Serial coronary CT angiography-derived fractional flow reserve and plaque progression can predict long-term outcomes of coronary artery disease

Prediction of plaque progression using different machine learning models of pericoronary adipose tissue radiomics based on coronary computed tomography angiography

Objectives

To develop and validate the value of different machine learning models of pericoronary adipose tissue (PCAT) radiomics based on coronary computed tomography angiography (CCTA) for predicting coronary plaque progression (PP).

Methods

This retrospective study evaluated 97 consecutive patients (with 127 plaques: 40 progressive and 87 nonprogressive) who underwent serial CCTA examinations. We analyzed conventional parameters and PCAT radiomics features. PCAT radiomics models were constructed using logistic regression (LR), K-nearest neighbors (KNN), and random forest (RF). Logistic regression analysis was applied to identify variables for developing conventional parameter models. Model performances were assessed by metrics including area under the curve (AUC), accuracy, sensitivity, and specificity.

Results

At baseline CCTA, 93 radiomics features were extracted from CCTA images. After dimensionality reduction and feature selection, two radiomics features were deemed valuable. Among radiomics models, we selected the RF as the optimal model in the training and validation sets (AUC = 0.971, 0.821). At follow-up CCTA, logistic regression analysis showed that increase in fat attenuation index (FAI) and decrease in PCAT volume were independent predictors of PP. The predictive capability of the combined model (increase in FAI + decrease in PCAT volume) was the best in the training and validation sets (AUC = 0.907, 0.882).

Conclusions

At baseline CCTA, the RF-based PCAT radiomics model demonstrated excellent predictive ability for PP. Furthermore, at follow-up CCTA, our results indicated that both increase in FAI and decrease in PCAT volume can independently predict PP, and their combination provided enhanced predictive ability.

Suboptimal Control of Small Dense Low-Density Lipoprotein Cholesterol Is Associated With Coronary Plaque Progression: An Intravascular Ultrasound Study

BACKGROUND

Plaque progression (PP) is critical between subclinical atherosclerosis and plaque rupture. Small dense low-density lipoprotein cholesterol (sdLDL-C) is considered as the most atherogenic lipoprotein. This study aims to investigate the relationship between sdLDL-C level and PP in patients with stable coronary artery disease.

METHODS

We conducted a retrospective analysis of 146 lesions in 86 patients by repeat intravascular ultrasound examinations from January 2020 to May 2023. PP was determined by increases in percent atheroma volume, defined as the atheroma volume in proportion to the volume occupied by the entire vascular wall, ≥5% during follow-up. Time-averaged values were calculated for all cardiometabolic parameters including sdLDL-C. Multivariate logistic regression analysis was performed to interrogate the association between time-averaged sdLDL-C and PP.

RESULTS

During a median follow-up of 12.6 months, PP was found in 65 lesions (44.5%), and mean changes in percent atheroma volume were 4.1%±10.2%. A positive correlation was observed between time-averaged sdLDL-C and changes in total atheroma volume (Pearson r=0.29, P=0.006), especially in diabetic patients (Pearson r=0.58, P<0.001). After multivariate adjustment, every 0.1-mmol/L increase in time-averaged sdLDL-C conferred a 1.2-fold increased risk of PP.

CONCLUSIONS

Our findings suggest that sdLDL-C is an independent risk factor of PP in patients with coronary artery disease. Intensive control of sdLDL-C along with other risk factors should be considered to mitigate PP and improve cardiovascular outcomes.

Unveiling the Prognostic Power of Coronary Physiological Progression

Coronary atherosclerosis, a progressive disease, has long been the focus of clinical investigations aimed at understanding its natural evolution and response to medical therapies. While traditional imaging modalities, such as intravascular ultrasound (IVUS) and coronary computed tomography angiography (CTA), have shed light on plaque characteristics and vulnerability to rupture leading to clinical events, the clinical implications of coronary physiological techniques associated with increased risk of clinical events remain underexplored. The incremental prognostic value of 3V-quantitative flow ratio (QFR) advocates for its integration into routine clinical evaluations as a non-invasive tool for risk stratification in coronary artery disease patients.

Prognostic Implications of Changes in Total Physiological Atherosclerotic Burden in Patients With Coronary Artery Disease-A Serial QFR Study

The association between coronary physiological progression and clinical outcomes has not been investigated. A total of 421 patients who underwent serial coronary angiography at least 6 months apart were included. Total physiological atherosclerotic burden was characterized by sum of quantitative flow ratio in 3 epicardial vessels (3V-QFR). The relationships of the 3V-QFR and its longitudinal change (△3V-QFR) with major adverse cardiovascular events (MACE) were explored. 3V-QFR values derived from follow-up angiograms were slightly lower compared with baseline (2.85 [2.77, 2.90] vs 2.86 [2.80, 2.90], P < .001). The median △3V-QFR value was -0.01 (-0.05, 0.02). The multivariable models demonstrated that follow-up 3V-QFR and △3V-QFR were independently associated with MACE (both P < .05). Patients with both low follow-up 3V-QFR (≤2.78) and low △3V-QFR (≤-0.05) presented 3 times higher risk of MACE than those without (hazard ratio: 2.953, 95% confidence interval 1.428-6.104, P = .003). Furthermore, adding patient-level 3V-QFR and △3V-QFR to clinical model significantly improved the predictability for MACE. In conclusion, total physiological atherosclerotic burden and its progression can provide incremental prognostic value over clinical characteristics, supporting the use of coronary physiology in the evaluation of disease progression and for the identification of vulnerable patients.

Non-invasive imaging innovation: FFR-CT combined with plaque characterization, safeguarding your cardiac health

Studies have shown that high-risk plaque features (including thin fibrous caps, lipid-rich cores, large plaque volumes, and intraplaque microcalcifications) are closely associated with the occurrence of acute coronary events. CT-derived fractional flow reserve (CT-FFR) is a non-invasive imaging post-processing technique that utilizes artificial intelligence to analyze data obtained from conventional coronary CT angiography (CCTA). FFR-CT technology offers the hemodynamic assessment of coronary lesions, aiding in the prediction of potential cardiovascular risks. This review summarizes the latest research progress on the complex relationship between FFR-CT, plaque characteristics, and hemodynamics, closely linking plaque volume, composition, and distribution with the clinical significance of coronary artery stenosis. It is hoped that these research findings will provide valuable guidance for clinicians, promoting the application of CT in the non-invasive detection of vulnerable plaques, thereby more effectively preventing and managing coronary artery disease. In the future, further optimization of FFR-CT technology and expansion of its clinical application are expected to significantly reduce the incidence and mortality of coronary artery disease, offering new hope for the prevention and treatment of cardiovascular diseases.

Coronary Atherosclerosis Progression Provides Incremental Prognostic Value and Optimizes Risk Reclassification by Computed Tomography Angiography

PURPOSE

This study investigated the prognostic value and risk reclassification ability of coronary atherosclerosis progression through serial coronary computed tomography angiography (CCTA).

MATERIALS AND METHODS

This study enrolled patients with suspected or confirmed coronary artery disease who underwent serial CCTA. Coronary atherosclerosis progression was represented by coronary artery calcium score (CACS) and segment stenosis score (SSS) progression. The baseline and follow-up CCTA characteristics and coronary atherosclerosis progression were compared. Furthermore, the incremental prognostic value and reclassification ability of three models (model 1, baseline risk factors; model 2, model 1 + SSS; and model 3, model 2 + SSS progression) for major adverse cardiovascular events (MACEs) were compared.

RESULTS

In total, 516 patients (aged 56.40 ± 9.56 y, 67.4% men) were enrolled. During a mean follow-up of 65.29 months, 114 MACE occurred. The MACE group exhibited higher CACS and SSS than the non-MACE group at baseline and follow-up CCTA ( P < 0.001), and demonstrated higher coronary atherosclerosis progression than the non-MACE group (ΔSSS: 2.63 ± 2.50 vs 1.06 ± 1.78, P < 0.001; ΔCACS: 115.15 ± 186.66 vs 89.91 ± 173.08, P = 0.019). SSS progression provided additional prognostic information (C-index = 0.757 vs 0.715, P < 0.001; integrated discrimination index = 0.066, P < 0.001) and improved the reclassification ability of risk (categorical-net reclassification index = 0.149, P = 0.015) compared with model 2.

CONCLUSIONS

Coronary atherosclerosis progression through CCTA significantly increased the prognostic value and risk stratification for MACE compared with baseline risk factor evaluation and CCTA only.

Prognostic value of serial coronary computed tomography angiography-derived perivascular fat-attenuation index and plaque volume in patients with suspected coronary artery disease

AIMS

To investigate the prognostic value of serial coronary computed tomography angiography (CCTA) derived plaque information, fractional flow reserve (CT-FFR), and perivascular fat-attenuation index (FAI) on major adverse cardiac events (MACE) in patients with suspected coronary artery disease.

MATERIALS AND METHODS

A total of 252 patients who underwent serial CCTA between January 2018 and December 2021 and were followed until June 2022. MACE were recorded. The analysis indexes included percent diameter stenosis (%DS), lesion length, plaque volume, CT-FFR, and FAI, with an emphasis on their changes between the baseline and follow-up CCTAs. Multivariate regression analysis were employed to identify independent risk factors for MACE.

RESULTS

After a median follow-up of 48-month, MACE occurred in 32 patients (12.7%). Patients with MACE displayed more severe stenosis, longer lesions, and larger plaque volumes in both baseline and follow-up CCTAs compared with no-MACE patients (all P<0.05). Patients with MACE displayed more severe stenosis, longer lesion, and larger plaque volume in both baseline and follow-up CCTAs compared with no-MACE patients. In addition, MACE patients also showed lower CT-FFR and higher △CT-FFR. Although FAI was significantly higher in MACE patients at baseline CCTA, FAI was notably increased in MACE patients, and decreased in the no-MACE patients (all P<0.05). Logistic regression analysis showed that ΔFAI, %DS, and plaque volume were independent predictors of MACE, with ΔFAI being the most significant (OR: 16.725, P<0.000). A multivariable model showed a significantly improved C-index of 0.903 (95% confidence interval: 0.836-0.970) for MACE prediction, when compared with single index alone.

CONCLUSIONS

Serial CCTA-derived ΔFAI, %DS, and plaque volume are crucial independent predictors of MACE in patients with suspected coronary artery disease, highlighting the importance of CCTA in patient risk stratification and prognostic assessment.

Coronary artery calcium (CAC) score for cardiovascular risk stratification in a Thai clinical cohort: A comparison of absolute scores and age-sex-specific percentiles

Purposes

Coronary artery calcium (CAC) score provides a quantification of atherosclerotic plaque within the coronary arteries. This study aimed to examine the prevalence and CAC score distribution and to evaluate the association of each CAC score classifications with major adverse cardiovascular events (MACE) in a Thai clinical cohort.

Methods

This study was a retrospective observational cohort. We included patients aged above 35 years who underwent CAC score testing. The absolute and age-sex specific percentile classifications were categorized as 0, 1 to 10, 11 to 100, 101 to 400, and >400 and 0, <75th, 75th - 90th, and >90th, respectively. The endpoint was MACE, including cardiovascular death, myocardial infarction, heart failure hospitalization, coronary artery revascularization procedure, and stroke. Multivariable Cox regression was used to estimate the hazard ratios. The discriminative performance between classifications were compared using Harrell's C-statistics. The agreement was assessed via Cohen's Kappa.

Results

This study included 440 patients, with approximately 70% of Thai patients exhibiting a CAC score. CAC score distributed higher in male than female and increased with age. Both CAC score classification demonstrated the acceptable predictive performance. However, fair agreement was observed between classifications (Cohen's kappa 0.51, 95%CI 0.42-0.59). Within the absolute classification, a higher CAC score was associated with increased hazard ratios for MACE across stratified age-sex-specific percentile levels. In contrast, the hazard ratios for MACE did not consistently rise with higher age-sex-specific percentile CAC score when stratified by absolute CAC score levels.

Conclusions

Both absolute and age-sex-specific percentile CAC score demonstrated acceptable performance in predicting MACE. However, the absolute CAC score classification may be more suitable for risk stratification within the Thai clinical cohort. Our findings offer supportive information that could inform future recommendations for CAC score testing criteria within national clinical practice guidelines.

Prognostic value of plaque volume combined with CT fractional flow reserve in patients with suspected coronary artery disease

AIM

To investigate the prognostic value of quantitative plaque volume on coronary computed tomography (CT) angiography (CTA) combined with CT fractional flow reserve (CT-FFR) for major adverse cardiac events (MACE) in suspected coronary artery disease (CAD) patients.

MATERIALS AND METHODS

Patients who underwent coronary CTA with clinically suspected CAD were enrolled retrospectively in this study. Patients' baseline, Framingham Risk Score (FRS), coronary CTA plaque assessment, and CT-FFR were analysed retrospectively. Study outcomes included rehospitalisation and MACE (ST-segment elevation myocardial infarction, unstable angina, or non-ST-segment elevation myocardial infarction, revascularisation, and cardiac death).

RESULTS

There were 251 patients in the study, with a follow-up period of 1-6.58 years. Mean age was 61.16 ± 10.45 years and 146 (58%) patients were male. Higher CT-adapted Leaman score and quantitative plaque volume were found in patients with FRS >0.2 regardless of categorical or continuous variables. Coronary scores, quantitative plaque parameters, and CT-FFR were associated with MACE and rehospitalisation in univariate analysis. In model 1, CT-FFR was associated with MACE in multivariate Cox analysis when adjusted for FRS and CT-adapted Leaman score. Quantitative plaque parameters including calcified plaque volume, fibro-fatty plaque volume, low-attenuation plaque volume, non-calcified plaque volume, and total plaque volume were significantly associated with MACE and improved overall prognostic performance in a model adjusted for CT-FFR.

CONCLUSION

Additional quantitative plaque volume and CT-FFR further improve the predictive incremental value based on risk factor scores for prognostic prediction in patients. Adding quantitative plaque volume combined with CT-FFR analysis to anatomical and clinical assessment will be further beneficial to predict patients' prognosis of MACE.

Machine learning based ischemia-specific stenosis prediction: A Chinese multicenter coronary CT angiography study

OBJECTIVES

To evaluate the performance of coronary computed tomography angiography (CCTA) derived characteristics including CT derived fractional flow reserve (CT-FFR) with FFR as a reference standard in identifying the lesion-specific ischemia by machine learning (ML) algorithms.

METHODS

The retrospective analysis enrolled 596 vessels in 462 patients (mean age, 61 years ± 11 [SD]; 71.4 % men) with suspected coronary artery disease who underwent CCTA and invasive FFR. The data were divided into training cohort, internal validation cohort, external validation cohorts 1 and 2 according to participating centers. All CCTA-derived parameters, which contained 10 qualitative and 33 quantitative plaque parameters, were collected to establish ML model. The Boruta and unsupervised clustering algorithm were implemented to select important and non-redundant parameters. Finally, the eight features with the highest mean importance were included for further ML model establishment and decision tree building. Five models were built to predict lesion-specific ischemia: stenosis degree from CCTA, CT-FFR, ΔCT-FFR, ML model and nested model.

RESULTS

Low-attenuation plaque, bend and lesion length were the main predictors of ischemia-specific lesions. Of 5 models, the ML model showed favorable discrimination for ischemia-specific lesions in the training and three validation sets (area under the curve [95 % confidence interval], 0.93 [0.90-0.96], 0.86 [0.79-0.94], 0.88 [0.83-0.94], and 0.90 [0.84-0.96], respectively). The nested model which combined the ML model and CT-FFR showed better diagnostic efficacy (AUC [95 %CI], 0.96 [0.94-0.99], 0.92 [0.86-0.99], 0.92 [0.86-0.99] and 0.94 [0.91-0.98], respectively; all P < 0.05), and net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were significantly higher than CT-FFR alone.

CONCLUSIONS

Comprehensive CCTA-derived multiparameter model could better predict the ischemia-specific lesions by ML algorithms compared to stenosis degree from CTA, CT-FFR and ΔCT-FFR. Decision tree can be used to predict myocardial ischemia effectively.

Development and Validation of CCTA-based Radiomics Signature for Predicting Coronary Plaques With Rapid Progression

BACKGROUND

Rapid plaque progression (RPP) is associated with a higher risk of acute coronary syndromes compared with gradual plaque progression. We aimed to develop and validate a coronary computed tomography angiography (CCTA)-based radiomics signature (RS) of plaques for predicting RPP.

METHODS

A total of 214 patients who underwent serial CCTA examinations from 2 tertiary hospitals (development group, 137 patients with 164 lesions; validation group, 77 patients with 101 lesions) were retrospectively enrolled. Conventional CCTA-defined morphological parameters (eg, high-risk plaque characteristics and plaque burden) and radiomics features of plaques were analyzed. RPP was defined as an annual progression of plaque burden ≥1.0% on lesion-level at follow-up CCTA. RS was built to predict RPP using XGBoost method.

RESULTS

RS significantly outperformed morphological parameters for predicting RPP in both the development group (area under the receiver operating characteristic curve, 0.82 versus 0.74; P=0.04) and validation group (area under the receiver operating characteristic curve, 0.81 versus 0.69; P=0.04). Multivariable analysis identified RS (odds ratio, 2.35 [95% CI, 1.32-4.46]; P=0.005) as an independent predictor of subsequent RPP in the validation group after adjustment of morphological confounders. Unlike unchanged RS in the non-RPP group, RS increased significantly in the RPP group at follow-up in the whole dataset (P<0.001).

CONCLUSIONS

The proposed CCTA-based RS had a better discriminative value to identify plaques at risk of rapid progression compared with conventional morphological plaque parameters. These data suggest the promising utility of radiomics for predicting RPP in a low-risk group on CCTA.

Role of Quantitative Plaque Analysis and Fractional Flow Reserve Derived From Coronary Computed Tomography Angiography to Assess Plaque Progression

PURPOSE

To explore the role of quantitative plaque analysis and fractional flow reserve (CT-FFR) derived from coronary computed angiography (CCTA) in evaluating plaque progression (PP).

METHODS

A total of 248 consecutive patients who underwent serial CCTA examinations were enrolled. All patients' images were analyzed quantitatively by plaque analysis software. The quantitative analysis indexes included diameter stenosis (%DS), plaque length, plaque volume (PV), calcified PV, noncalcified PV, minimum lumen area (MLA), and remodeling index (RI). PP is defined as PAV (percentage atheroma volume) change rate >1%. CT-FFR analysis was performed using the cFFR software.

RESULTS

A total of 76 patients (30.6%) and 172 patients (69.4%) were included in the PP group and non-PP group, respectively. Compared with the non-PP group, the PP group showed greater %DS, smaller MLA, larger PV and non-calcified PV, larger RI, and lower CT-FFR on baseline CCTA (all P <0.05). Logistic regression analysis showed that RI≥1.10 (odds ratio [OR]: 2.709, 95% CI: 1.447-5.072), and CT-FFR≤0.85 (OR: 5.079, 95% CI: 2.626-9.283) were independent predictors of PP. The model based on %DS, quantitative plaque features, and CT-FFR (area under the receiver-operating characteristics curve [AUC]=0.80, P <0.001) was significantly better than that based rarely on %DS (AUC=0.61, P =0.007) and that based on %DS and quantitative plaque characteristics (AUC=0.72, P <0.001).

CONCLUSIONS

Quantitative plaque analysis and CT-FFR are helpful to identify PP. RI and CT-FFR are important predictors of PP. Compared with the prediction model only depending on %DS, plaque quantitative markers and CT-FFR can further improve the predictive performance of PP.

Optimal Measurement Sites of Coronary-Computed Tomography Angiography-derived Fractional Flow Reserve: The Insight From China CT-FFR Study

OBJECTIVES

To investigate the optimal measurement site of coronary-computed tomography angiography-derived fractional flow reserve (FFR CT ) for the assessment of coronary artery disease (CAD) in the whole clinical routine practice.

MATERIALS AND METHODS

This retrospective multicenter study included 396 CAD patients who underwent coronary-computed tomography angiography, FFR CT , and invasive FFR. FFR CT was measured at 1 cm (FFR CT -1 cm), 2 cm (FFR CT -2 cm), 3 cm (FFR CT -3 cm), and 4 cm (FFR CT -4 cm) distal to coronary stenosis, respectively. FFR CT and invasive FFR ≤0.80 were defined as lesion-specific ischemia. The diagnostic performance of FFR CT to detect ischemia was obtained using invasive FFR as the reference standard. Reduced invasive coronary angiography rate and revascularization efficiency were calculated. After a median follow-up of 35 months in 267 patients for major adverse cardiovascular events (MACE), Cox hazard proportional models were performed with FFR CT values at each measurement site.

RESULTS

For discriminating lesion-specific ischemia, the areas under the curve of FFR CT -1 cm (0.91) as well as FFR CT -2 cm (0.91) were higher than those of FFR CT -3 cm (0.89) and FFR CT -4 cm (0.88), respectively (all P <0.05). The higher reduced invasive coronary angiography rate (81.6%) was found at FFR CT -1 cm than FFR CT -2 cm (81.6% vs. 62.6%, P <0.05). Revascularization efficiency did not differ between FFR CT -1 cm and FFR CT -2 cm (80.8% vs. 65.5%, P =0.019). In 12.4% (33/267) MACE occurred and only values of FFR CT -2 cm were independently predictive of MACE (hazard ratio: 0.957 [95% CI: 0.925-0.989]; P =0.010).

CONCLUSIONS

This study indicates FFR CT -2 cm is the optimal measurement site with superior diagnostic performance and independent prognostic role.

Coronary Computed Tomography Angiography-derived Fractional Flow Reserve: An Expert Consensus Document of Chinese Society of Radiology

Invasive fractional flow reserve (FFR) measured by a pressure wire is a reference standard for evaluating functional stenosis in coronary artery disease. Coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) uses advanced computational analysis methods to noninvasively obtain FFR results from a single conventional coronary computed tomography angiography data to evaluate the hemodynamic significance of coronary artery disease. More and more evidence has found good correlation between the results of noninvasive CT-FFR and invasive FFR. CT-FFR has proven its potential in optimizing patient management, improving risk stratification and prognosis, and reducing total health care costs. However, there is still a lack of standardized interpretation of CT-FFR technology in real-world clinical settings. This expert consensus introduces the principle, workflow, and interpretation of CT-FFR; summarizes the state-of-the-art application of CT-FFR; and provides suggestions and recommendations for the application of CT-FFR with the aim of promoting the standardized application of CT-FFR in clinical practice.

Influence of diabetes mellitus on the diagnostic performance of machine learning-based coronary CT angiography-derived fractional flow reserve: a multicenter study

OBJECTIVES

To examine the diagnostic accuracy of machine learning-based coronary CT angiography-derived fractional flow reserve (FFR_CT) in diabetes mellitus (DM) patients.

METHODS

In total, 484 patients with suspected or known coronary artery disease from 11 Chinese medical centers were retrospectively analyzed. All patients underwent CCTA, FFR_CT, and invasive FFR. The patients were further grouped into mild (25~49 %), moderate (50~69 %), and severe (≥ 70 %) according to CCTA stenosis degree and Agatston score < 400 and Agatston score ≥ 400 groups according to coronary artery calcium severity. Propensity score matching (PSM) was used to match DM (n  = 112) and non-DM (n  = 214) groups. Sensitivity, specificity, accuracy, and area under the curve (AUC) with 95 % confidence interval (CI) were calculated and compared.

RESULTS

Sensitivity, specificity, accuracy, and AUC of FFR_CT were 0.79, 0.96, 0.87, and 0.91 in DM patients and 0.82, 0.93, 0.89, and 0.89 in non-DM patients without significant difference (all p > 0.05) on a per-patient level. The accuracies of FFR_CT had no significant difference among different coronary stenosis subgroups and between two coronary calcium subgroups (all p > 0.05) in the DM and non-DM groups. After PSM grouping, the accuracies of FFR_CT were 0.88 in the DM group and 0.87 in the non-DM group without a statistical difference (p > 0.05).

CONCLUSIONS

DM has no negative impact on the diagnostic accuracy of machine learning-based FFR_CT.

KEY POINTS

• ML-based FFR_CT has a high discriminative accuracy of hemodynamic ischemia, which is not affected by DM. • FFR_CT was superior to the CCTA alone for the detection of ischemia relevance of coronary artery stenosis in both DM and non-DM patients. • Coronary calcification had no significant effect on the diagnostic accuracy of FFR_CT to detect ischemia in DM patients.