteria. Subsequently, the results of the evaluation were optimized by means of feature transformation using regression neural networks (RNN). These were used for a direct classification based on regressed Hounsfield units as well as preprocessing for a subsequent KNN classification. RESULTS For feature selection, an RDF model achieved the highest accuracy of 84.42% and a KNN model achieved the best precision of 86.21%. The most important parameters include age, height, and hemoglobin. The feature transformation using an RNN considerably exceeded these values with an accuracy of 90.00% and a precision of 97.62% using all 22 parameters as input. However, also the feasibility of the parameter sets in routine clinical practice has to be considered, because some of the 22 parameters are not measured in routine clinical practice and additional measurement time of 15 to 20 minutes per patient is needed. Using the standard feature set available in clinical routine the best accuracy of 86.67% and precision of 93.18% was achieved by the RNN. CONCLUSION We developed a reliable hybrid system that helps radiologists determine the optimal contrast dose for CT angiography based on patient-specific parameters.
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Affiliation(s)
- Marja Fleitmann
- Artificial Intelligence in Medical Imaging, German Research Center for Artificial Intelligence, Kaiserslautern, Germany
| | - Hristina Uzunova
- Artificial Intelligence in Medical Imaging, German Research Center for Artificial Intelligence, Kaiserslautern, Germany
| | - René Pallenberg
- Institute for Signal Processing, University of Lübeck, Schleswig-Holstein, Germany
| | - Andreas M Stroth
- Department of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein (UKSH) Lübeck, Lübeck, Germany
| | - Jan Gerlach
- Department of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein (UKSH) Lübeck, Lübeck, Germany
| | - Alexander Fürschke
- Department of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein (UKSH) Lübeck, Lübeck, Germany
| | - Jörg Barkhausen
- Department of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein (UKSH) Lübeck, Lübeck, Germany
| | - Arpad Bischof
- Department of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein (UKSH) Lübeck, Lübeck, Germany
- IMAGE Information Systems Europe, Rostock, Germany
| | - Heinz Handels
- Artificial Intelligence in Medical Imaging, German Research Center for Artificial Intelligence, Kaiserslautern, Germany
- Institute of Medical Informatics, University of Lübeck, Schleswig-Holstein, Germany
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Lee HJ, Schwamm LH, Sansing L, Kamel H, de Havenon A, Turner AC, Sheth KN, Krishnaswamy S, Brandt C, Zhao H, Krumholz H, Sharma R. StrokeClassifier: Ischemic Stroke Etiology Classification by Ensemble Consensus Modeling Using Electronic Health Records. RESEARCH SQUARE 2023:rs.3.rs-3367169. [PMID: 37961532 PMCID: PMC10635373 DOI: 10.21203/rs.3.rs-3367169/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Determining the etiology of an acute ischemic stroke (AIS) is fundamental to secondary stroke prevention efforts but can be diagnostically challenging. We trained and validated an automated classification machine intelligence tool, StrokeClassifier, using electronic health record (EHR) text data from 2,039 non-cryptogenic AIS patients at 2 academic hospitals to predict the 4-level outcome of stroke etiology determined by agreement of at least 2 board-certified vascular neurologists' review of the stroke hospitalization EHR. StrokeClassifier is an ensemble consensus meta-model of 9 machine learning classifiers applied to features extracted from discharge summary texts by natural language processing. StrokeClassifier was externally validated in 406 discharge summaries from the MIMIC-III dataset reviewed by a vascular neurologist to ascertain stroke etiology. Compared with stroke etiologies adjudicated by vascular neurologists, StrokeClassifier achieved the mean cross-validated accuracy of 0.74 (±0.01) and weighted F1 of 0.74 (±0.01). In the MIMIC-III cohort, the accuracy and weighted F1 of StrokeClassifier were 0.70 and 0.71, respectively. SHapley Additive exPlanation analysis elucidated that the top 5 features contributing to stroke etiology prediction were atrial fibrillation, age, middle cerebral artery occlusion, internal carotid artery occlusion, and frontal stroke location. We then designed a certainty heuristic to deem a StrokeClassifier diagnosis as confidently non-cryptogenic by the degree of consensus among the 9 classifiers, and applied it to 788 cryptogenic patients. This reduced the percentage of the cryptogenic strokes from 25.2% to 7.2% of all ischemic strokes. StrokeClassifier is a validated artificial intelligence tool that rivals the performance of vascular neurologists in classifying ischemic stroke etiology for individual patients. With further training, StrokeClassifier may have downstream applications including its use as a clinical decision support system.
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Affiliation(s)
- Ho-Joon Lee
- Department of Genetics and Yale Center for Genome Analysis, Yale School of Medicine, New Haven, CT
| | - Lee H. Schwamm
- Department of Neurology and Comprehensive Stroke Center, Massachusetts General Hospital and Harvard Medical School Boston, MA
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Lauren Sansing
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Hooman Kamel
- Department of Neurology, Weill Cornell Medicine, New York City, NY
| | - Adam de Havenon
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Ashby C. Turner
- Department of Neurology and Comprehensive Stroke Center, Massachusetts General Hospital and Harvard Medical School Boston, MA
| | - Kevin N. Sheth
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Smita Krishnaswamy
- Departments of Genetics and Computer Science, Yale School of Medicine, New Haven, CT
| | - Cynthia Brandt
- Department of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT
| | - Hongyu Zhao
- Departments of Biostatistics, Yale School of Public Health, New Haven, CT
| | - Harlan Krumholz
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT
| | - Richa Sharma
- Department of Neurology, Yale School of Medicine, New Haven, CT
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Chahine Y, Magoon MJ, Maidu B, del Álamo JC, Boyle PM, Akoum N. Machine Learning and the Conundrum of Stroke Risk Prediction. Arrhythm Electrophysiol Rev 2023; 12:e07. [PMID: 37427297 PMCID: PMC10326666 DOI: 10.15420/aer.2022.34] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 02/07/2023] [Indexed: 07/11/2023] Open
Abstract
Stroke is a leading cause of death worldwide. With escalating healthcare costs, early non-invasive stroke risk stratification is vital. The current paradigm of stroke risk assessment and mitigation is focused on clinical risk factors and comorbidities. Standard algorithms predict risk using regression-based statistical associations, which, while useful and easy to use, have moderate predictive accuracy. This review summarises recent efforts to deploy machine learning (ML) to predict stroke risk and enrich the understanding of the mechanisms underlying stroke. The surveyed body of literature includes studies comparing ML algorithms with conventional statistical models for predicting cardiovascular disease and, in particular, different stroke subtypes. Another avenue of research explored is ML as a means of enriching multiscale computational modelling, which holds great promise for revealing thrombogenesis mechanisms. Overall, ML offers a new approach to stroke risk stratification that accounts for subtle physiologic variants between patients, potentially leading to more reliable and personalised predictions than standard regression-based statistical associations.
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Affiliation(s)
- Yaacoub Chahine
- Division of Cardiology, University of Washington, Seattle, WA, US
| | - Matthew J Magoon
- Department of Bioengineering, University of Washington, Seattle, WA, US
| | - Bahetihazi Maidu
- Department of Mechanical Engineering, University of Washington, Seattle, WA, US
| | - Juan C del Álamo
- Department of Mechanical Engineering, University of Washington, Seattle, WA, US
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, US
- Center for Cardiovascular Biology, University of Washington, Seattle, WA, US
| | - Patrick M Boyle
- Department of Bioengineering, University of Washington, Seattle, WA, US
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, US
- Center for Cardiovascular Biology, University of Washington, Seattle, WA, US
| | - Nazem Akoum
- Division of Cardiology, University of Washington, Seattle, WA, US
- Department of Bioengineering, University of Washington, Seattle, WA, US
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10
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Miceli G, Basso MG, Rizzo G, Pintus C, Cocciola E, Pennacchio AR, Tuttolomondo A. Artificial Intelligence in Acute Ischemic Stroke Subtypes According to Toast Classification: A Comprehensive Narrative Review. Biomedicines 2023; 11:biomedicines11041138. [PMID: 37189756 DOI: 10.3390/biomedicines11041138] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 03/29/2023] [Accepted: 04/06/2023] [Indexed: 05/17/2023] Open
Abstract
The correct recognition of the etiology of ischemic stroke (IS) allows tempestive interventions in therapy with the aim of treating the cause and preventing a new cerebral ischemic event. Nevertheless, the identification of the cause is often challenging and is based on clinical features and data obtained by imaging techniques and other diagnostic exams. TOAST classification system describes the different etiologies of ischemic stroke and includes five subtypes: LAAS (large-artery atherosclerosis), CEI (cardio embolism), SVD (small vessel disease), ODE (stroke of other determined etiology), and UDE (stroke of undetermined etiology). AI models, providing computational methodologies for quantitative and objective evaluations, seem to increase the sensitivity of main IS causes, such as tomographic diagnosis of carotid stenosis, electrocardiographic recognition of atrial fibrillation, and identification of small vessel disease in magnetic resonance images. The aim of this review is to provide overall knowledge about the most effective AI models used in the differential diagnosis of ischemic stroke etiology according to the TOAST classification. According to our results, AI has proven to be a useful tool for identifying predictive factors capable of subtyping acute stroke patients in large heterogeneous populations and, in particular, clarifying the etiology of UDE IS especially detecting cardioembolic sources.
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Affiliation(s)
- Giuseppe Miceli
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (ProMISE), Università Degli Studi di Palermo, Piazza Delle Cliniche 2, 90127 Palermo, Italy
- Internal Medicine and Stroke Care Ward, University Hospital, Policlinico "P. Giaccone", 90141 Palermo, Italy
| | - Maria Grazia Basso
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (ProMISE), Università Degli Studi di Palermo, Piazza Delle Cliniche 2, 90127 Palermo, Italy
- Internal Medicine and Stroke Care Ward, University Hospital, Policlinico "P. Giaccone", 90141 Palermo, Italy
| | - Giuliana Rizzo
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (ProMISE), Università Degli Studi di Palermo, Piazza Delle Cliniche 2, 90127 Palermo, Italy
- Internal Medicine and Stroke Care Ward, University Hospital, Policlinico "P. Giaccone", 90141 Palermo, Italy
| | - Chiara Pintus
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (ProMISE), Università Degli Studi di Palermo, Piazza Delle Cliniche 2, 90127 Palermo, Italy
- Internal Medicine and Stroke Care Ward, University Hospital, Policlinico "P. Giaccone", 90141 Palermo, Italy
| | - Elena Cocciola
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (ProMISE), Università Degli Studi di Palermo, Piazza Delle Cliniche 2, 90127 Palermo, Italy
- Internal Medicine and Stroke Care Ward, University Hospital, Policlinico "P. Giaccone", 90141 Palermo, Italy
| | - Andrea Roberta Pennacchio
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (ProMISE), Università Degli Studi di Palermo, Piazza Delle Cliniche 2, 90127 Palermo, Italy
- Internal Medicine and Stroke Care Ward, University Hospital, Policlinico "P. Giaccone", 90141 Palermo, Italy
| | - Antonino Tuttolomondo
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (ProMISE), Università Degli Studi di Palermo, Piazza Delle Cliniche 2, 90127 Palermo, Italy
- Internal Medicine and Stroke Care Ward, University Hospital, Policlinico "P. Giaccone", 90141 Palermo, Italy
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11
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Loeffler SE, Trayanova N. Primer on Machine Learning in Electrophysiology. Arrhythm Electrophysiol Rev 2023; 12:e06. [PMID: 37427298 PMCID: PMC10323871 DOI: 10.15420/aer.2022.43] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 01/10/2023] [Indexed: 07/11/2023] Open
Abstract
Artificial intelligence has become ubiquitous. Machine learning, a branch of artificial intelligence, leads the current technological revolution through its remarkable ability to learn and perform on data sets of varying types. Machine learning applications are expected to change contemporary medicine as they are brought into mainstream clinical practice. In the field of cardiac arrhythmia and electrophysiology, machine learning applications have enjoyed rapid growth and popularity. To facilitate clinical acceptance of these methodologies, it is important to promote general knowledge of machine learning in the wider community and continue to highlight the areas of successful application. The authors present a primer to provide an overview of common supervised (least squares, support vector machine, neural networks and random forest) and unsupervised (k-means and principal component analysis) machine learning models. The authors also provide explanations as to how and why the specific machine learning models have been used in arrhythmia and electrophysiology studies.
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Affiliation(s)
- Shane E Loeffler
- Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), Johns Hopkins University Baltimore, MD, US
| | - Natalia Trayanova
- Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), Johns Hopkins University Baltimore, MD, US
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, US
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12
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Huang YC, Cheng YC, Jhou MJ, Chen M, Lu CJ. Integrated Machine Learning Decision Tree Model for Risk Evaluation in Patients with Non-Valvular Atrial Fibrillation When Taking Different Doses of Dabigatran. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:2359. [PMID: 36767726 PMCID: PMC9915180 DOI: 10.3390/ijerph20032359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 01/24/2023] [Accepted: 01/24/2023] [Indexed: 06/18/2023]
Abstract
The new generation of nonvitamin K antagonists are broadly applied for stroke prevention due to their notable efficacy and safety. Our study aimed to develop a suggestive utilization of dabigatran through an integrated machine learning (ML) decision-tree model. Participants taking different doses of dabigatran in the Randomized Evaluation of Long-Term Anticoagulant Therapy trial were included in our analysis and defined as the 110 mg and 150 mg groups. The proposed scheme integrated ML methods, namely naive Bayes, random forest (RF), classification and regression tree (CART), and extreme gradient boosting (XGBoost), which were used to identify the essential variables for predicting vascular events in the 110 mg group and bleeding in the 150 mg group. RF (0.764 for 110 mg; 0.747 for 150 mg) and XGBoost (0.708 for 110 mg; 0.761 for 150 mg) had better area under the receiver operating characteristic curve (AUC) values than logistic regression (benchmark model; 0.683 for 110 mg; 0.739 for 150 mg). We then selected the top ten important variables as internal nodes of the CART decision tree. The two best CART models with ten important variables output tree-shaped rules for predicting vascular events in the 110 mg group and bleeding in the 150 mg group. Our model can be used to provide more visualized and interpretable suggestive rules to clinicians managing NVAF patients who are taking dabigatran.
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Affiliation(s)
- Yung-Chuan Huang
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, Taiwan
- Department of Neurology, Fu Jen Catholic University Hospital, Fu Jen Catholic University, New Taipei City 24352, Taiwan
| | - Yu-Chen Cheng
- Department of Neurology, Fu Jen Catholic University Hospital, Fu Jen Catholic University, New Taipei City 24352, Taiwan
| | - Mao-Jhen Jhou
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, Taiwan
| | - Mingchih Chen
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, Taiwan
- Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City 242062, Taiwan
| | - Chi-Jie Lu
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, Taiwan
- Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City 242062, Taiwan
- Department of Information Management, Fu Jen Catholic University, New Taipei City 242062, Taiwan
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13
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Abdalkader M, Siegler JE, Lee JS, Yaghi S, Qiu Z, Huo X, Miao Z, Campbell BC, Nguyen TN. Neuroimaging of Acute Ischemic Stroke: Multimodal Imaging Approach for Acute Endovascular Therapy. J Stroke 2023; 25:55-71. [PMID: 36746380 PMCID: PMC9911849 DOI: 10.5853/jos.2022.03286] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 12/19/2022] [Accepted: 01/04/2023] [Indexed: 02/04/2023] Open
Abstract
Advances in acute ischemic stroke (AIS) treatment have been contingent on innovations in neuroimaging. Neuroimaging plays a pivotal role in the diagnosis and prognosis of ischemic stroke and large vessel occlusion, enabling triage decisions in the emergent care of the stroke patient. Current imaging protocols for acute stroke are dependent on the available resources and clinicians' preferences and experiences. In addition, differential application of neuroimaging in medical decision-making, and the rapidly growing evidence to support varying paradigms have outpaced guideline-based recommendations for selecting patients to receive intravenous or endovascular treatment. In this review, we aimed to discuss the various imaging modalities and approaches used in the diagnosis and treatment of AIS.
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Affiliation(s)
| | - James E. Siegler
- Cooper Neurological Institute, Cooper University Hospital, Camden, NJ, USA
| | - Jin Soo Lee
- Department of Neurology, Ajou University Hospital, Ajou University School of Medicine, Suwon, Korea
| | - Shadi Yaghi
- Department of Neurology, Brown University, Providence, RI, USA
| | - Zhongming Qiu
- Department of Neurology, The 903rd Hospital of The Chinese People’s Liberation Army, Hangzhou, China
| | - Xiaochuan Huo
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhongrong Miao
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Bruce C.V. Campbell
- Department of Medicine and Neurology, Melbourne Brain Centre at the Royal Melbourne Hospital, University of Melbourne, Parkville, Victoria, Australia
| | - Thanh N. Nguyen
- Department of Radiology, Boston Medical Center, Boston, MA, USA
- Department of Neurology, Boston Medical Center, Boston, MA, USA
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14
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Chen CH, Lee M, Weng HH, Lee JD, Yang JT, Tsai YH, Huang YC. Identification of magnetic resonance imaging features for the prediction of unrecognized atrial fibrillation in acute ischemic stroke. Front Neurol 2022; 13:952462. [PMID: 36176550 PMCID: PMC9513827 DOI: 10.3389/fneur.2022.952462] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 07/25/2022] [Indexed: 11/13/2022] Open
Abstract
Background and purposeThe early identification of cardioembolic stroke is critical for the early initiation of anticoagulant treatment. However, it can be challenging to identify the major cardiac source, particularly since the predominant source, paroxysmal atrial fibrillation (AF), may not be present at the time of stroke. In this study, we aimed to evaluate imaging predictors for unrecognized AF in patients with acute ischemic stroke.MethodsWe performed a cross-sectional analysis of data and magnetic resonance imaging (MRI) scans from two prospective cohorts of patients who underwent serial 12-lead electrocardiography and 24-h Holter monitoring to detect unrecognized AF. The imaging patterns in diffusion-weighted imaging and imaging characteristics were assessed and classified. A logistic regression model was used to identify predictive factors for newly detected AF in patients with acute ischemic stroke.ResultsA total of 734 patients were recruited for analysis, with a median age of 72 (interquartile range: 65–79) years and a median National Institutes of Health Stroke Scale score of 4 (interquartile range: 2–6). Of these patients, 64 (8.7%) had newly detected AF during the follow-up period. Stepwise multivariate logistic regression revealed that age ≥75 years [adjusted odds ratio (aOR) 5.66, 95% confidence interval (CI) 2.98–10.75], receiving recombinant tissue plasminogen activator treatment (aOR 4.36, 95% CI 1.65–11.54), congestive heart failure (aOR 6.73, 95% CI 1.85–24.48), early hemorrhage in MRI (aOR 3.62, 95% CI 1.52–8.61), single cortical infarct (aOR 6.49, 95% CI 2.35–17.92), and territorial infarcts (aOR 3.54, 95% CI 1.06–11.75) were associated with newly detected AF. The C-statistic of the prediction model for newly detected AF was 0.764.ConclusionInitial MRI at the time of stroke may be useful to predict which patients have cardioembolic stroke caused by unrecognized AF. Further studies are warranted to verify these findings and their application to high-risk patients.
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Affiliation(s)
- Chao-Hui Chen
- Department of Neurology, Chang Gung Memorial Hospital at Chiayi, Chang-Gung University College of Medicine, Chiayi City, Taiwan
| | - Meng Lee
- Department of Neurology, Chang Gung Memorial Hospital at Chiayi, Chang-Gung University College of Medicine, Chiayi City, Taiwan
| | - Hsu-Huei Weng
- Department of Diagnostic Radiology, Chang Gung Memorial Hospital at Chiayi, Chang-Gung University College of Medicine, Chiayi City, Taiwan
| | - Jiann-Der Lee
- Department of Neurology, Chang Gung Memorial Hospital at Chiayi, Chang-Gung University College of Medicine, Chiayi City, Taiwan
| | - Jen-Tsung Yang
- Department of Neurosurgery, Chang Gung Memorial Hospital at Chiayi, Chang-Gung University College of Medicine, Chiayi City, Taiwan
| | - Yuan-Hsiung Tsai
- Department of Diagnostic Radiology, Chang Gung Memorial Hospital at Chiayi, Chang-Gung University College of Medicine, Chiayi City, Taiwan
| | - Yen-Chu Huang
- Department of Neurology, Chang Gung Memorial Hospital at Chiayi, Chang-Gung University College of Medicine, Chiayi City, Taiwan
- *Correspondence: Yen-Chu Huang
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15
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Lin X, Zheng X, Zhang J, Cui X, Zou D, Zhao Z, Pan X, Jie Q, Wu Y, Qiu R, Zhou J, Chen N, Tang L, Ge C, Zou J. Machine learning to predict futile recanalization of large vessel occlusion before and after endovascular thrombectomy. Front Neurol 2022; 13:909403. [PMID: 36062013 PMCID: PMC9437637 DOI: 10.3389/fneur.2022.909403] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 07/05/2022] [Indexed: 11/30/2022] Open
Abstract
Background and purpose Futile recanalization occurs when the endovascular thrombectomy (EVT) is a technical success but fails to achieve a favorable outcome. This study aimed to use machine learning (ML) algorithms to develop a pre-EVT model and a post-EVT model to predict the risk of futile recanalization and to provide meaningful insights to assess the prognostic factors associated with futile recanalization. Methods Consecutive acute ischemic stroke patients with large vessel occlusion (LVO) undergoing EVT at the National Advanced Stroke Center of Nanjing First Hospital (China) between April 2017 and May 2021 were analyzed. The baseline characteristics and peri-interventional characteristics were assessed using four ML algorithms. The predictive performance was evaluated by the area under curve (AUC) of receiver operating characteristic and calibration curve. In addition, the SHapley Additive exPlanations (SHAP) approach and partial dependence plot were introduced to understand the relative importance and the influence of a single feature. Results A total of 312 patients were included in this study. Of the four ML models that include baseline characteristics, the “Early” XGBoost had a better performance {AUC, 0.790 [95% confidence intervals (CI), 0.677–0.903]; Brier, 0.191}. Subsequent inclusion of peri-interventional characteristics into the “Early” XGBoost showed that the “Late” XGBoost performed better [AUC, 0.910 (95% CI, 0.837–0.984); Brier, 0.123]. NIHSS after 24 h, age, groin to recanalization, and the number of passages were the critical prognostic factors associated with futile recanalization, and the SHAP approach shows that NIHSS after 24 h ranks first in relative importance. Conclusions The “Early” XGBoost and the “Late” XGBoost allowed us to predict futile recanalization before and after EVT accurately. Our study suggests that including peri-interventional characteristics may lead to superior predictive performance compared to a model based on baseline characteristics only. In addition, NIHSS after 24 h was the most important prognostic factor for futile recanalization.
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Affiliation(s)
- Xinping Lin
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
- Department of Pharmacy Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Xiaohan Zheng
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
- Department of Pharmacy Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Juan Zhang
- Department of Neurology, Nanjing Yuhua Hospital, Yuhua Branch of Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Xiaoli Cui
- Department of Neurology, Nanjing Yuhua Hospital, Yuhua Branch of Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Daizu Zou
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
- Department of Pharmacy Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Zheng Zhao
- Department of Pharmacy Nanjing First Hospital, Nanjing Medical University, Nanjing, China
- Department of Pharmacy, Nanjing First Hospital, China Pharmaceutical University, Nanjing, China
| | - Xiding Pan
- Department of Pharmacy Nanjing First Hospital, Nanjing Medical University, Nanjing, China
- Department of Pharmacy, Nanjing First Hospital, China Pharmaceutical University, Nanjing, China
| | - Qiong Jie
- Department of Pharmacy Nanjing First Hospital, Nanjing Medical University, Nanjing, China
- Department of Pharmacy, Nanjing First Hospital, China Pharmaceutical University, Nanjing, China
| | - Yuezhang Wu
- Department of Pharmacy Nanjing First Hospital, Nanjing Medical University, Nanjing, China
- Department of Pharmacy, Nanjing First Hospital, China Pharmaceutical University, Nanjing, China
| | - Runze Qiu
- Department of Pharmacy Nanjing First Hospital, Nanjing Medical University, Nanjing, China
- Department of Pharmacy, Nanjing First Hospital, China Pharmaceutical University, Nanjing, China
| | - Junshan Zhou
- Department of Neurology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Nihong Chen
- Department of Neurology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Li Tang
- Department of Pharmacy, Yixing Cancer Hospital, Yixing, China
- Li Tang
| | - Chun Ge
- Department of Pharmacy Nanjing First Hospital, Nanjing Medical University, Nanjing, China
- Department of Pharmacy, Nanjing First Hospital, China Pharmaceutical University, Nanjing, China
- Chun Ge
| | - Jianjun Zou
- Department of Pharmacy Nanjing First Hospital, Nanjing Medical University, Nanjing, China
- Department of Pharmacy, Nanjing First Hospital, China Pharmaceutical University, Nanjing, China
- *Correspondence: Jianjun Zou
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16
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Sung SF, Sung KL, Pan RC, Lee PJ, Hu YH. Automated risk assessment of newly detected atrial fibrillation poststroke from electronic health record data using machine learning and natural language processing. Front Cardiovasc Med 2022; 9:941237. [PMID: 35966534 PMCID: PMC9372298 DOI: 10.3389/fcvm.2022.941237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 07/11/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundTimely detection of atrial fibrillation (AF) after stroke is highly clinically relevant, aiding decisions on the optimal strategies for secondary prevention of stroke. In the context of limited medical resources, it is crucial to set the right priorities of extended heart rhythm monitoring by stratifying patients into different risk groups likely to have newly detected AF (NDAF). This study aimed to develop an electronic health record (EHR)-based machine learning model to assess the risk of NDAF in an early stage after stroke.MethodsLinked data between a hospital stroke registry and a deidentified research-based database including EHRs and administrative claims data was used. Demographic features, physiological measurements, routine laboratory results, and clinical free text were extracted from EHRs. The extreme gradient boosting algorithm was used to build the prediction model. The prediction performance was evaluated by the C-index and was compared to that of the AS5F and CHASE-LESS scores.ResultsThe study population consisted of a training set of 4,064 and a temporal test set of 1,492 patients. During a median follow-up of 10.2 months, the incidence rate of NDAF was 87.0 per 1,000 person-year in the test set. On the test set, the model based on both structured and unstructured data achieved a C-index of 0.840, which was significantly higher than those of the AS5F (0.779, p = 0.023) and CHASE-LESS (0.768, p = 0.005) scores.ConclusionsIt is feasible to build a machine learning model to assess the risk of NDAF based on EHR data available at the time of hospital admission. Inclusion of information derived from clinical free text can significantly improve the model performance and may outperform risk scores developed using traditional statistical methods. Further studies are needed to assess the clinical usefulness of the prediction model.
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Affiliation(s)
- Sheng-Feng Sung
- Division of Neurology, Department of Internal Medicine, Ditmanson Medical Foundation Chiayi Christian Hospital, Chiayi City, Taiwan
- Department of Nursing, Min-Hwei Junior College of Health Care Management, Tainan, Taiwan
| | - Kuan-Lin Sung
- School of Medicine, National Taiwan University, Taipei, Taiwan
| | - Ru-Chiou Pan
- Clinical Data Center, Department of Medical Research, Ditmanson Medical Foundation Chiayi Christian Hospital, Chiayi City, Taiwan
| | - Pei-Ju Lee
- Department of Information Management and Institute of Healthcare Information Management, National Chung Cheng University, Chiayi County, Taiwan
- *Correspondence: Pei-Ju Lee
| | - Ya-Han Hu
- Department of Information Management, National Central University, Taoyuan, Taiwan
- Ya-Han Hu
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Li J, Zhu W, Zhou J, Yun W, Li X, Guan Q, Lv W, Cheng Y, Ni H, Xie Z, Li M, Zhang L, Xu Y, Zhang Q. A Presurgical Unfavorable Prediction Scale of Endovascular Treatment for Acute Ischemic Stroke. Front Aging Neurosci 2022; 14:942285. [PMID: 35847671 PMCID: PMC9284674 DOI: 10.3389/fnagi.2022.942285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 06/02/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectiveTo develop a prognostic prediction model of endovascular treatment (EVT) for acute ischemic stroke (AIS) induced by large-vessel occlusion (LVO), this study applied machine learning classification model light gradient boosting machine (LightGBM) to construct a unique prediction model.MethodsA total of 973 patients were enrolled, primary outcome was assessed with modified Rankin scale (mRS) at 90 days, and favorable outcome was defined using mRS 0–2 scores. Besides, LightGBM algorithm and logistic regression (LR) were used to construct a prediction model. Then, a prediction scale was further established and verified by both internal data and other external data.ResultsA total of 20 presurgical variables were analyzed using LR and LightGBM. The results of LightGBM algorithm indicated that the accuracy and precision of the prediction model were 73.77 and 73.16%, respectively. The area under the curve (AUC) was 0.824. Furthermore, the top 5 variables suggesting unfavorable outcomes were namely admitting blood glucose levels, age, onset to EVT time, onset to hospital time, and National Institutes of Health Stroke Scale (NIHSS) scores (importance = 130.9, 102.6, 96.5, 89.5 and 84.4, respectively). According to AUC, we established the key cutoff points and constructed prediction scale based on their respective weightings. Then, the established prediction scale was verified in raw and external data and the sensitivity was 80.4 and 83.5%, respectively. Finally, scores >3 demonstrated better accuracy in predicting unfavorable outcomes.ConclusionPresurgical prediction scale is feasible and accurate in identifying unfavorable outcomes of AIS after EVT.
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Affiliation(s)
- Jingwei Li
- Department of Neurology of Drum Tower Hospital, Medical School and the State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, China
- Institute of Brain Sciences, Nanjing University, Nanjing, China
- Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neurology Clinic Medical Center, Nanjing, China
| | - Wencheng Zhu
- The Institute of Software, Chinese Academy of Sciences, Beijing, China
| | - Junshan Zhou
- Department of Neurology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Wenwei Yun
- Department of Neurology, Changzhou No.2 People's Hospital Affiliated to Nanjing Medical University, Changzhou, China
| | - Xiaobo Li
- Department of Neurology, Northern Jiangsu People's Hospital, Clinical Medical School of Yangzhou University, Yangzhou, China
| | - Qiaochu Guan
- Department of Neurology of Drum Tower Hospital, Medical School and the State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, China
| | - Weiping Lv
- Department of Neurology of Drum Tower Hospital, Medical School and the State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, China
| | - Yue Cheng
- Department of Neurology of Drum Tower Hospital, Medical School and the State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, China
| | - Huanyu Ni
- Department of Pharmacy of Drum Tower Hospital, Medical School, Nanjing University, Nanjing, China
| | - Ziyi Xie
- Department of Neurology of Drum Tower Hospital, Medical School and the State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, China
| | - Mengyun Li
- Department of Neurology of Drum Tower Hospital, Medical School and the State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, China
| | - Lu Zhang
- Department of Neurology of Drum Tower Hospital, Medical School and the State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, China
| | - Yun Xu
- Department of Neurology of Drum Tower Hospital, Medical School and the State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, China
- Institute of Brain Sciences, Nanjing University, Nanjing, China
- Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neurology Clinic Medical Center, Nanjing, China
| | - Qingxiu Zhang
- Department of Neurology of Drum Tower Hospital, Medical School and the State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, China
- Institute of Brain Sciences, Nanjing University, Nanjing, China
- Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neurology Clinic Medical Center, Nanjing, China
- *Correspondence: Qingxiu Zhang
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18
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Huang YC, Cheng YC, Jhou MJ, Chen M, Lu CJ. Important Risk Factors in Patients with Nonvalvular Atrial Fibrillation Taking Dabigatran Using Integrated Machine Learning Scheme-A Post Hoc Analysis. J Pers Med 2022; 12:756. [PMID: 35629177 PMCID: PMC9146635 DOI: 10.3390/jpm12050756] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 04/29/2022] [Accepted: 05/03/2022] [Indexed: 02/06/2023] Open
Abstract
Our study aims to develop an effective integrated machine learning (ML) scheme to predict vascular events and bleeding in patients with nonvalvular atrial fibrillation taking dabigatran and identify important risk factors. This study is a post-hoc analysis from the Randomized Evaluation of Long-Term Anticoagulant Therapy trial database. One traditional prediction method, logistic regression (LGR), and four ML techniques-naive Bayes, random forest (RF), classification and regression tree, and extreme gradient boosting (XGBoost)-were combined to construct our scheme. Area under the receiver operating characteristic curve (AUC) of RF (0.780) and XGBoost (0.717) was higher than that of LGR (0.674) in predicting vascular events. In predicting bleeding, AUC of RF (0.684) and XGBoost (0.618) showed higher values than those generated by LGR (0.605). Our integrated ML feature selection scheme based on the two convincing prediction techniques identified age, history of congestive heart failure and myocardial infarction, smoking, kidney function, and body mass index as major variables of vascular events; age, kidney function, smoking, bleeding history, concomitant use of specific drugs, and dabigatran dosage as major variables of bleeding. ML is an effective data analysis algorithm for solving complex medical data. Our results may provide preliminary direction for precision medicine.
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Affiliation(s)
- Yung-Chuan Huang
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, Taiwan; (Y.-C.H.); (M.-J.J.); (M.C.)
- Department of Neurology, Fu Jen Catholic University Hospital, Fu Jen Catholic University, New Taipei City 24352, Taiwan;
| | - Yu-Chen Cheng
- Department of Neurology, Fu Jen Catholic University Hospital, Fu Jen Catholic University, New Taipei City 24352, Taiwan;
| | - Mao-Jhen Jhou
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, Taiwan; (Y.-C.H.); (M.-J.J.); (M.C.)
| | - Mingchih Chen
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, Taiwan; (Y.-C.H.); (M.-J.J.); (M.C.)
- Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City 242062, Taiwan
| | - Chi-Jie Lu
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, Taiwan; (Y.-C.H.); (M.-J.J.); (M.C.)
- Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City 242062, Taiwan
- Department of Information Management, Fu Jen Catholic University, New Taipei City 242062, Taiwan
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19
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Grory BM, Yaghi S, Cordonnier C, Sposato LA, Romano JG, Chaturvedi S. Advances in Recurrent Stroke Prevention: Focus on Antithrombotic Therapies. Circ Res 2022; 130:1075-1094. [PMID: 35420910 PMCID: PMC9015232 DOI: 10.1161/circresaha.121.319947] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
The past decade has seen significant advances in stroke prevention. These advances include new antithrombotic agents, new options for dyslipidemia treatment, and novel techniques for surgical stroke prevention. In addition, there is greater recognition of the benefits of multifaceted interventions, including the role of physical activity and dietary modification. Despite these advances, the aging of the population and the high prevalence of key vascular risk factors pose challenges to reducing the burden of stroke. Using a cause-based framework, current approaches to prevention of cardioembolic, cryptogenic, atherosclerotic, and small vessel disease stroke are outlined in this paper. Special emphasis is given to recent trials of antithrombotic agents, including studies that have tested combination treatments and responses according to genetic factors.
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Affiliation(s)
| | | | - Charlotte Cordonnier
- Univ. Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, F-59000 Lille, France
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20
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Martin MC, Sichtermann T, Schürmann K, Habib P, Wiesmann M, Schulz JB, Nikoubashman O, Pinho J, Reich A. Classification of patients with embolic stroke of undetermined source in cardioembolic and non-cardioembolic profiles. Eur J Neurol 2022; 29:2275-2282. [PMID: 35420727 DOI: 10.1111/ene.15356] [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: 02/09/2022] [Revised: 03/29/2022] [Accepted: 04/11/2022] [Indexed: 11/28/2022]
Abstract
BACKGROUND It is currently thought that embolic stroke of undetermined source (ESUS) patients have diverse underlying hidden etiologies, of which cardioembolism is one of the most important. This subgroup of patients could theoretically benefit from oral anticoagulation, but it remains unclear if these of patients can be correctly identified from other ESUS-subgroups and which markers should be used. We aimed to determine whether a machine learning (ML) model could discriminate ESUS patients into cardioembolic and non-cardioembolic profiles using baseline demographic and laboratory variables. METHODS Based on a prospective registry of consecutive ischemic stroke patients submitted to acute revascularization therapies, a ML model was trained using data on age, sex and 11 selected baseline laboratory parameters of patients with known stroke etiology with the aim of correctly identifying patients with cardioembolic and non-cardioembolic etiologies. The resulting model was used to classify ESUS patients into either cardioembolic or non-cardioembolic profiles. RESULTS The ML model was able to distinguish patients with known stroke etiology into cardioembolic or non-cardioembolic with excellent accuracy (area under the curve = 0.82). When applied to ESUS patients, the model classified 40.3% of them as having cardioembolic profiles. ESUS patients with cardioembolic profiles were older, more frequently female, more frequently had hypertension, less frequently were active smokers, had higher CHA2DS2-VASc scores, and had more premature atrial complexes per hour. CONCLUSIONS A ML model based on baseline demographic and laboratory parameters was able to classify ESUS patients in cardioembolic and non-cardioembolic profiles and predicted that 40% of the ESUS patients have a cardioembolic profile.
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Affiliation(s)
| | - Thorsten Sichtermann
- Department of Diagnostic and Interventional Neuroradiology, University Hospital RWTH, Aachen, Germany
| | - Kolja Schürmann
- Department of Neurology, University Hospital RWTH, Aachen, Germany
| | - Pardes Habib
- Department of Neurology, University Hospital RWTH, Aachen, Germany.,JARA-Institute Molecular Neuroscience and Neuroimaging, Forschungszentrum Jülich, Jülich, Germany
| | - Martin Wiesmann
- Department of Diagnostic and Interventional Neuroradiology, University Hospital RWTH, Aachen, Germany
| | - Jörg B Schulz
- Department of Neurology, University Hospital RWTH, Aachen, Germany.,JARA-Institute Molecular Neuroscience and Neuroimaging, Forschungszentrum Jülich, Jülich, Germany
| | - Omid Nikoubashman
- Department of Diagnostic and Interventional Neuroradiology, University Hospital RWTH, Aachen, Germany
| | - João Pinho
- Department of Neurology, University Hospital RWTH, Aachen, Germany
| | - Arno Reich
- Department of Neurology, University Hospital RWTH, Aachen, Germany
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21
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Jiang L, Zhang C, Wang S, Ai Z, Shen T, Zhang H, Duan S, Yin X, Chen YC. MRI Radiomics Features From Infarction and Cerebrospinal Fluid for Prediction of Cerebral Edema After Acute Ischemic Stroke. Front Aging Neurosci 2022; 14:782036. [PMID: 35309889 PMCID: PMC8929352 DOI: 10.3389/fnagi.2022.782036] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 02/11/2022] [Indexed: 12/17/2022] Open
Abstract
Neuroimaging biomarkers that predict the edema after acute stroke may help clinicians provide targeted therapies and minimize the risk of secondary injury. In this study, we applied pretherapy MRI radiomics features from infarction and cerebrospinal fluid (CSF) to predict edema after acute ischemic stroke. MRI data were obtained from a prospective, endovascular thrombectomy (EVT) cohort that included 389 patients with acute stroke from two centers (dataset 1, n = 292; dataset 2, n = 97), respectively. Patients were divided into edema group (brain swelling and midline shift) and non-edema group according to CT within 36 h after therapy. We extracted the imaging features of infarct area on diffusion weighted imaging (DWI) (abbreviated as DWI), CSF on fluid-attenuated inversion recovery (FLAIR) (CSFFLAIR) and CSF on DWI (CSFDWI), and selected the optimum features associated with edema for developing models in two forms of feature sets (DWI + CSFFLAIR and DWI + CSFDWI) respectively. We developed seven ML models based on dataset 1 and identified the most stable model. External validations (dataset 2) of the developed stable model were performed. Prediction model performance was assessed using the area under the receiver operating characteristic curve (AUC). The Bayes model based on DWI + CSFFLAIR and the RF model based on DWI + CSFDWI had the best performances (DWI + CSFFLAIR: AUC, 0.86; accuracy, 0.85; recall, 0.88; DWI + CSFDWI: AUC, 0.86; accuracy, 0.84; recall, 0.84) and the most stability (RSD% in DWI + CSFFLAIR AUC: 0.07, RSD% in DWI + CSFDWI AUC: 0.09), respectively. External validation showed that the AUC of the Bayes model based on DWI + CSFFLAIR was 0.84 with accuracy of 0.77 and area under precision-recall curve (auPRC) of 0.75, and the AUC of the RF model based on DWI + CSFDWI was 0.83 with accuracy of 0.81 and the auPRC of 0.76. The MRI radiomics features from infarction and CSF may offer an effective imaging biomarker for predicting edema.
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Affiliation(s)
- Liang Jiang
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Chuanyang Zhang
- Department of Radiology, Nanjing Gaochun People’s Hospital, Nanjing, China
| | - Siyu Wang
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Zhongping Ai
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Tingwen Shen
- Department of Radiology, Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing, China
| | - Hong Zhang
- Department of Radiology, Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing, China
| | - Shaofeng Duan
- GE Healthcare, Precision Health Institution, Shanghai, China
| | - Xindao Yin
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
- *Correspondence: Xindao Yin,
| | - Yu-Chen Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
- Yu-Chen Chen,
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22
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Zhu B, Zhao J, Cao M, Du W, Yang L, Su M, Tian Y, Wu M, Wu T, Wang M, Zhao X, Zhao Z. Predicting 1-Hour Thrombolysis Effect of r-tPA in Patients With Acute Ischemic Stroke Using Machine Learning Algorithm. Front Pharmacol 2022; 12:759782. [PMID: 35046804 PMCID: PMC8762247 DOI: 10.3389/fphar.2021.759782] [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: 08/17/2021] [Accepted: 11/29/2021] [Indexed: 01/01/2023] Open
Abstract
Background: Thrombolysis with r-tPA is recommended for patients after acute ischemic stroke (AIS) within 4.5 h of symptom onset. However, only a few patients benefit from this therapeutic regimen. Thus, we aimed to develop an interpretable machine learning (ML)–based model to predict the thrombolysis effect of r-tPA at the super-early stage. Methods: A total of 353 patients with AIS were divided into training and test data sets. We then used six ML algorithms and a recursive feature elimination (RFE) method to explore the relationship among the clinical variables along with the NIH stroke scale score 1 h after thrombolysis treatment. Shapley additive explanations and local interpretable model–agnostic explanation algorithms were applied to interpret the ML models and determine the importance of the selected features. Results: Altogether, 353 patients with an average age of 63.0 (56.0–71.0) years were enrolled in the study. Of these patients, 156 showed a favorable thrombolysis effect and 197 showed an unfavorable effect. A total of 14 variables were enrolled in the modeling, and 6 ML algorithms were used to predict the thrombolysis effect. After RFE screening, seven variables under the gradient boosting decision tree (GBDT) model (area under the curve = 0.81, specificity = 0.61, sensitivity = 0.9, and F1 score = 0.79) demonstrated the best performance. Of the seven variables, activated partial thromboplastin clotting time (time), B-type natriuretic peptide, and fibrin degradation products were the three most important clinical characteristics that might influence r-tPA efficiency. Conclusion: This study demonstrated that the GBDT model with the seven variables could better predict the early thrombolysis effect of r-tPA.
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Affiliation(s)
- Bin Zhu
- Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jianlei Zhao
- Department of Neurology, The Second Hospital of Lanzhou University, Lanzhou, China
| | - Mingnan Cao
- Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Wanliang Du
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | | | | | - Yue Tian
- Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Mingfen Wu
- Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Tingxi Wu
- Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Manxia Wang
- Department of Neurology, The Second Hospital of Lanzhou University, Lanzhou, China
| | - Xingquan Zhao
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhigang Zhao
- Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
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23
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Mishra NK, Liebeskind DS. Artificial Intelligence in Stroke. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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24
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Olier I, Ortega-Martorell S, Pieroni M, Lip GYH. How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management. Cardiovasc Res 2021; 117:1700-1717. [PMID: 33982064 PMCID: PMC8477792 DOI: 10.1093/cvr/cvab169] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 05/11/2021] [Indexed: 02/01/2023] Open
Abstract
There has been an exponential growth of artificial intelligence (AI) and machine learning (ML) publications aimed at advancing our understanding of atrial fibrillation (AF), which has been mainly driven by the confluence of two factors: the advances in deep neural networks (DeepNNs) and the availability of large, open access databases. It is observed that most of the attention has centred on applying ML for dvsetecting AF, particularly using electrocardiograms (ECGs) as the main data modality. Nearly a third of them used DeepNNs to minimize or eliminate the need for transforming the ECGs to extract features prior to ML modelling; however, we did not observe a significant advantage in following this approach. We also found a fraction of studies using other data modalities, and others centred in aims, such as risk prediction, AF management, and others. From the clinical perspective, AI/ML can help expand the utility of AF detection and risk prediction, especially for patients with additional comorbidities. The use of AI/ML for detection and risk prediction into applications and smart mobile health (mHealth) technology would enable ‘real time’ dynamic assessments. AI/ML could also adapt to treatment changes over time, as well as incident risk factors. Incorporation of a dynamic AI/ML model into mHealth technology would facilitate ‘real time’ assessment of stroke risk, facilitating mitigation of modifiable risk factors (e.g. blood pressure control). Overall, this would lead to an improvement in clinical care for patients with AF.
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Affiliation(s)
- Ivan Olier
- School of Computer Science and Mathematics, Liverpool John Moores University, 3 Byrom Street, Liverpool L3 3AF, UK.,Liverpool Centre for Cardiovascular Science, Liverpool John Moores University, Liverpool, UK
| | - Sandra Ortega-Martorell
- School of Computer Science and Mathematics, Liverpool John Moores University, 3 Byrom Street, Liverpool L3 3AF, UK.,Liverpool Centre for Cardiovascular Science, Liverpool John Moores University, Liverpool, UK
| | - Mark Pieroni
- School of Computer Science and Mathematics, Liverpool John Moores University, 3 Byrom Street, Liverpool L3 3AF, UK.,Liverpool Centre for Cardiovascular Science, Liverpool John Moores University, Liverpool, UK
| | - Gregory Y H Lip
- Liverpool Centre for Cardiovascular Science, Liverpool John Moores University, Liverpool, UK.,Liverpool Heart and Chest Hospital, Liverpool, UK
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25
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Velagapudi L, Mouchtouris N, Baldassari MP, Nauheim D, Khanna O, Saiegh FA, Herial N, Gooch MR, Tjoumakaris S, Rosenwasser RH, Jabbour P. Discrepancies in Stroke Distribution and Dataset Origin in Machine Learning for Stroke. J Stroke Cerebrovasc Dis 2021; 30:105832. [PMID: 33940363 DOI: 10.1016/j.jstrokecerebrovasdis.2021.105832] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Revised: 04/11/2021] [Accepted: 04/11/2021] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND Machine learning algorithms depend on accurate and representative datasets for training in order to become valuable clinical tools that are widely generalizable to a varied population. We aim to conduct a review of machine learning uses in stroke literature to assess the geographic distribution of datasets and patient cohorts used to train these models and compare them to stroke distribution to evaluate for disparities. AIMS 582 studies were identified on initial searching of the PubMed database. Of these studies, 106 full texts were assessed after title and abstract screening which resulted in 489 papers excluded. Of these 106 studies, 79 were excluded due to using cohorts from outside the United States or being review articles or editorials. 27 studies were thus included in this analysis. SUMMARY OF REVIEW Of the 27 studies included, 7 (25.9%) used patient data from California, 6 (22.2%) were multicenter, 3 (11.1%) were in Massachusetts, 2 (7.4%) each in Illinois, Missouri, and New York, and 1 (3.7%) each from South Carolina, Washington, West Virginia, and Wisconsin. 1 (3.7%) study used data from Utah and Texas. These were qualitatively compared to a CDC study showing the highest distribution of stroke in Mississippi (4.3%) followed by Oklahoma (3.4%), Washington D.C. (3.4%), Louisiana (3.3%), and Alabama (3.2%) while the prevalence in California was 2.6%. CONCLUSIONS It is clear that a strong disconnect exists between the datasets and patient cohorts used in training machine learning algorithms in clinical research and the stroke distribution in which clinical tools using these algorithms will be implemented. In order to ensure a lack of bias and increase generalizability and accuracy in future machine learning studies, datasets using a varied patient population that reflects the unequal distribution of stroke risk factors would greatly benefit the usability of these tools and ensure accuracy on a nationwide scale.
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Affiliation(s)
- Lohit Velagapudi
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA
| | | | | | - David Nauheim
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA
| | - Omaditya Khanna
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA
| | - Fadi Al Saiegh
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA
| | - Nabeel Herial
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA
| | - M Reid Gooch
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA
| | | | | | - Pascal Jabbour
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA.
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26
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Artificial Intelligence in Stroke. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_197-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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27
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Guo Y, Zhang J, Lip GYH. Embolic Stroke of Undetermined Source: The Need for an Integrated and Holistic Approach to Care. Thromb Haemost 2020; 121:251-254. [PMID: 33307563 DOI: 10.1055/a-1336-0576] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Yutao Guo
- Department of Cardiology, Medical School of Chinese PLA, Chinese PLA General Hospital, Beijing, China.,Liverpool Centre for Cardiovascular Sciences, University of Liverpool and Liverpool Heart and Chest Hospital, Liverpool, United Kingdom
| | - Juqian Zhang
- Liverpool Centre for Cardiovascular Sciences, University of Liverpool and Liverpool Heart and Chest Hospital, Liverpool, United Kingdom
| | - Gregory Y H Lip
- Department of Cardiology, Medical School of Chinese PLA, Chinese PLA General Hospital, Beijing, China.,Liverpool Centre for Cardiovascular Sciences, University of Liverpool and Liverpool Heart and Chest Hospital, Liverpool, United Kingdom.,Aalborg Thrombosis Research Unit, Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
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28
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Aguiar de Sousa D, Katan M. Promising Use of Automated Electronic Phenotyping: Turning Big Data Into Big Value in Stroke Research. Stroke 2020; 52:190-192. [PMID: 33297867 DOI: 10.1161/strokeaha.120.033061] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- Diana Aguiar de Sousa
- Department of Neurosciences and Mental Health (Neurology), Hospital de Santa Maria-Centro Hospitalar Universitário Lisboa Norte (CHULN), Lisbon, Portugal (D.A.d.S.).,Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal (D.A.d.S.)
| | - Mira Katan
- Department of Neurology, University Hospital of Zurich, Switzerland (M.K.).,Neuroscience Center of Zurich, University of Zurich, Switzerland (M.K.)
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