Machine learning assisted reflectance spectral analysis of coronary thrombi correlated significantly with the index of microcirculatory resistance (R2 0.80, p<0.0001) and microvascular obstruction (R2 0.64, p=0.02) in patients with STEACS.
Observational (n=306)
No
Does machine learning-assisted reflectance spectral characterization of coronary thrombi correlate with microvascular injury in patients with ST-segment elevation acute coronary syndrome?
Machine learning-assisted reflectance spectral analysis of aspirated coronary thrombi provides a reliable measure of thrombus composition that correlates significantly with coronary microvascular injury in patients with STEACS.
Effect estimate: R2 0.80
p-value: p=<0.0001
Aims We set out to further develop reflectance spectroscopy for the characterisation and quantification of coronary thrombi. Additionally, we explore the potential of our approach for use as a risk stratification tool by exploring the relation of reflectance spectra to indices of coronary microvascular injury. Methods and results We performed hyperspectral imaging of coronary thrombi aspirated from 306 patients presenting with ST-segment elevation acute coronary syndrome (STEACS). Spatially resolved reflected light spectra were analysed using unsupervised machine learning approaches. Invasive index of coronary microvascular resistance (IMR) and non-invasive microvascular obstruction (MVO) at cardiac magnetic resonance imaging indices of coronary microvascular injury were measured in a sub-cohort of 36 patients. The derived spectral signatures of coronary thrombi were correlated with both invasive and non-invasive indices of coronary microvascular injury. Successful machine-learning-based classification of the various thrombus image components, including differentiation between blood and thrombus, was achieved when classifying the pixel spectra into 11 groups. Fitting of the spectra to basis spectra recorded for separated blood components confirmed excellent correlation with visually inspected thrombi. In the 36 patients who underwent successful thrombectomy, spectral signatures were found to correlate well with the index of microcirculatory resistance and microvascular obstruction; R 2 : 0.80, p 0.0001, n = 21 and R 2 : 0.64, p = 0.02, n = 17, respectively. Conclusion Machine learning assisted reflectance spectral analysis can provide a measure of thrombus composition and evaluate coronary microvascular injury in patients with STEACS. Future work will further validate its deployment as a point-of-care diagnostic and risk stratification tool for STEACS care.
Kotronias et al. (Tue,) conducted a observational in ST-segment elevation acute coronary syndrome (STEACS) (n=306). Machine learning assisted reflectance spectral characterisation was evaluated on Correlation of spectral signatures with index of microcirculatory resistance (IMR) (R2 0.80, p=<0.0001). Machine learning assisted reflectance spectral analysis of coronary thrombi correlated significantly with the index of microcirculatory resistance (R2 0.80, p<0.0001) and microvascular obstruction (R2 0.64, p=0.02) in patients with STEACS.