Patients and study protocol
This study protocol was approved by the local institutional ethics committee, and patients provided written informed consent. Between 2010 and 2013, consecutive patients with an intermediate-grade stenosis on computed tomography angiography involving one or two major epicardial coronary vessels >1.5mm in diameter were eligible for a study requiring both catheter coronary angiography with FFR measurements and adenosine perfusion MRI within 4 months as previously reported [12]. MRI examinations were performed on a 1.5T MR scanner (Avanto, Siemens Healthineers), as previously reported [12]. In short, the examination consisted in performing stress and resting perfusion on dynamic contrast-enhancement imaging (each using 0.1 mmol/kg Gadodiamide, OmniscanÒ), and late-gadolinium enhancement (LGE) in the same three short-axis positions (see the supplementary materials for more details). These prospectively acquired data were analyzed retrospectively as follows: visually twice with one-year interval, the second reading with knowledge of the diseased coronary artery; semi-quantitatively and using machine learning analysis to confirm the added value of accounting for perfusion changes in remote myocardium in predicting the FFR value.
MRI analysis
Visual analysis
Two readers (AN, JND) with more than 10-years of experience in cardiac MRI, blinded to patient’s characteristics, history and coronary angiography and FFR findings, performed twice an individual visual analysis of perfusion MRI, using dedicated software (Syngo ViaÒ, Siemens Health). First, splenic switch-off was qualitatively assessed to evaluate the appropriateness of the vasodilatation response after adenosine administration [13] . Myocardial ischemia was defined as stress-induced myocardial perfusion defect in the absence of LGE in the same segment, as previously reported [3]. The readers had no common training before the study and had freedom to adjust the display window level and width. In a final step, all reading discordances were solved by consensus. Twelve months after the first readings, a second round of individual and consensus visual analysis was performed by the same readers who then were provided with full knowledge of the coronary stenosis location, but still blinded to the FFR data.
Semi-quantitative analysis
Semi-quantitative analysis was performed under the supervision of an expert in cardiac MRI using dedicated software (MOCO, Syngo ViaVA30Ò, cardiac Engine-perfusion module, Siemens Healthineers). This operator had full knowledge of the location of the stenosis, but was blinded to the FFR data. He performed a visual analysis using the same scheme as the 2 other readers to determine areas of myocardial perfusion defects.
As previously described [12], equally divided subendocardial (END) and subepicardial (EPI) regions of interest and time-signal intensity curves were obtained during adenosine stress in the myocardium distal to the stenosis (=RISK). When the RISK area involved more than one segment of the left ventricle representation, the myocardial segment with the greatest lateral and transmural extent of the perfusion defect was used for further measurements. Then, similar curves were obtained for a remote myocardial segment without a stenosis ≥40% diameter reduction on the supplying artery on QCA (=REMOTE) (Figure 1).
When no myocardial perfusion defect was visualized, the RISK segment was defined distal to the anatomic location of the coronary stenosis and the remaining steps were performed as when a perfusion defect could be visually detected. In patients with more than one intermediate-grade stenosis, each corresponding area of myocardial supply was assessed separately. Subsequently, these regions of interest were copy-pasted on the resting perfusion images. If necessary, manual correction was made to adjust the region of interest placement.
The stress subendocardial RMPI (i.e.: RISK/REMOTE mean maximal enhancement upslopes) of each stenosis was assessed for the diagnosis of FFR≤0.80 stenosis, as previously reported [12].
Maximal enhancement upslopes derived from subendocardial and subepicardial time-signal intensity curves per RISK and REMOTE areas (n=8, Figure 1) for each of the stenoses were normalized to the respective left ventricle cavity enhancement maximal upslope and used for Boosting machine learning.
Statistical analysis
Statistical analyses were performed using R (version 3.2.3, with the model-based boosting package 2.6-0). Normally distributed continuous variables are expressed as mean +/- standard deviation (SD). Comparisons between continuous variables were performed using two-tailed Student t-tests, and comparisons of proportions were performed using χ² tests.
A regression model was fitted to determine the best cut-off value for stress subendocardial RMPI in predicting FFR≤0.80. Then, boosting models including all semi-quantitative parameters collected in the RISK and REMOTE myocardium were used to predict FFR-related outcomes and to validate the added value of accounting for perfusion parameters in REMOTE myocardium in the prediction of these outcomes. As more extensively described in the supplementary materials, the Boosting models were assessed with the Akaike Information Criterion (AIC) value. Subsequently, the best model was optimized for a categorical outcome (i.e.: predicting FFR≤0.80). The diagnostic values were expressed as sensitivity, specificity, positive predictive value, negative predictive value, likelihood ratios and accuracy. The diagnostic accuracies were compared between visual readings, stress subendocardial RMPI and the boosting predictive model for FFR≤0.80, using binomial exact tests. P-values<0.05 were considered to express a statistically significant difference.