Why the study?
Angio-FFR accuracy is limited by assumptions regarding coronary microvascular resistance (CMVR), prompting evaluation of machine learning techniques to provide patient-specific CMVR estimates.
Does incorporating machine learning-predicted coronary microvascular resistance based on clinical and echocardiographic data improve the diagnostic accuracy of angiography-derived FFR in patients with chronic coronary syndromes?
Does incorporating machine learning-predicted coronary microvascular resistance based on clinical and echocardiographic data improve the diagnostic accuracy of angiography-derived FFR in patients with chronic coronary syndromes?
Personalizing coronary microvascular resistance estimation using machine learning and standard clinical/echocardiographic data significantly improves the accuracy of angiography-derived FFR.
ML-predicted CMVR may refine angio-FFR accuracy; leaves open clinical adoption pending prospective validation.
Aims Angiography-derived fractional flow reserve (angio-FFR) permits physiological lesion assessment without the need for an invasive pressure wire or induction of hyperaemia. However, accuracy is limited by assumptions made when defining the distal boundary, namely coronary microvascular resistance (CMVR). We sought to determine whether machine learning (ML) techniques could provide a patient-specific estimate of CMVR and therefore improve the accuracy of angio-FFR. Methods and results Patients with chronic coronary syndromes underwent coronary angiography with FFR assessment. Vessel-specific CMVR was computed using a three-dimensional computational fluid dynamics simulation with invasively measured proximal and distal pressures applied as boundary conditions. Predictive models were created using non-linear autoregressive moving average with exogenous input (NARMAX) modelling with computed CMVR as the dependent variable. Angio-FFR (VIRTUheart™) was computed using previously described methods. Three simulations were run: using a generic CMVR value (Model A); using ML-predicted CMVR based upon simple clinical data (Model B); and using ML-predicted CMVR also incorporating echocardiographic data (Model C). The diagnostic (FFR ≤ or >0.80) and absolute accuracies of these models were compared. Eighty-four patients underwent coronary angiography with FFR assessment in 157 vessels. The mean measured FFR was 0.79 (±0.15). The diagnostic and absolute accuracies of each personalized model were: (A) 73% and ±0.10; (B) 81% and ±0.07; and (C) 89% and ±0.05, P < 0.001. Conclusion The accuracy of angio-FFR was dependent in part upon CMVR estimation. Personalization of CMVR from standard clinical data resulted in a significant reduction in angio-FFR error.
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Gosling et al. (2022) studied this question.
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