دورية أكاديمية
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FAST-IT: F ind A S imple T est - I n T IA (transient ischaemic attack): a prospective cohort study to develop a multivariable prediction model for diagnosis of TIA through proteomic discovery and candidate lipid mass spectrometry, neuroimaging and machine learning-study protocol.

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العنوان: FAST-IT: F ind A S imple T est - I n T IA (transient ischaemic attack): a prospective cohort study to develop a multivariable prediction model for diagnosis of TIA through proteomic discovery and candidate lipid mass spectrometry, neuroimaging and machine learning-study protocol.
المؤلفون: Milton AG; Stroke Research Programme, Central Adelaide Local Health Network, Adelaide, South Australia, Australia austin.milton@adelaide.edu.au., Lau S; Faculty of Engineering, Computer and Mathematical Sciences, Australian Institute for Machine Learning, The University of Adelaide, Adelaide, South Australia, Australia.; South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia., Kremer KL; Adelaide Medical School, Stroke Research Programme, The University of Adelaide Faculty of Health and Medical Sciences, Adelaide, South Australia, Australia., Rao SR; Proteomics, Metabolomics and MS-imaging Core Facility, South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia.; Adelaide Medical School, The University of Adelaide Faculty of Health and Medical Sciences, Adelaide, South Australia, Australia., Mas E; Adelaide Medical School, The University of Adelaide Faculty of Health and Medical Sciences, Adelaide, South Australia, Australia.; SA Pathology - Genetics and Molecular Pathology, Women's and Children's Hospital Adelaide, North Adelaide, South Australia, Australia., Snel MF; Proteomics, Metabolomics and MS-imaging Core Facility, South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia.; Adelaide Medical School, The University of Adelaide Faculty of Health and Medical Sciences, Adelaide, South Australia, Australia., Trim PJ; Proteomics, Metabolomics and MS-imaging Core Facility, South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia.; Adelaide Medical School, The University of Adelaide Faculty of Health and Medical Sciences, Adelaide, South Australia, Australia., Sharma D; South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia.; Adelaide Medical School, Stroke Research Programme, The University of Adelaide Faculty of Health and Medical Sciences, Adelaide, South Australia, Australia., Edwards S; Adelaide Health Technology Assessment, The University of Adelaide Faculty of Health and Medical Sciences, Adelaide, South Australia, Australia., Jenkinson M; Faculty of Engineering, Computer and Mathematical Sciences, Australian Institute for Machine Learning, The University of Adelaide, Adelaide, South Australia, Australia.; South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia., Kleinig T; Adelaide Medical School, The University of Adelaide Faculty of Health and Medical Sciences, Adelaide, South Australia, Australia.; Department of Neurology, Royal Adelaide Hospital, Adelaide, South Australia, Australia., Noschka E; Adelaide Medical School, Stroke Research Programme, The University of Adelaide Faculty of Health and Medical Sciences, Adelaide, South Australia, Australia.; School of Animal and Veterinary Sciences, The University of Adelaide, Adelaide, South Australia, Australia., Hamilton-Bruce MA; Stroke Research Programme, Central Adelaide Local Health Network, Adelaide, South Australia, Australia.; Adelaide Medical School, Stroke Research Programme, The University of Adelaide Faculty of Health and Medical Sciences, Adelaide, South Australia, Australia., Koblar SA; Adelaide Medical School, Stroke Research Programme, The University of Adelaide Faculty of Health and Medical Sciences, Adelaide, South Australia, Australia.
المصدر: BMJ open [BMJ Open] 2022 Apr 01; Vol. 12 (4), pp. e045908. Date of Electronic Publication: 2022 Apr 01.
نوع المنشور: Journal Article; Research Support, Non-U.S. Gov't
اللغة: English
بيانات الدورية: Publisher: BMJ Publishing Group Ltd Country of Publication: England NLM ID: 101552874 Publication Model: Electronic Cited Medium: Internet ISSN: 2044-6055 (Electronic) Linking ISSN: 20446055 NLM ISO Abbreviation: BMJ Open Subsets: MEDLINE
أسماء مطبوعة: Original Publication: [London] : BMJ Publishing Group Ltd, 2011-
مواضيع طبية MeSH: Ischemic Attack, Transient*/diagnostic imaging, Humans ; Lipids ; Machine Learning ; Mass Spectrometry ; Neuroimaging ; Prospective Studies ; Proteomics
مستخلص: Introduction: Transient ischaemic attack (TIA) may be a warning sign of stroke and difficult to differentiate from minor stroke and TIA-mimics. Urgent evaluation and diagnosis is important as treating TIA early can prevent subsequent strokes. Recent improvements in mass spectrometer technology allow quantification of hundreds of plasma proteins and lipids, yielding large datasets that would benefit from different approaches including machine learning. Using plasma protein, lipid and radiological biomarkers, our study will develop predictive algorithms to distinguish TIA from minor stroke (positive control) and TIA-mimics (negative control). Analysis including machine learning employs more sophisticated modelling, allowing non-linear interactions, adapting to datasets and enabling development of multiple specialised test-panels for identification and differentiation.
Methods and Analysis: Patients attending the Emergency Department, Stroke Ward or TIA Clinic at the Royal Adelaide Hospital with TIA, minor stroke or TIA-like symptoms will be recruited consecutively by staff-alert for this prospective cohort study. Advanced neuroimaging will be performed for each participant, with images assessed independently by up to three expert neurologists. Venous blood samples will be collected within 48 hours of symptom onset. Plasma proteomic and lipid analysis will use advanced mass spectrometry (MS) techniques. Principal component analysis and hierarchical cluster analysis will be performed using MS software. Output files will be analysed for relative biomarker quantitative differences between the three groups. Differences will be assessed by linear regression, one-way analysis of variance, Kruskal-Wallis H-test, χ 2 test or Fisher's exact test. Machine learning methods will also be applied including deep learning using neural networks.
Ethics and Dissemination: Patients will provide written informed consent to participate in this grant-funded study. The Central Adelaide Local Health Network Human Research Ethics Committee approved this study (HREC/18/CALHN/384; R20180618). Findings will be disseminated through peer-reviewed publication and conferences; data will be managed according to our Data Management Plan (DMP2020-00062).
Competing Interests: Competing interests: None declared.
(© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)
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فهرسة مساهمة: Keywords: CT; MRI; health informatics; protocols & guidelines; stroke
المشرفين على المادة: 0 (Lipids)
تواريخ الأحداث: Date Created: 20220402 Date Completed: 20220405 Latest Revision: 20220524
رمز التحديث: 20240628
مُعرف محوري في PubMed: PMC8977752
DOI: 10.1136/bmjopen-2020-045908
PMID: 35365506
قاعدة البيانات: MEDLINE
الوصف
تدمد:2044-6055
DOI:10.1136/bmjopen-2020-045908