## 4 Discussion  
In the present study, we demonstrate that lipidomics in combination with computational modeling can be used for the prediction of atherosclerotic plaque progression. Overall plaque progression was defined when at least two out of the following three conditions occurred: 20% lumen reduction, 20% plaque increase, 20% plaque burden increase. In the current literature, there is not a fixed threshold for each of the three parameters to define the progression of an atherosclerotic plaque. Several studies have applied different thresholds ranging from around 10% to more than 20% change [6; 18]; thus, using a conservative approach, we selected the highest threshold. In the current analysis, several cut-offs were examined considering that <15% was not investigated because it is questionable that CCTA can assess minor plaque change, and >25% was not examined because few patients present such disease progression. The cut-off of 20% presented the best balance between  
Figure 3: ROC curves of the risk score models comparing the predictive effect of each different feature sets. the patients without (N = 1556) and with plaque progression (N = 2443) at 3 mm segment analysis. We performed 3D reconstruction of 480 main coronary vessels from 187 patients for blood flow and LDL transport modeling. The results of the present study show that a lower baseline plaque burden coupled with a lower ESS and a higher LDL accumulation in the vessel wall are good predictors of coronary plaque progression, in particular of lumen coronary reduction >20% in a 6-year follow-up interval. CCTA, in combination with computational modeling, showed 88% accuracy for the prediction of plaque progression. Additionally, computational modeling affects the predictability of the results, since in most individual cases, a low minimum ESS or an increased accumulation of LDL at baseline are associated with plaque progression or lumen reduction. SmartFFR can also be used as an additional predictor, even if not independently associated at multivariate analysis. Prediction of atherosclerotic plaque growth was attempted in previous studies, mainly based on invasive imaging modalities, where increased plaque burden at baseline and low ESS have been reported as independent predictors of plaque progression, in terms of lumen narrowing [3,19]. Those findings are partially in agreement with the present results. Low ESS was also a predictor of plaque progression in these studies, while baseline plaque burden showed the opposite results with our study. This discrepancy can be due to the specific characteristics of the SMARTool population, which included mainly patients with a low prevalence of obstructive CAD at enrolment. In this population, the majority of patients showing a relatively low coronary atherosclerotic burden were more likely to develop plaque progression. The incremental prediction value of biomechanical factors and ESS was already demonstrated [3,5,10]. CCTA efficacy and high cost-effectiveness is well known [20,21]. Its applicability and capacity to detect cardiovascular disease has been further improved recently by the introduction of advanced technology, allowing higher spatial and temporal resolution with increased imaging quality and reduced radiation exposure. Our study demonstrated that computational modeling of non-invasive coronary imaging by CCTA may allow accurate prediction of plaque progression without the need of invasive procedures and may warrant a cost-effective prevention of CAD. Studies focused on serial CCTA imaging to assess disease progression using blood flow simulation to estimate ESS have also demonstrated, in accordance with our findings, that low ESS is an independent predictor of lumen reduction or plaque burden increase [5,6]. The addition of the more advanced computational modeling of LDL transport and accumulation in the arterial wall increases the predictability results even more [10]. Our results were obtained in a larger population at medium-low risk and with a low prevalence of obstructive CAD.