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

Milton, AG; Lau, S; Kremer, KL; Rao, SR; Mas, E; Snel, MF; Trim, PJ; Sharma, D; Edwards, S; Jenkinson, M; Kleinig, T; Noschka, E; Hamilton-Bruce, MA; Koblar, SA

Journal article

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

Abstract:: 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, χ2 test or Fisher’s exact test. Machine learning methods will also be applied including deep learning using neural networks.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 Kobla

Publication status:: Published

Peer review status:: Peer reviewed

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APA Style

Milton, A. G., Lau, S., Kremer, K. L., Rao, S. R., Mas, E., Snel, M. F., Trim, P. J., Sharma, D., Edwards, S., Jenkinson, M., Kleinig, T., Noschka, E., Hamilton-Bruce, M. A., & Koblar, S. A. (2022). 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. BMJ Open, 12(4), e045908–e045908.

MLA Style

Milton, AG, et al. “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.” BMJ Open, vol. 12, no. 4, 2022, pp. e045908–e045908.

Chicago Style

Milton, AG, S Lau, KL Kremer, et al. 2022. “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.” BMJ Open 12 (4): e045908–e045908.
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