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Clancy Ú, Puttock EJ, Chen W, Whiteley W, Vickery EM, Leung LY, Luetmer PH, Kallmes DF, Fu S, Zheng C, Liu H, Kent DM. Mortality Outcomes in a Large Population with and without Covert Cerebrovascular Disease. Aging Dis 2024:AD.2024.0211. [PMID: 38421836 DOI: 10.14336/ad.2024.0211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 02/11/2024] [Indexed: 03/02/2024] Open
Abstract
Covert cerebrovascular disease (CCD) is frequently reported on neuroimaging and associates with increased dementia and stroke risk. We aimed to determine how incidentally-discovered CCD during clinical neuroimaging in a large population associates with mortality. We screened CT and MRI reports of adults aged ≥50 in the Kaiser Permanente Southern California health system who underwent neuroimaging for a non-stroke clinical indication from 2009-2019. Natural language processing identified incidental covert brain infarcts (CBI) and/or white matter hyperintensities (WMH), grading WMH as mild/moderate/severe. Models adjusted for age, sex, ethnicity, multimorbidity, vascular risks, depression, exercise, and imaging modality. Of n=241,028, the mean age was 64.9 (SD=10.4); mean follow-up 4.46 years; 178,554 (74.1%) had CT; 62,474 (25.9%) had MRI; 11,328 (4.7%) had CBI; and 69,927 (29.0%) had WMH. The mortality rate per 1,000 person-years with CBI was 59.0 (95%CI 57.0-61.1); with WMH=46.5 (45.7-47.2); with neither=17.4 (17.1-17.7). In adjusted models, mortality risk associated with CBI was modified by age, e.g. HR 1.34 [1.21-1.48] at age 56.1 years vs HR 1.22 [1.17-1.28] at age 72 years. Mortality associated with WMH was modified by both age and imaging modality e.g., WMH on MRI at age 56.1 HR = 1.26 [1.18-1.35]; WMH on MRI at age 72 HR 1.15 [1.09-1.21]; WMH on CT at age 56.1 HR 1.41 [1.33-1.50]; WMH on CT at age 72 HR 1.28 [1.24-1.32], vs. patients without CBI or without WMH, respectively. Increasing WMH severity associated with higher mortality, e.g. mild WMH on MRI had adjusted HR=1.13 [1.06-1.20] while severe WMH on CT had HR=1.45 [1.33-1.59]. Incidentally-detected CBI and WMH on population-based clinical neuroimaging can predict higher mortality rates. We need treatments and healthcare planning for individuals with CCD.
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Affiliation(s)
- Úna Clancy
- Centre for Clinical Brain Sciences, Edinburgh Imaging, and UK Dementia Research Institute, University of Edinburgh, Edinburgh EH16 4SB, United Kingdom
| | - Eric J Puttock
- Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, California, USA
| | - Wansu Chen
- Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, California, USA
| | - William Whiteley
- Centre for Clinical Brain Sciences, Edinburgh Imaging, and UK Dementia Research Institute, University of Edinburgh, Edinburgh EH16 4SB, United Kingdom
| | - Ellen M Vickery
- Predictive Analytics and Comparative Effectiveness Center, Tufts Medical Center, Boston, Massachusetts, USA
| | - Lester Y Leung
- Department of Neurology, Tufts Medical Center, Boston, Massachusetts, USA
| | | | - David F Kallmes
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Sunyang Fu
- Center for Translational AI Excellence and Applications in Medicine, University of Texas Health Science Center, Houston, Texas, USA
| | - Chengyi Zheng
- Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, California, USA
| | - Hongfang Liu
- Center for Translational AI Excellence and Applications in Medicine, University of Texas Health Science Center, Houston, Texas, USA
| | - David M Kent
- Predictive Analytics and Comparative Effectiveness Center, Tufts Medical Center, Boston, Massachusetts, USA
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Malani SN, Acharya S, Shukla S. Current and Future Developments in Imaging and Treatment of White Matter Disease: A Systematic Review. Cureus 2023; 15:e51030. [PMID: 38264375 PMCID: PMC10804206 DOI: 10.7759/cureus.51030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 12/24/2023] [Indexed: 01/25/2024] Open
Abstract
The elderly often suffer from "mild" dementia due to white matter disease, which is another name for repeated brain infarctions. The degeneration of white matter, which links various parts of the brain to the spinal cord, is the root cause of this disorder, which develops with age. Dementia, imbalance, and movement problems are symptoms of this degenerative disease that worsen with age. This research's goal is to study current therapy options and identify methods for early diagnosis of white matter illness. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement for meta-analyses and systematic reviews served as the basis for our literature review. Results from the search in ScienceDirect and Medline/Pubmed led to the finalization of 33 studies. The complex relationship between white matter hyperintensities (WMHs) and neurological disorders is the subject of this comprehensive review, which sheds light on the varied terrain of WMH studies by highlighting their consequences and developing evaluation techniques.
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Affiliation(s)
- Sagar N Malani
- Department of Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research (Deemed to Be University), Wardha, IND
| | - Sourya Acharya
- Department of Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research (Deemed to Be University), Wardha, IND
| | - Samarth Shukla
- Department of Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research (Deemed to Be University), Wardha, IND
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3
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Meinel TR, Triulzi CB, Kaesmacher J, Mujanovic A, Pasi M, Leung LY, Kent DM, Sui Y, Seiffge D, Bücke P, Umarova R, Arnold M, Roten L, Nguyen TN, Wardlaw J, Fischer U. Management of covert brain infarction survey: A call to care for and trial this neglected population. Eur Stroke J 2023; 8:1079-1088. [PMID: 37427426 PMCID: PMC10683731 DOI: 10.1177/23969873231187444] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 06/26/2023] [Indexed: 07/11/2023] Open
Abstract
BACKGROUND Covert brain infarction (CBI) is highly prevalent and linked with stroke risk factors, increased mortality, and morbidity. Evidence to guide management is sparse. We sought to gain information on current practice and attitudes toward CBI and to compare differences in management according to CBI phenotype. METHODS We conducted a web-based, structured, international survey from November 2021 to February 2022 among neurologists and neuroradiologists. The survey captured respondents' baseline characteristics, general approach toward CBI and included two case scenarios designed to evaluate management decisions taken upon incidental detection of an embolic-phenotype and a small-vessel-disease phenotype. RESULTS Of 627 respondents (38% vascular neurologists, 24% general neurologists, and 26% neuroradiologists), 362 (58%) had a partial, and 305 (49%) a complete response. Most respondents were university hospital senior faculty members experienced in stroke, mostly from Europe and Asia. Only 66 (18%) of respondents had established institutional written protocols to manage CBI. The majority indicated that they were uncertain regarding useful investigations and further management of CBI patients (median 67 on a slider 0-100, 95% CI 35-81). Almost all respondents (97%) indicated that they would assess vascular risk factors. Although most would investigate and treat similarly to ischemic stroke for both phenotypes, including initiating antithrombotic treatment, there was considerable diagnostic and therapeutic heterogeneity. Less than half of respondents (42%) would assess cognitive function or depression. CONCLUSIONS There is a high degree of uncertainty and heterogeneity regarding management of two common types of CBI, even among experienced stroke physicians. Respondents were more proactive regarding the diagnostic and therapeutic management than the minimum recommended by current expert opinions. More data are required to guide management of CBI; meantime, more consistent approaches to identification and consistent application of current knowledge, that also consider cognition and mood, would be promising first steps to improve consistency of care.
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Affiliation(s)
- Thomas R Meinel
- Neurology, Stroke Research Center Bern, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Camilla B Triulzi
- Neurology, Stroke Research Center Bern, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Johannes Kaesmacher
- Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, and University of Bern, Bern, Switzerland
| | - Adnan Mujanovic
- Neurology, Stroke Research Center Bern, Bern University Hospital, University of Bern, Bern, Switzerland
- Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, and University of Bern, Bern, Switzerland
| | - Marco Pasi
- University of Lille, Inserm, CHU Lille, U1172-Lille Neuroscience & Cognition (LilNCog), Lille, France
| | - Lester Y Leung
- Department of Neurology, Tufts Medical Center, Boston, MA, USA
| | - David M Kent
- Predictive Analytics and Comparative Effectiveness Center, Tufts Medical Center, Boston, MA, USA
| | - Yi Sui
- Department of Neurology, The First Affiliated Hospital of China Medical University, Shenyang, China
- Department of Neurology, Shenyang First People’s Hospital, Shenyang Brain Institute, Shenyang, China
| | - David Seiffge
- Neurology, Stroke Research Center Bern, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Philipp Bücke
- Neurology, Stroke Research Center Bern, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Roza Umarova
- Neurology, Stroke Research Center Bern, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Marcel Arnold
- Neurology, Stroke Research Center Bern, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Laurent Roten
- Cardiology, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Thanh N Nguyen
- Neurology and Radiology, Boston Medical Center, Boston, MA, USA
| | - Joanna Wardlaw
- Division of Neuroimaging Sciences, Brain Research Imaging Centre, Centre for Clinical Brain Sciences, UK Dementia Research Institute at the University of Edinburgh, Edinburgh, UK
| | - Urs Fischer
- Neurology, Stroke Research Center Bern, Bern University Hospital, University of Bern, Bern, Switzerland
- Neurology, Basel University Hospital, University of Basel, Basel, Switzerland
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De Rosario H, Pitarch-Corresa S, Pedrosa I, Vidal-Pedrós M, de Otto-López B, García-Mieres H, Álvarez-Rodríguez L. Applications of Natural Language Processing for the Management of Stroke Disorders: Scoping Review. JMIR Med Inform 2023; 11:e48693. [PMID: 37672328 PMCID: PMC10512117 DOI: 10.2196/48693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 07/26/2023] [Accepted: 07/28/2023] [Indexed: 09/07/2023] Open
Abstract
BACKGROUND Recent advances in natural language processing (NLP) have heightened the interest of the medical community in its application to health care in general, in particular to stroke, a medical emergency of great impact. In this rapidly evolving context, it is necessary to learn and understand the experience already accumulated by the medical and scientific community. OBJECTIVE The aim of this scoping review was to explore the studies conducted in the last 10 years using NLP to assist the management of stroke emergencies so as to gain insight on the state of the art, its main contexts of application, and the software tools that are used. METHODS Data were extracted from Scopus and Medline through PubMed, using the keywords "natural language processing" and "stroke." Primary research questions were related to the phases, contexts, and types of textual data used in the studies. Secondary research questions were related to the numerical and statistical methods and the software used to process the data. The extracted data were structured in tables and their relative frequencies were calculated. The relationships between categories were analyzed through multiple correspondence analysis. RESULTS Twenty-nine papers were included in the review, with the majority being cohort studies of ischemic stroke published in the last 2 years. The majority of papers focused on the use of NLP to assist in the diagnostic phase, followed by the outcome prognosis, using text data from diagnostic reports and in many cases annotations on medical images. The most frequent approach was based on general machine learning techniques applied to the results of relatively simple NLP methods with the support of ontologies and standard vocabularies. Although smaller in number, there has been an increasing body of studies using deep learning techniques on numerical and vectorized representations of the texts obtained with more sophisticated NLP tools. CONCLUSIONS Studies focused on NLP applied to stroke show specific trends that can be compared to the more general application of artificial intelligence to stroke. The purpose of using NLP is often to improve processes in a clinical context rather than to assist in the rehabilitation process. The state of the art in NLP is represented by deep learning architectures, among which Bidirectional Encoder Representations from Transformers has been found to be especially widely used in the medical field in general, and for stroke in particular, with an increasing focus on the processing of annotations on medical images.
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Affiliation(s)
- Helios De Rosario
- Instituto de Biomecánica de Valencia, Universitat Politècnica de València, Valencia, Spain
| | | | - Ignacio Pedrosa
- CTIC Centro Tecnológico de la Información y la Comunicación, Gijón, Spain
| | - Marina Vidal-Pedrós
- Instituto de Biomecánica de Valencia, Universitat Politècnica de València, Valencia, Spain
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5
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Wen A, He H, Fu S, Liu S, Miller K, Wang L, Roberts KE, Bedrick SD, Hersh WR, Liu H. The IMPACT framework and implementation for accessible in silico clinical phenotyping in the digital era. NPJ Digit Med 2023; 6:132. [PMID: 37479735 PMCID: PMC10362064 DOI: 10.1038/s41746-023-00878-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Accepted: 07/13/2023] [Indexed: 07/23/2023] Open
Abstract
Clinical phenotyping is often a foundational requirement for obtaining datasets necessary for the development of digital health applications. Traditionally done via manual abstraction, this task is often a bottleneck in development due to time and cost requirements, therefore raising significant interest in accomplishing this task via in-silico means. Nevertheless, current in-silico phenotyping development tends to be focused on a single phenotyping task resulting in a dearth of reusable tools supporting cross-task generalizable in-silico phenotyping. In addition, in-silico phenotyping remains largely inaccessible for a substantial portion of potentially interested users. Here, we highlight the barriers to the usage of in-silico phenotyping and potential solutions in the form of a framework of several desiderata as observed during our implementation of such tasks. In addition, we introduce an example implementation of said framework as a software application, with a focus on ease of adoption, cross-task reusability, and facilitating the clinical phenotyping algorithm development process.
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Affiliation(s)
- Andrew Wen
- Department of AI & Informatics, Mayo Clinic, Rochester, MN, 55905, USA
- School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Huan He
- Department of AI & Informatics, Mayo Clinic, Rochester, MN, 55905, USA
| | - Sunyang Fu
- Department of AI & Informatics, Mayo Clinic, Rochester, MN, 55905, USA
- School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Sijia Liu
- Department of AI & Informatics, Mayo Clinic, Rochester, MN, 55905, USA
| | - Kurt Miller
- Department of AI & Informatics, Mayo Clinic, Rochester, MN, 55905, USA
| | - Liwei Wang
- Department of AI & Informatics, Mayo Clinic, Rochester, MN, 55905, USA
- School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Kirk E Roberts
- School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Steven D Bedrick
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, 97239, USA
| | - William R Hersh
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, 97239, USA
| | - Hongfang Liu
- Department of AI & Informatics, Mayo Clinic, Rochester, MN, 55905, USA.
- School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA.
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6
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Kent DM, Leung LY, Zhou Y, Luetmer PH, Kallmes DF, Nelson J, Fu S, Puttock EJ, Zheng C, Liu H, Chen W. Association of Incidentally Discovered Covert Cerebrovascular Disease Identified Using Natural Language Processing and Future Dementia. J Am Heart Assoc 2022; 12:e027672. [PMID: 36565208 PMCID: PMC9973577 DOI: 10.1161/jaha.122.027672] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Background Covert cerebrovascular disease (CCD) has been shown to be associated with dementia in population-based studies with magnetic resonance imaging (MRI) screening, but dementia risk associated with incidentally discovered CCD is not known. Methods and Results Individuals aged ≥50 years enrolled in the Kaiser Permanente Southern California health system receiving head computed tomography (CT) or MRI for nonstroke indications from 2009 to 2019, without prior ischemic stroke/transient ischemic attack, dementia/Alzheimer disease, or visit reason/scan indication suggestive of cognitive decline or stroke were included. Natural language processing identified incidentally discovered covert brain infarction (id-CBI) and white matter disease (id-WMD) on the neuroimage report; white matter disease was characterized as mild, moderate, severe, or undetermined. We estimated risk of dementia associated with id-CBI and id-WMD. Among 241 050 qualified individuals, natural language processing identified 69 931 (29.0%) with id-WMD and 11 328 (4.7%) with id-CBI. Dementia incidence rates (per 1000 person-years) were 23.5 (95% CI, 22.9-24.0) for patients with id-WMD, 29.4 (95% CI, 27.9-31.0) with id-CBI, and 6.0 (95% CI, 5.8-6.2) without id-CCD. The association of id-WMD with future dementia was stronger in younger (aged <70 years) versus older (aged ≥70 years) patients and for CT- versus MRI-discovered lesions. For patients with versus without id-WMD on CT, the adjusted HR was 2.87 (95% CI, 2.58-3.19) for older and 1.87 (95% CI, 1.79-1.95) for younger patients. For patients with versus without id-WMD on MRI, the adjusted HR for dementia risk was 2.28 (95% CI, 1.99-2.62) for older and 1.48 (95% CI, 1.32-1.66) for younger patients. The adjusted HR for id-CBI was 2.02 (95% CI, 1.70-2.41) for older and 1.22 (95% CI, 1.15-1.30) for younger patients for either modality. Dementia risk was strongly correlated with id-WMD severity; adjusted HRs compared with patients who were negative for id-WMD by MRI ranged from 1.41 (95% CI, 1.25-1.60) for those with mild disease on MRI to 4.11 (95% CI, 3.58-4.72) for those with severe disease on CT. Conclusions Incidentally discovered CCD is common and associated with a high risk of dementia, representing an opportunity for prevention. The association is strengthened when discovered at younger age, by increasing id-WMD severity, and when id-WMD is detected by CT scan rather than MRI.
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Affiliation(s)
- David M. Kent
- Predictive Analytics and Comparative Effectiveness Center, Tufts Medical CenterBostonMA
| | | | - Yichen Zhou
- Department of Research and EvaluationKaiser Permanente Southern CaliforniaPasadenaCA
| | | | | | - Jason Nelson
- Predictive Analytics and Comparative Effectiveness Center, Tufts Medical CenterBostonMA
| | - Sunyang Fu
- Department of AI and InformaticsMayo ClinicRochesterMN
| | - Eric J. Puttock
- Department of Research and EvaluationKaiser Permanente Southern CaliforniaPasadenaCA
| | - Chengyi Zheng
- Department of Research and EvaluationKaiser Permanente Southern CaliforniaPasadenaCA
| | - Hongfang Liu
- Department of AI and InformaticsMayo ClinicRochesterMN
| | - Wansu Chen
- Department of Research and EvaluationKaiser Permanente Southern CaliforniaPasadenaCA
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7
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Kent DM, Leung LY, Puttock EJ, Wang AY, Luetmer PH, Kallmes DF, Nelson J, Fu S, Zheng C, Vickery EM, Liu H, Noyce AJ, Chen W. Development of Parkinson Disease and Its Relationship with Incidentally Discovered White Matter Disease and Covert Brain Infarction in a Real-World Cohort. Ann Neurol 2022; 92:620-630. [PMID: 35866711 PMCID: PMC9489676 DOI: 10.1002/ana.26458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 07/18/2022] [Accepted: 07/20/2022] [Indexed: 11/08/2022]
Abstract
OBJECTIVE This study aimed to examine the relationship between covert cerebrovascular disease, comprised of covert brain infarction and white matter disease, discovered incidentally in routine care, and subsequent Parkinson disease. METHODS Patients were ≥50 years and received neuroimaging for non-stroke indications in the Kaiser Permanente Southern California system from 2009 to 2019. Natural language processing identified incidentally discovered covert brain infarction and white matter disease and classified white matter disease severity. The Parkinson disease outcome was defined as 2 ICD diagnosis codes. RESULTS 230,062 patients were included (median follow-up 3.72 years). A total of 1,941 Parkinson disease cases were identified (median time-to-event 2.35 years). Natural language processing identified covert cerebrovascular disease in 70,592 (30.7%) patients, 10,622 (4.6%) with covert brain infarction and 65,814 (28.6%) with white matter disease. After adjustment for known risk factors, white matter disease was associated with Parkinson disease (hazard ratio 1.67 [95%CI, 1.44, 1.93] for patients <70 years and 1.33 [1.18, 1.50] for those ≥70 years). Greater severity of white matter disease was associated with increased incidence of Parkinson disease(/1,000 person-years), from 1.52 (1.43, 1.61) in patients without white matter disease to 4.90 (3.86, 6.13) in those with severe disease. Findings were robust when more specific definitions of Parkinson disease were used. Covert brain infarction was not associated with Parkinson disease (adjusted hazard ratio = 1.05 [0.88, 1.24]). INTERPRETATION Incidentally discovered white matter disease was associated with subsequent Parkinson disease, an association strengthened with younger age and increased white matter disease severity. Incidentally discovered covert brain infarction did not appear to be associated with subsequent Parkinson disease. ANN NEUROL 2022;92:620-630.
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Affiliation(s)
- David M. Kent
- Predictive Analytics and Comparative Effectiveness Center,
Tufts Medical Center, Boston, MA, USA
| | - Lester Y. Leung
- Department of Neurology, Tufts Medical Center, Boston, MA,
USA
| | - Eric J. Puttock
- Department of Research and Evaluation, Kaiser Permanente
Southern California, Pasadena, CA, USA
| | - Andy Y. Wang
- Predictive Analytics and Comparative Effectiveness Center,
Tufts Medical Center, Boston, MA, USA
| | | | | | - Jason Nelson
- Predictive Analytics and Comparative Effectiveness Center,
Tufts Medical Center, Boston, MA, USA
| | - Sunyang Fu
- Department of AI and Informatics, Mayo Clinic, Rochester,
MN, USA
| | - Chengyi Zheng
- Department of Research and Evaluation, Kaiser Permanente
Southern California, Pasadena, CA, USA
| | - Ellen M. Vickery
- Predictive Analytics and Comparative Effectiveness Center,
Tufts Medical Center, Boston, MA, USA
| | - Hongfang Liu
- Department of AI and Informatics, Mayo Clinic, Rochester,
MN, USA
| | - Alastair J. Noyce
- Preventive Neurology Unit, Wolfson Institute of Population
Health, Queen Mary University of London, UK
- Department of Clinical and Movement Neuroscience, UCL
Institute of Neurology, London, UK
| | - Wansu Chen
- Department of Research and Evaluation, Kaiser Permanente
Southern California, Pasadena, CA, USA
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8
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Leung LY, Zhou Y, Fu S, Zheng C, Luetmer PH, Kallmes DF, Liu H, Chen W, Kent DM. Risk Factors for Silent Brain Infarcts and White Matter Disease in a Real-World Cohort Identified by Natural Language Processing. Mayo Clin Proc 2022; 97:1114-1122. [PMID: 35487789 PMCID: PMC9284412 DOI: 10.1016/j.mayocp.2021.11.038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 11/17/2021] [Accepted: 11/29/2021] [Indexed: 11/21/2022]
Abstract
OBJECTIVE To assess the frequency of silent brain infarcts (SBIs) and white matter disease (WMD) and associations with stroke risk factors (RFs) in a real-world population. PATIENTS AND METHODS This was an observational study of patients 50 years or older in the Kaiser Permanente Southern California health system from January 1, 2009, through June 30, 2019, with head computed tomography or magnetic resonance imaging for nonstroke indications and no history of stroke, transient ischemic attack, or dementia. A natural language processing (NLP) algorithm was applied to the electronic health record to identify individuals with reported SBIs or WMD. Multivariable Poisson regression estimated risk ratios of demographic characteristics, RFs, and scan modality on the presence of SBIs or WMD. RESULTS Among 262,875 individuals, the NLP identified 13,154 (5.0%) with SBIs and 78,330 (29.8%) with WMD. Stroke RFs were highly prevalent. Advanced age was strongly associated with increased risk of SBIs (adjusted relative risks [aRRs], 1.90, 3.23, and 4.72 for those aged in their 60s, 70s, and ≥80s compared with those in their 50s) and increased risk of WMD (aRRs, 1.79, 3.02, and 4.53 for those aged in their 60s, 70s, and ≥80s compared with those in their 50s). Magnetic resonance imaging was associated with a reduced risk of SBIs (aRR, 0.87; 95% CI, 0.83 to 0.91) and an increased risk of WMD (aRR, 2.86; 95% CI, 2.83 to 2.90). Stroke RFs had modest associations with increased risk of SBIs or WMD. CONCLUSION An NLP algorithm can identify a large cohort of patients with incidentally discovered SBIs and WMD. Advanced age is strongly associated with incidentally discovered SBIs and WMD.
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Affiliation(s)
- Lester Y Leung
- Department of Neurology, Tufts Medical Center, Boston, MA, USA.
| | - Yichen Zhou
- Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA
| | - Sunyang Fu
- Department of AI and Informatics, Mayo Clinic, Rochester, MN, USA
| | - Chengyi Zheng
- Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA
| | | | | | - Hongfang Liu
- Department of AI and Informatics, Mayo Clinic, Rochester, MN, USA
| | - Wansu Chen
- Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA
| | - David M Kent
- Predictive Analytics and Comparative Effectiveness Center, Tufts Medical Center, Boston, MA, USA
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