Correlation Engine 2.0
Clear Search sequence regions

  • attack (12)
  • blood (1)
  • cluster analysis (1)
  • cohort study (2)
  • diagnosis (2)
  • HREC (1)
  • humans (1)
  • lipid (6)
  • mass (5)
  • minor (3)
  • patients (2)
  • plasma (1)
  • plasma proteins (2)
  • research ethics committee (1)
  • sign (1)
  • stroke (6)
  • χ2 test (1)
  • Sizes of these terms reflect their relevance to your search.

    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. 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, χ2 test or Fisher's exact test. Machine learning methods will also be applied including deep learning using neural networks. 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). © 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.


    Austin G Milton, Stephan Lau, Karlea L Kremer, Sushma R Rao, Emilie Mas, Marten F Snel, Paul J Trim, Deeksha Sharma, Suzanne Edwards, Mark Jenkinson, Timothy Kleinig, Erik Noschka, Monica Anne Hamilton-Bruce, Simon A Koblar. FAST-IT: Find A Simple Test - In TIA (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. BMJ open. 2022 Apr 01;12(4):e045908

    Expand section icon Mesh Tags

    Expand section icon Substances

    PMID: 35365506

    View Full Text