Synergistic value of fractional flow reserve and low‑density non‑calcified plaque based on coronary computed tomography angiography for the identification of lesion‑specific ischemia
- Authors:
- Published online on: September 29, 2022 https://doi.org/10.3892/etm.2022.11637
- Article Number: 701
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Copyright: © Tang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
A non-reversible imbalance in myocardial blood supply and demand results in myocardial ischemia in patients with coronary artery disease (CAD), which can further lead to heart failure and myocardial infarction. CAD is currently a major cause of death worldwide, with 31% of all deaths resulting from it (1). During invasive coronary radiography, fractional flow reserve (FFR) is measured, and this is used as a gold standard for determining myocardial ischemia caused by coronary stenosis. However, both its invasive nature and the risk of complications serve to limit its clinical applicability (2). Coronary computed tomography (CT) angiography (CCTA) is recognized as the most accurate means for excluding CAD (3). Nevertheless, a purely anatomical assessment of hemodynamics is hardly able to provide sufficient guidance for clinical treatment.
Currently, the application of non-invasive techniques to assess myocardial ischemia due to abnormal coronary hemodynamics reduces the occurrence of adverse cardiac events. FFR obtained by CCTA (FFRCT), based on deep machine learning algorithms, has been shown to be an effective assessment method for detecting ischemia, demonstrating a high diagnostic performance compared with invasive FFR (4-6). Additionally, when CCTA-derived plaque characteristics and composition have been obtained using a semi-automated program, this has been shown to improve the ability of CCTA to predict hemodynamic abnormalities by obtaining more information about the lesion (7,8). Previous studies have also suggested that low-density non-calcified plaque (LD-NCP) acts as a substitute for the necrotic lipid core, and the larger its volume, the higher the possibility of ischemia (9,10). However, the potential of quantitative CCTA-derived plaque combined with FFRCT for lesion-specific ischemia detection needs to be explored further. To meet this end, the present study aimed to evaluate the predictive performance of deep learning-based FFRCT in combination with CCTA-derived plaque characteristics for identifying lesion-specific ischemia according to the gold standard of invasive FFR.
Materials and methods
Study population
The present study was a retrospective study conducted at a single center. A total of 144 patients with coronary heart disease admitted to The First Affiliated Hospital of Hebei North University (Zhangjiakou, China) between February 2019 and March 2022 were included in the current study. Invasive coronary angiography (ICA) and CCTA were both performed on all patients, and the interval between the two examinations was ≤30 days. The inclusion criteria were as follows: i) Complete clinical data and CCTA images were available for the patient; and ii) these were of sufficient quality for FFRCT and plaque analysis. The exclusion criteria were as follows: i) Poor coronary CTA image quality; ii) patients who had previously undergone revascularization procedures (for example, cardiac bypass graft and/or percutaneous coronary intervention); iii) the patient had contraindications to adenosine, nitrates or β-blockers; and iv) the patient had been diagnosed with a combination of severe cardiovascular disease (for example, severe arrhythmias and/or severe heart failure). The current study was approved by the Ethics Committee of The First Affiliated Hospital of Hebei North University (Zhangjiakou, China; approval no. K2020237), and written informed consent was obtained from all of the participants.
ICA and FFR techniques
ICA and FFR were performed in accordance with standard practices (11). The FFR pressure-wire was placed at least 20 mm distal to the ≥2 mm vessel stenosis after ICA. Adenosine (140-180 µg/kg/min; Pfizer, Inc.) was used to induce hyperemia, and both the distal coronary pressure (Pd) and the aortic pressure (Pa) were measured simultaneously at baseline and during maximal hyperemia. Based on a beat-to-beat calculation, the FFR was determined as the mean Pd divided by the mean Pa at maximal hyperemia. Lesion-specific ischemia was defined upon calculating a FFR value of ≤0.80.
CCTA acquisition
CCTA was performed using an Aquilion ONE ViSION CT scanner (320-MDCT; Canon Medical Systems Corporation). Nitroglycerin (0.8 mg; Xinyi Pharmaceutical Co., Ltd.) was given sublingually to all patients prior to the CT scan, and patients whose heart rate pre-scan was >60 beats/min were administered metoprolol (AstraZeneca Pharmaceutical Co., Ltd.) orally (20-40 mg), with the heart rate held at ≤60 beats/min. The isotonic contrast agent, iodixanol (320 mg iodine/ml; Jiangsu Hengrui Medicine Co., Ltd.), was injected at a rate of 5.5 ml/sec using a double-barrel hyperbaric syringe, followed immediately by injection of 30 ml of 0.9% sodium chloride solution at the same rate. Regarding the scan parameters, the tube voltage was set at 100 kV, and the tube current was automatically modulated. Monitoring was set in the descending aorta at the level of 1 cm below the tracheal bifurcation. The scan was automatically triggered using SUREStart™ software (version 1.0; Canon Medical Systems Corporation) when 200 Hounsfield units (HU) were reached, ranging from below the tracheal ridge to the diaphragm surface, and the clearest coronary image was selected for reconstruction. The CCTA images were analyzed and processed by two physicians with professional diagnostic imaging qualifications in the Department of Medical Imaging, The First Affiliated Hospital of Hebei North University. The two physicians were blinded to the clinical information and CCTA results of the patients, and differences in the assessment results were re-evaluated by a third experienced physician, before subsequently being discussed to obtain the final results. The branches of the stenotic coronary arteries [i.e., the left anterior descending artery (LAD), the left circumflex artery (LCX) and the right coronary artery (RCA)] were observed.
Coronary plaque analysis
The scan-specific algorithm, Vitrea FX version 4.0 (Vital Images; Canon Medical Systems Corporation), was used to assess the plaque characteristics for each coronary lesion segment ≥2 mm. By using this automated method, CCTA was able to rapidly measure plaque features (12). Vitrea FX utilizes multiplanar CCTA images to identify the proximal and distal center points of each lesion, and then the vessel borders and plaque are automatedly segmented. For each lesion, plaque volumes were measured for the following subtypes of plaque: Total plaque, fibrous plaque, calcified plaque (CP), NCP and LD-NCP (the latter was defined as attenuation <30 HU). NCP was further classified by plaque HU into two components: Necrotic core (-30 to 30 HU) and fibrous plaque (131 to 350 HU) (13,14). Semi-quantitative measurements were conducted at the region of maximal stenosis degree to determine the diameter of the minimal lumen. To calculate the remodeling index (RI), the cross-sectional vessel area at the site of greatest stenosis was divided by the mean cross-sectional vessel area at the proximal point of the reference. The plaque burden was calculated according to the plaque volume divided by the vessel volume of the analyzed coronary lesion (i.e., plaque volume/vessel volume x100). The presence of the napkin-ring sign, defined as a low-density plaque core surrounded by high-density areas (15), was investigated. Spotty calcifications were defined as calcified plaque of length <3 mm within a lesion (16). The volumes and characteristics of coronary plaques were assessed for each patient and for each vessel.
Deep learning-based FFRCT
FFRCT measurements were conducted using deep learning-based DEEPVESSEL® FFR software (version 1.0; Beijing Keya Medical Technology Co., Ltd.), and the CCTA images of the patients were uploaded to its image-computing platform using Digital Imaging and Communications in Medicine (DICOM), the standard for the communication and management of medical imaging information and associated data. The system automatically calculated the FFRCT value of each coronary artery via a hydrodynamic model (17), which was presented as a color coronary tree, with different colors indicating different FFRCT values. FFRCT values ≤0.80 were considered to be indicative of lesion-specific ischemia (18).
Statistical analysis
The continuous variables are expressed as the mean ± standard deviation (SD) or median (interquartile range), whereas categorical variables are expressed as numbers (percentages). As required, unpaired Student's t-test, Pearson's χ2 test or Mann-Whitney U-test were used for data comparisons. To determine the predictors of ischemia, logistic regression analysis was conducted (for FFRCT values ≤0.80). Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were used to evaluate the predictive values of FFRCT, LD-NCP, and the combination of FFRCT and LD-NCP for lesion-specific ischemia, and pairwise comparisons of AUC were made using the DeLong test (19). P<0.05 with a 95% confidence interval (CI) was considered to indicate a statistically significant difference. All of the statistical analyses were conducted using SPSS software, version 26.0 (IBM Corp.) and MedCalc software, version 20 (MedCalc Software bvba).
Results
Patient characteristics
In the study population, 144 patients with 243 vessels were investigated by FFR (Fig. 1). The mean age (± SD) of the patients was 62.0±5.4 years, the mean body mass index (± SD) was 25.0±1.5 kg/m2 and 96/144 (66.7%) of the participants were male. Regarding risk factors for coronary heart disease, 67/144 (46.5%) of the patients had hypertension, 43/144 (29.9%) had diabetes, 76/144 (52.8%) had hyperlipidemia and 47/144 (32.6%) had a history of smoking. All patients underwent ICA within 30 days of CCTA, with a mean interval (± SD) of 19.1±5.8 days. Of the 243 vessels, 136/243 (56.0%) were the LAD, 57/243 (23.5%) were the RCA and 50/243 (20.6%) were the LCX. The basic characteristics of the included study population are shown in Table I.
Association of plaque characteristics and lesion-specific ischemia
Associations between plaque characteristics and lesion-specific ischemia were evaluated. The plaque characteristics RI, CP, fibrous plaque volume, plaque burden, napkin-ring sign, spotty calcifications and stenosis >50% showed no significant differences when comparing between lesion-specific ischemia and non-significant lesions (P>0.05). By contrast, NCP, LD-NCP, total plaque volume and plaque length were significantly different in lesion-specific ischemia compared with non-significant lesions (P<0.05). Table II summarizes the different quantitative and qualitative plaque characteristics and their association with FFRCT ≤0.80. Univariate logistic regression analysis demonstrated that NCP [odds ratio (OR), 1.158; 95% CI, 1.103-1.215; P<0.0001], LD-NCP (OR, 1.128; 95% CI, 1.094-1.164; P<0.0001) and plaque length (OR, 1.147; 95% CI, 1.043-1.261; P=0.005) were significantly associated with lesion-specific ischemia. Finally, multivariate logistic regression revealed that LD-NCP (OR, 1.18; 95% CI, 1.105-1.256; P<0.001) was an independent predictor of lesion-specific ischemia (Table III).
 | Table IIIUnivariate and multivariate logistic regression analysis of coronary CT angiography-derived plaque markers and FFRCT. |
Association of FFRCT and lesion-specific ischemia
FFRCT was found to be significantly associated with the presence of ischemia (P<0.0001) (Table II). According to the univariate regression analysis, FFRCT was a significant predictor of lesion-specific ischemia (OR, 23.20; 95% CI, 11.171-48.084; P<0.0001), and was a strong independent predictor of ischemia (OR, 23.28; 95% CI, 3.505-54.561; P<0.0001) (Table III).
Combined assessment of LD-NCP and FFRCT for identifying ischemia
The AUC values for the identification of FFRCT ≤0.80 were 0.79 (95% CI, 0.732-0.838) for LD-NCP, 0.88 (95% CI, 0.835-0.920) for FFRCT and 0.92 (95% CI, 0.877-0.950) for LD-NCP + FFRCT. FFRCT showed a better predictive performance relative to LD-NCP for lesion-specific ischemia (0.88 vs. 0.79; P=0.01). The addition of FFRCT to LD-NCP further enhanced the predictive performance, albeit with incremental discriminatory power, compared with LD-NCP alone (0.92 vs. 0.79; P<0.0001) or FFRCT alone (0.92 vs. 0.88; P=0.0057). In Fig. 2 and Table IV, analyses of the ROC curves for optimal thresholds for identifying lesion-specific ischemia are shown, as well as the results of the sensitivity, specificity, positive predictive value, negative predictive value and cut-off value calculations.
| Table IVDiagnostic performance of coronary CT angiography-derived plaque markers and FFRCT for the identification of lesion-specific ischemia. |
Discussion
The results of the present study have demonstrated that CCTA-based FFRCT and plaque characteristics, especially LD-NCP, are predictors of lesion-specific ischemia. Importantly, FFRCT and LD-NCP have been shown to be significant predictors of specific ischemia, and in combination, they synergistically increase the predictive value for ischemia compared with FFRCT or LD-NCP alone.
Several studies have found a correlation between CCTA-derived plaque characteristics and ischemia, as well as significant differences between ischemia and non-significant lesions based on multiple quantitative and qualitative plaque characteristics (20-22). In the present study, it was found that plaque length, NCP volume and LD-NCP volume were not only significantly different when comparing between ischemia-causing lesions and non-significant lesions, but they were also useful in terms of predicting lesion-specific ischemia. These findings are similar to those reported by Diaz-Zamudio et al (23) and Iguchi et al (24), showing the predictive value of plaque length, NCP volume and LD-NCP volume. However, contrary to the findings of the present study, Gaur et al (25) reported a significant association between CCTA-derived RI and the presence of ischemia. By contrast, a different study indicated no significant correlation between plaque length or RI derived from CCTA and ischemia (26). It is likely that the determinants of study outcomes will differ significantly, which could explain the differences in results seen among studies.
LD-NCP is considered a surrogate for necrotic core plaques, and it has been shown to be useful in assessing the hemodynamic significance of coronary arteries (27). Notably, the results of the present study revealed that LD-NCP volume was an independent predictor for lesion-specific ischemia; this is a similar result to that found in a previous total-vessel study, which revealed that a higher probability of ischemia was associated with a higher LD-NCP volume (28). A retrospective study also suggested that LD-NCP volume predicts acute coronary syndromes, both on a per-patient and a per-vessel basis (29). Notably, the volume of LD-NCP was shown to be associated with the endothelial dysfunction caused by local inflammation and oxidative stress, and an increase in the LD-NCP volume led to reduced bioavailability of the vasodilator, nitric oxide, which made it difficult for the blood vessels to dilate under conditions of stress, thereby leading to ischemia (9). This mechanism may account for the ability of LD-NCP to act as a significant predictor of ischemia. However, in the presence of high-grade stenosis, plaque analysis, such as that of NCP and LD-NCP, may be less useful in terms of diagnosing ischemia (28).
Over the course of the last few decades, researchers have pursued an ideal non-invasive imaging diagnostic for ischemia. Numerous studies have shown a higher diagnostic accuracy of FFRCT for lesion-specific ischemia compared with invasive FFR (30-33). In addition, FFRCT based on deep-learning algorithms has been used to evaluate the hemodynamics of coronary arteries (34). A combined multicenter meta-analysis study revealed a high predictive value of FFRCT for lesion-specific ischemia, with an AUC value of 0.86(35). A similar AUC value was derived in the present study (AUC, 0.88), and deep-learning FFRCT showed excellent predictive performance (OR, 23.19; P<0.0001) in terms of identifying ischemia. Additionally, FFRCT provided superior discriminatory performance over LD-NCP (AUC, 0.88 vs. 0.79; P=0.006), and the addition of FFRCT to LD-NCP demonstrated an incremental increase in predictive value (AUC, 0.92 vs. 0.79; P<0.0001), which is consistent with the findings of a previous study by von Knebel Doeberitz et al (36). However, in contrast with the present study results, a previous study found that the combination of FFR and LD-NCP was unable to increase the predictive value of FFR alone for lesion-specific ischemia (25). However, this previous study added stenosis >50% as a predictive index, and the presence of a difference in FFRCT when accompanied by markedly stenotic coronary arteries may explain why the addition of FFRCT had no incremental value for ischemia.
The present study had certain limitations. Firstly, the design protocol for retrospective studies and the relatively small sample size of the included study cases may have led to the existence of selection bias. Therefore, more prospective, multicenter studies are needed in the future to validate the findings. Secondly, FFRCT values may vary, depending on factors such as fluid dynamics models, blood viscosity and individual differences (37), which require continuous optimization of image quality and algorithms. Combining FFRCT and plaque features based on deep machine learning models may improve the identification of ischemia. Thirdly, the analysis of plaque characteristics may be limited by the resolution of CT, and the volume measurement of LD-NCP will also be affected to a certain extent (38). Therefore, in addition to improving the CT resolution, it is necessary to verify different CT scanners and different tube voltages prior to their clinical application. Fourthly, patients with severe cardiovascular disease or previous revascularization were excluded from the present study, and the predictive performance of FFRCT and LD-NCP for ischemia in this group of patients requires further study. Lastly, since the automated software only calculated the total plaque length and burden, but could not measure the length of different types of plaques or the volume of blood vessels where they were located (39), the algorithm needs to be improved in the future to explore the predictive value of plaque length, volume and burden for lesion-specific ischemia.
In conclusion, CCTA-derived plaque characteristics and FFRCT have been demonstrated to have predictive value in terms of identifying lesion-specific ischemia. Furthermore, the addition of FFRCT to LD-NCP showed incremental discriminatory power for ischemia compared with FFRCT or LD-NCP alone.
Acknowledgements
Not applicable.
Funding
Funding: The present study was supported by Zhangjiakou Key Research and Development Program Projects (grant no. 2021030D).
Availability of data and materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Authors' contributions
SC and LT conceived the study and wrote the manuscript. LT, FY and TD performed the experiments. LT and FL carried out the data collection and data analysis. SC and FY assessed the quality of the studies. LT, SC and FL confirm the authenticity of all the raw data. SC, LT, TD, FL and FY reviewed the results. All authors have read and approved the final manuscript.
Ethics approval and consent to participate
The Ethics Review Board of The First Affiliated Hospital, Hebei North University (Zhangjiakou, China) examined and approved the study protocol. All patients included in the study provided written informed consent.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
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