Model development and validation of noninvasive parameters based on coronary computed tomography angiography to predict culprit lesions in acute coronary syndromes within 3 years: value of plaque characteristics, hemodynamics and pericoronary adipose tissue.

Background: Machine learning (ML) is combined with noninvasive parameters from coronary computed tomography angiography (CTA) to construct predictive models to identify culprit lesions that may lead to acute coronary syndrome (ACS). Methods: We retrospectively analyzed 132 patients with ACS at the Fourth Affiliated Hospital of Harbin Medical University who had coronary CTA between 3 months and 3 years before the ACS event, with a total of 240 lesions. Lesions from 2020 (n=154) were included in the training set, and lesions from 2021 (n=86) were included in the test set for internal validation. We evaluated the role of plaque characteristics, hemodynamic parameters and pericoronary adipose tissue (PCAT) attenuation from CTA in identifying culprit ACS lesions. In the training set, logistic regression was used to screen CTA-derived parameters with P values <0.05 for the model construction. Logistic regression, random forest, Bayesian and K-nearest neighbor algorithms were used to build classification models, and their performance was assessed using the test set. The following models were established to evaluate the effectiveness of different combinations of models to identify culprit lesions: Model 1 was established for plaque characteristics; Model 2 was established for hemodynamic parameters; Model 3 was established for PCAT attenuation; Model 4 was established for plaque characteristics and hemodynamic parameters; and Model 5 was established for plaque characteristics, hemodynamic parameters and PCAT attenuation. Results: A total of ten high-risk factors were screened for the ML model construction, and the ML model based on the logistic regression algorithm had the best performance among the four algorithms (accuracy =0.721; sensitivity =0.892; specificity =0.592; positive prediction =0.623; and negative prediction =0.879). In this model, the minimum lumen area, positive remodeling and lesion-specific fat attenuation index (FAI) were the risk factors significantly associated with the culprit lesion. Analysis of the effect of different combinations of models to identify culprit lesions showed that Model 5 had the best predictive effect (AUC =0.819 and 95% CI: 0.722-0.916). Conclusions: ACS can be predicted using ML based on CTA parameters. Compared to other models, the model combining plaque characteristics, hemodynamic parameters and PCAT attenuation performed best in predicting the culprit lesion.

Identification of patients with acute coronary syndrome and representation of their degree of inflammation: application of pericoronary adipose tissue within different radial distances of the proximal coronary arteries.

Background: Pericoronary adipose tissue (PCAT) around the proximal right coronary artery (RCA) is considered a marker of coronary inflammation. We aimed to explore the segments of PCAT that represent coronary inflammation in patients with acute coronary syndrome (ACS) and to identify patients with ACS and stable coronary artery disease (CAD) prior to intervention. Methods: We retrospectively enrolled consecutive patients with ACS and stable CAD who underwent invasive coronary angiography (ICA) after coronary computed tomography angiography (CCTA) from November 2020 to October 2021 at the Fourth Affiliated Hospital of Harbin Medical University. The fat attenuation index (FAI) was obtained using PCAT quantitative measurement software, and the coronary Gensini score was also calculated to indicate the severity of CAD. The differences and correlations between FAI within different radial distances of proximal coronary arteries were evaluated, and the recognition ability of FAI for patients with ACS and stable CAD was evaluated by establishing receiver operator characteristic (ROC) curves. Results: A total of 267 patients were included in the cross-sectional study, including 173 patients with ACS. With the increase of radial distance from the outer wall of proximal coronary vessels, the FAI decreased (P<0.001). The FAI around the proximal left anterior descending artery (LAD) within the reference diameter from the outer wall of the vessel (LADref) had the highest correlation with the FAI around culprit lesions [r=0.587; 95% confidence interval (CI): 0.489-0.671; P<0.001]. The model based on clinical features, Gensini score, and LADref had the highest recognition performance for patients with ACS and stable CAD [area under the curve (AUC): 0.663; 95% CI: 0.540-0.785]. Conclusions: LADref is most correlated with FAI around culprit lesions in patients with ACS and has higher value in the preintervention differentiation of patients with ACS and stable CAD compared to the use of clinical features alone.

Diagnosis of coronary artery disease in patients with type 2 diabetes mellitus based on computed tomography and pericoronary adipose tissue radiomics: a retrospective cross-sectional study.

BACKGROUND: Patients with type 2 diabetes mellitus (T2DM) are highly susceptible to cardiovascular disease, and coronary artery disease (CAD) is their leading cause of death. We aimed to assess whether computed tomography (CT) based imaging parameters and radiomic features of pericoronary adipose tissue (PCAT) can improve the diagnostic efficacy of whether patients with T2DM have developed CAD. METHODS: We retrospectively recruited 229 patients with T2DM but no CAD history (146 were diagnosed with CAD at this visit and 83 were not). We collected clinical information and extracted imaging manifestations from CT images and 93 radiomic features of PCAT from all patients. All patients were randomly divided into training and test groups at a ratio of 7:3. Four models were constructed, encapsulating clinical factors (Model 1), clinical factors and imaging indices (Model 2), clinical factors and Radscore (Model 3), and all together (Model 4), to identify patients with CAD. Receiver operating characteristic curves and decision curve analysis were plotted to evaluate the model performance and pairwise model comparisons were performed via the DeLong test to demonstrate the additive value of different factors. RESULTS: In the test set, the areas under the curve (AUCs) of Model 2 and Model 4 were 0.930 and 0.929, respectively, with higher recognition effectiveness compared to the other two models (each p < 0.001). Of these models, Model 2 had higher diagnostic efficacy for CAD than Model 1 (p < 0.001, 95% CI [0.129-0.350]). However, Model 4 did not improve the effectiveness of the identification of CAD compared to Model 2 (p = 0.776); similarly, the AUC did not significantly differ between Model 3 (AUC = 0.693) and Model 1 (AUC = 0.691, p = 0.382). Overall, Model 2 was rated better for the diagnosis of CAD in patients with T2DM. CONCLUSIONS: A comprehensive diagnostic model combining patient clinical risk factors with CT-based imaging parameters has superior efficacy in diagnosing the occurrence of CAD in patients with T2DM.

Effect of 320-row CT reconstruction technology on fractional flow reserve derived from coronary CT angiography based on machine learning: single- versus multiple-cardiac periodic images.

Background: Fractional flow reserve derived from computed tomography (CT-FFR) can be used to noninvasively evaluate the functions of coronary arteries and has been widely welcomed in the field of cardiovascular research. However, whether different image reconstruction schemes have an effect on CT-FFR analysis through single- and multiple-cardiac periodic images in the same patient has not been investigated. Methods: This study retrospectively enrolled 122 patients who underwent 320-row computed tomography (CT) examination with both single- and multiple-cardiac periodic reconstruction schemes; a total of 366 coronary arteries were analyzed. The lowest CT-FFR values of each vessel and the poststenosis CT-FFR values of the lesion-specific coronary artery were measured using the two reconstruction techniques. The Wilcoxon signed-rank test was used to compare differences in CT-FFR values between the two reconstruction techniques. Spearman correlation analysis was performed to determine the relationship between CT-FFR values derived using the two methods. Bland-Altman and intraclass correlation coefficient (ICC) analyses were performed to evaluate the consistency of CT-FFR values. Results: In all blood vessels, the lowest CT-FFR values showed no significant differences between the two reconstruction techniques in the left anterior descending artery (LAD; P=0.65), left circumflex artery (LCx; P=0.46), or right coronary artery (RCA; P=0.22). In blood vessels with atherosclerotic plaques, the poststenosis CT-FFR values (2 cm distal to the maximum stenosis) exhibited no significant differences between the two reconstruction techniques in the LAD (P=0.78), LCx (P=1.00), or RCA (P=1.00). The mean CT-FFR values of single- and multiple-cardiac periodic images showed excellent correlation and minimal bias in all groups. Conclusions: CT-FFR analysis based on an artificial intelligence deep learning neural network is stable and not affected by the type of 320-row CT reconstruction technology.

Identification of patients with acute myocardial infarction based on coronary CT angiography: the value of pericoronary adipose tissue radiomics.

OBJECTIVE: To determine whether radiomics analysis of pericoronary adipose tissue (PCAT) captured by coronary computed tomography angiography (CCTA) could discriminate acute myocardial infarction (MI) from unstable angina (UA). METHODS: In a single-center retrospective case-control study, patients with acute MI (n = 105) were matched to patients with UA (n = 105) and all patients were randomly divided into training and validation cohorts with a ratio of 7:3. Fat attenuation index (FAI) and PCAT radiomics features selected by Max-Relevance and Min-Redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) around the proximal three major epicardial coronary vessels (LAD [left anterior descending artery], LCx [left circumflex artery], and RCA [right coronary artery]) were used to build logistic regression models. Finally, a FAI model, three radiomics models of PCAT (LAD, LCx, and RCA), and a combined model that used the scores of these independent models were constructed. The performance of the models was evaluated by identification, calibration, and clinical application. RESULTS: In training and validation cohorts, compared with the FAI model (AUC = 0.53, 0.50), the combined model achieved superior performance (AUC = 0.97, 0.95) while there was a significant difference of AUC between two models (p < 0.05). The calibration curves of the combined model demonstrated the smallest Brier score loss. Decision curve analysis suggested that the combined model provided higher clinical benefit than the FAI model. CONCLUSIONS: The CCTA-based radiomics phenotype of PCAT outperforms the FAI model in discriminating acute MI from UA. The combination of PCAT radiomics and FAI could further enhance the performance of acute MI identification. KEY POINTS: • Fat attenuation index based on CCTA can detect inflammation-induced changes in the ratio of lipid to aqueous phase in pericoronary adipose tissue. • Fat attenuation index cannot distinguish acute MI patients from UA patients, suggesting that the two groups have the same degree of ratio of lipid to aqueous phase in pericoronary adipose tissue. • Radiomics features of PCAT have the potential to distinguish acute MI patients from UA patients.