Prognostic value of deep learning based RCA PCAT and plaque volume beyond CT-FFR in patients with stent implantation

Prognostic value of deep learning based RCA PCAT and plaque volume beyond CT-FFR in patients with stent implantation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Prognostic value of deep learning based RCA PCAT and plaque volume beyond CT-FFR in patients with stent implantation Zengfa Huang, Ruiyao Tang, Xinyu Du, Yi Ding, ZhiWen Yang, Beibei Cao, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4343032/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The study aims to investigate the prognostic value of deep learning based pericoronary adipose tissue attenuation computed tomography (PCAT) and plaque volume beyond coronary computed tomography angiography (CTA) -derived fractional flow reserve (CT-FFR) in patients with percutaneous coronary intervention (PCI). A total of 183 patients with PCI who underwent coronary CTA were included in this retrospectively study. Imaging assessment included PCAT, plaque volume and CT-FFR which were performed using an artificial intelligence (AI) assisted workstation. Kaplan-Meier and multivariate Cox regression were used to estimate major adverse cardiovascular events (MACE) including non-fatal myocardial infraction (MI), stroke and mortality. In total, 22 (12%) MACE occurred during the median follow-up of 38.0 months (interquartile range 34.6–54.6 months). Kaplan-Meier survival curves indicated that right coronary artery (RCA) PCAT ( p = 0.007) and plaque volume ( p = 0.008) were significantly associated with the increasing of MACE. Multivariable Cox regression analysis showed that RCA PCAT [hazard ratios ( HR ): 2.94, 95%CI : 1.15–7.50, p = 0.025] and plaque volume ( HR : 3.91, 95%CI : 1.20-12.75, p = 0.024) were independent predictors of MACE after adjusting for clinical risk factors. However, CT-FFR was not independently associated with MACE in multivariable Cox regression ( p = 0.271). Deep learning based RCA PCAT and plaque volume derived from coronary CTA was found to be more strongly associated with MACE than CT-FFR in patients with PCI. Health sciences/Cardiology Health sciences/Diseases/Cardiovascular diseases coronary computed tomography angiography pericoronary adipose tissue attenuation computed tomography percutaneous coronary intervention prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Percutaneous coronary intervention (PCI) is a recommended as the first-line treatment for patients with high-complexity coronary artery disease (CAD) in ESC and AHA guidelines ( 1 , 2 ). More than 500,000 PCI procedures are performed annually worldwide for stable CAD ( 3 ). According to the statistical data from National Center For Cardiovascular Disease (NCCD), The total number of registered cases of PCI therapy in mainland China was 1,164,117, which increase 20.18% than 2020 ( 4 ). However, the incidence of MACE including myocardial infarction, revascularizations and all-cause mortality after PCI remains up to 50–15% ( 5 ). In addition, previous study has reported that the recurrent rate of chest pain is up to 50% ( 6 ). Previous studies have used clinical models and SYNTAX II score to evaluate the prognosis in patients with PCI ( 7 , 8 ). Unfortunately, so far, limited studies have reported effective method contains coronary computed tomography angiography (CTA) features to assess the prognostic value in those patients ( 9 ). Coronary CTA was regarded as the first-line examination for evaluation of patients with PCI. Recently, coronary CTA -derived fractional flow reserve (CT-FFR), plaque volume and perivascular fat attenuation index has been introduced as a novel imaging biomarker in patients with CAD. Previous study has indicated that post-PCI invasive FFR values revealed closely association with repeat PCI and poor prognosis during follow-up ( 10 ). Furthermore, a recent study with small sample has showed that CT-FFR was highly correlated with FFR and performed good prognostic value in predicting MACE in patients with PCI ( 11 ). Another novel imaging biomarker measured in coronary CTA is perivascular fat attenuation index (FAI) which reflects the coronary vascular inflammation. The increased FAI value was reported to be associated with increased risk of MACE ( 12 , 13 ). Recent coronary CTA studies have demonstrated that plaque volume provides independent further MACE events prediction during follow-up ( 14 , 15 ). However, little is known about the difference between CT-FFR, FAI and plaque volume for predicting MACE in patients with PCI. Thus, the present study aimed to investigate the prognostic value of deep learning based CT-FFR, pericoronary adipose tissue attenuation computed tomography (PCAT) and plaque volume in patients with PCI. Materials and Methods Patient Selection and Study Design The retrospective and observational study complied with the Declaration of Helsinki. The study protocol was approved by the institutional ethics committee and written informed consent was waived by institutional ethics committee because of its retrospective observational nature of the study. Between November 2018 and December 2020, consecutive patients who underwent coronary CTA for evaluating percutaneous coronary intervention (PCI) were retrospectively enrolled from our two hospitals (Nanjing Road and Houhu districts). The inclusion criterion was age above 18 years. Exclusion criteria were history of myocardial infraction, coronary revascularization (coronary artery bypass grafting, CABG), low quality coronary CTA images, missing coronary CTA images or reports, or reports without stenosis information, missing CT-FFR values, loss of follow-up. Coronary CTA Acquisition Protocol All coronary CTA examinations were performed with prospectively or retrospectively ECG-triggered on dual-scanner CT scanner (Somatom Definition, Siemens Medical Solutions, Forchheim, Germany) or ICT scanner (Philips Brilliance 64, Philips Medical Systems, Best, the Netherlands). Detailed coronary CTA protocol and parameters were presented in previous reports ( 16 , 17 ). CT-FFR, PCAT CT Attenuation, Plaque Volume Measurement and Analysis CT-FFR values were calculated through commercial software based on a deep learning algorithm which has been described in our previous reports ( 16 ). Plaque volume was performed on an AI ML platform (CoronaryDoc Premium, Shukun Technology Co, Beijing, China) and the total plaque volume was the sum of the separate plaque volumes in each coronary artery segment. PCAT measurements and analysis was performed using a dedicated workstation (Perivascular Fat Analysis Tool, Shukun Technology Co, Beijing, China). Follow up The local institutional review boards approved the follow-up procedures of the study. The primary endpoint was defined as MACE, including all-cause mortality, MI and stroke. MACE status was assessed through a query of the local Community Health Service Centers. If the MACE occurred outside of the city, MACE was ascertained by reviewing the medical records or contacting with the patients’ family by telephone to confirm the outcome. Two experts in the centers for disease control and prevention (CDC) performed the process of the follow-up with blinding to other information of the patient. The deadline date of follow-up was September 30, 2022. Statistical Analysis Continuous variables are shown as mean (± SD) and categorical variables are expressed as frequencies and percentages. Student’s t-test was used to compare continuous variables between groups and chi-square test was used for the comparison of categorical variables. Cumulative event-free survival was estimated by the Kaplan-Meier method and the log-rank test was used for comparison between groups. Hazard ratio (HR) with 95% confidence intervals (95% CI) was calculated by univariate and multivariate Cox proportional hazard analysis. Multivariate Cox analysis was adjusted by gender, age, smoking status, alcohol consumption, hypertension, diabetes and total cholesterol levels. Maximally selected rank statistics method was used to evaluate the prognostic threshold of PCAT and plaque volume. P < 0.05 was considered to be statistically significant. All statistical analyses were carried out using R statistical package (version 4.3, R foundation for Statistical Computing, Vienna, Austria), SPSS (version 18, SPSS, Inc., Chicago, IL, USA) and MedCalc Statistical Software (version16.8.4 Ostend, Belgium). Results Patient Basic Characteristics and Coronary CTA Features A total of 183 patients with PCI who underwent coronary CTA were included in the final analyses and 22 patients occurred MACE during a median follow-up period of 38.0 months (IQR 34.6–54.6 months). Overall, the average age of 183 patients was 68.1 ± 10.3 years with 111 (60.7%) male patients. Patients occurred MACE showed significantly higher percentage rate in plaque volume > 20 mm 3 (14 (63.6%) vs. 56 (34.8%), p = 0.009) and PCAT_RCA < -64 HU (4 (18.2%) vs. 7 (4.3%), p = 0.010) than those in patients without MACE. However, CT-FFR, PCAT _LAD, PCAT _LCX and PCAT volume showed no significant differences between patients with and without MACE. The patients’ characteristics and coronary CTA features were displayed in Table 1 . Table 1 Baseline characteristic and Coronary CTA features stratified by the occurrence of MACE Variables Total (N = 183) Without MACE (N = 161) With MACE (N = 22) P Value Age (years) 68.1 ± 10.3 67.6 ± 10.1 71.2 ± 10.9 0.128 Gender (Male, n, %) 111 (60.7) 97 (60.2) 14 (63.6) 0.760 Smoke 51 (27.9) 44 (27.3) 7 (31.8) 0.660 Drink 27 (14.8) 24 (14.9) 3 (13.6) 0.875 Hypertension 138 (75.4) 118 (73.3) 20 (90.9) 0.072 Diabetes 78 (42.6) 66 (41.0) 12 (54.5) 0.228 Coronary CTA features CT-FFR ≤ 0.80 101 (55.2) 89 (55.3) 12 (54.5) 0.948 Plaque Volume > 20 mm 3 70 (38.3) 56 (34.8) 14 (63.6) 0.009 FAI_LAD <-77 HU 68 (37.2) 57 (35.4) 11 (50.0) 0.184 FAI_LCX <-67 HU 43 (23.5) 35 (21.7) 8 (36.4) 0.129 FAI_RCA 1006 mm 3 172 (94.0) 150 (93.2) 22 (100) 0.206 Volume_ PCAT_LCX > 1754 mm 3 34 (18.6) 29 (18.0) 5 (22.7) 0.594 Volume_ PCAT_RCA > 1663 mm 3 159 (86.9) 140 (87.0) 19 (86.4) 0.938 PCAT = pericoronary adipose tissue attenuation; CT-FFR = coronary computed-derived fractional flow reserve; LAD = left anterior descending; LCX = left circumflex branch; RCA = right coronary artery; HR = Hazard ratio; CI = confidence intervals; HU = Hounsfield unit; MACE = major adverse cardiovascular events. CT-FFR, PCAT CT Attenuation and Plaque Volume with MACE The prognostic threshold of PCAT CT attenuation was > -77 HU for LAD, > -67 HU for LCX, > -64 HU for RCA, respectively, according to Youden index analysis. From Youden index analysis, > 20 mm 3 was the prognostic threshold of the plaque volume. The Kaplan-Meier survival curves indicated that plaque volume and PCAT_RCA CT attenuation were associated with increasing rate of MACE (log-rank p = 0.008 for plaque volume and p = 0.007 for PCAT_RCA, Fig. 1 ). However, PCAT_LAD and PCAT_LCX CT attenuation (Fig. 2 ), as well as CT-FFR showed no association with the increasing rate of MACE (Fig. 3 ). Univariable and Multivariable Cox Regression Analysis Univariable Cox regression analysis showed that PCAT_RCA CT attenuation ( p = 0.012) and plaque volume ( p = 0.013) were associated with MACE (Table 2 ). In multivariable Cox regression analysis, after adjusting clinical risk factors (age, gender, smoke, drink, hypertension, diabetes), PCAT_RCA CT attenuation was associated with MACE in model 1 (HR, 4.03; 95% CI: 1.16–13.98, p = 0.028) and plaque volume was associated with MACE in model 2 (HR, 2.63; 95% CI: 1.08–6.44, p = 0.034). After adjusting clinical risk factors and all coronary CTA features, PCAT_RCA CT attenuation (HR, 7.05; 95% CI: 1.44–34.63, p = 0.016) and plaque volume (HR, 3.84; 95% CI: 1.44–10.27, p = 0.007) remained independent significant predictors of MACE in model 4 (Table 3 ). The area under ROC curve (AUC) for predicting MACE was 0.678 (95% CI: 0.566–0.791), 0.697 (95% CI: 0.590–0.804), 0.727 (95% CI: 0.621–0.833), 0.745 (95% CI: 0.643–0.846) for clinical model (age, gender, smoke, drink, hypertension, diabetes), clinical model + PCAT_RCA CT attenuation, clinical model + plaque volume and clinical model + PCAT_RCA CT attenuation + plaque volume, respectively (Fig. 4 ). Table 2 Univariable Cox regression analysis for predicting MACE Variables Univariate HR (95% CI) P Value Age 1.03 (0.99–1.08) 0.120 Gender (male) 1.15 (0.48–2.75) 0.750 Smoke 1.24 (0.50–3.03) 0.642 Drink 0.90 (0.27–3.03) 0.861 Hypertension 3.41 (0.90–14.60) 0.098 Diabetes 1.64 (0.71–3.79) 0.250 CT-FFR ≤ 0.80 0.96 (0.41–2.21) 0.916 Plaque Volume > 20 mm 3 3.06 (1.28–7.29) 0.012 PCAT_LAD <-77 HU 1.78 (0.77–4.11) 0.176 PCAT_LCX <-67 HU 1.95 (0.82–4.65) 0.134 PCAT_RCA 1006 mm 3 22.25 (0.01–40781) 0.418 Volume_ PCAT_LCX > 1754 mm 3 1.36 (0.50–3.69) 0.544 Volume_ PCAT_RCA > 1663 mm 3 1.01 (0.30–3.43) 0.982 PCAT = pericoronary adipose tissue attenuation; CT-FFR = coronary computed-derived fractional flow reserve; LAD = left anterior descending; LCX = left circumflex branch; RCA = right coronary artery; HR = Hazard ratio; CI = confidence intervals; HU = Hounsfield unit; MACE = major adverse cardiovascular events. Table 3 Multivariate Cox regression analysis for predicting MACE Variables Model 1 Model 2 Model 3 HR (95% CI) P Value HR (95% CI) P Value HR (95% CI) P Value Age 1.04 (0.99–1.09) 0.683 1.03 (0.98–1.08) 0.274 1.01 (0.96–1.07) 0.683 Gender (male) 1.04 (0.40–2.70) 0.571 1.10 (0.43–2.84) 0.846 0.73 (0.25–2.16) 0.571 Smoke 1.05 (0.32–3.45) 0.935 1.64 (0.54–4.95) 0.382 1.49 (0.45–4.95) 0.519 Drink 1.24 (0.27–5.70) 0.785 0.67 (0.15–3.09) 0.606 1.00 (0.18–5.51) 0.999 Hypertension 3.08 (0.71–13.40) 0.133 3.00 (0.69–13.02) 0.143 3.86 (0.74–20.27) 0.110 Diabetes 1.55 (0.66–3.66) 0.317 1.38 (0.59–3.23) 0.460 1.42 (0.58–3.48) 0.445 CT-FFR ≤ 0.80 0.48 (0.18–1.31) 0.150 Plaque Volume > 20 mm 3 2.63 (1.08–6.44) 0.034 3.84 (1.44–10.27) 0.007 PCAT_LAD <-77 HU 1.50 (0.48–4.67) 0.484 PCAT_LCX <-67 HU 1.37 (0.35–5.37) 0.650 PCAT_RCA 1006 mm 3 - 0.973 Volume_ PCAT_LCX > 1754 mm 3 1.90 (0.63–5.76) 0.256 Volume_ PCAT_RCA > 1663 mm 3 0.90 (0.22–3.69) 0.881 PCAT = pericoronary adipose tissue attenuation; CT-FFR = coronary computed-derived fractional flow reserve; LAD = left anterior descending; LCX = left circumflex branch; RCA = right coronary artery; HR = Hazard ratio; CI = confidence intervals; HU = Hounsfield unit; MACE = major adverse cardiovascular events. Discussion As far as we know, this is the first study to explore the association between deep learning based CT-FFR, PCAT CT attenuation and plaque volume and MACE in patients with PCI. The current study investigated the prognostic potential of deep learning based CT-FFR, PCAT CT attenuation and plaque volume in patients with PCI. Our results demonstrated that PCAT_RCA CT attenuation and plaque volume were independently associated with increasing risk of MACE. Previous studies have elaborated the prognostic value of invasive FFR (iFFR). The FAME (Fractional Flow Reserve Versus Angiography for Multivessel Evaluation) 1 and 2 studies revealed that a higher post-PCI iFFR value predicted a better clinical outcome ( 18 ). Furthermore, Angarwal et al indicated that post-PCI iFFR showed incremental prognostic value beyond clinical and angiographic factors in predicting MACE ( 19 ). In additional, meta-analysis has demonstrated that post-PCI iFFR value revealed an inverse relationship with composite MACE (MI, death, revascularization) ( 20 ). However, the prognostic value of deep learning based CT-FFR in patients with PCI has not been clearly explored. The present study showed that deep learning based CT-FFR was not associated with MACE in patients with PCI. PCAT CT attenuation has been regarded as an imaging biomarker of capturing coronary inflammation in coronary CTA. Previous studies have investigated the prognostic value of PCAT CT attenuation ( 12 , 13 , 21 , 22 ).the Cardiovascular RISk Prediction using Computed Tomography (CRISP-CT) study showed that PCAT_RCA CT attenuation (HR, 1.49–1.84), PCAT_LAD CT attenuation (HR, 1.77–1.78) and PCAT_LCX CT attenuation (HR, 1.37–1.47) were independently associated with all-cause death in both derivation and validation cohorts during a median follow-up of 72 months (derivation cohort) and 54 months (validation cohort), respectively ( 12 ). However, recent studies indicated that only PCAT_RCA CT attenuation was associated with poor clinical outcome. Diemen et al revealed that PCAT_RCA CT attenuation remained as an independently predictor after adjusting for clinical and imaging factors (HR: 2.45, 95%CI: 1.23–4.93, p = 0.011), whereas PCAT_LAD and PCAT_LCX CT attenuation were not associated with the endpoint ( 21 ). Tzolos et al further indicated that PCAT_RCA CT attenuation, not PCAT_LAD or PCAT_LCX CT attenuation was predictive of further MI ( 22 ). The present study also indicated that only PCAT_RCA CT attenuation was independently associated with MACE after adjusting clinical and imaging factors in patients with PCI. The possible reason for this phenomenon may be because that there is more fat structure around RCA compared to LAD and LCX. In addition, compared with LAD and LCX, there are fewer side branches of RCA. These together made the measurement and analysis of PCAT CT attenuation easier ( 13 , 22 ). Plaque volumes derived from coronary CTA have been demonstrated with high prognostic value for adverse cardiovascular events ( 23 – 25 ). Some studies revealed this prognostic value was higher than clinical risk and lumen stenosis factors ( 26 – 28 ). The present study showed similar results. The current measurements of plaque volume in coronary CTA were mainly dependent with various semi-automated research software. Although, these platforms showed high correlations with intravascular ultrasound, the measurements and analysis of plaque volume is time-consuming, as this required a large amount of manual input from expert readers ( 25 ). Therefore, it limited its implementation in clinical practice. Our deep learning based-plaque volume measurement improves this process time-saving, thus increasing its potential of clinical application in the future. The present results indicated that plaque volumes of the coronary tree quantified by automatic measurement have an independent and strong prognostic value for MACE in patients with PCI, which has not been reported previously. Moreover, we determined an optimum cutoff (> 20 mm 3 ), exceeding this value leads to a sharp increase in the risk of events. There are some limitations in the current study. First, the current study is a single center retrospective study. This study lacks information on lifestyle changes, such as exercise, sleep or dietary habits and medical therapy after coronary CTA examination, this information may influence future outcome in the present study, thus may lead to biased results. Second, the high-risk plaque (HRP) has not been evaluated because the current AI version still has shortcomings in identifying and interpreting HRP. Third, other widely classification based on cardio CT scan (coronary artery calcification scores, CACS), or clinical information (SYNTAX score) have not been included. Further large multi-center investigations are needed to determine our findings. In conclusion, deep learning based RCA PCAT and plaque volume derived from coronary CTA, not CT-FFR was found to be associated with MACE in patients with PCI. Abbreviations PCAT pericoronary adipose tissue attenuation computed tomography CTA computed tomography angiography CT-FFR computed tomography-derived fractional flow reserve MACE major adverse cardiovascular events MI myocardial infraction RCA right coronary artery PCI Percutaneous coronary intervention CAD coronary artery disease Declarations Additional Information Correspondence and requests for materials should be addressed to Z.F.H. or X.W. Competing Interest The authors declare no competing interests. Author Contribution Study concepts: X.W., Z.F.H., Study design: X.W., Z.F.H., R.Y.T., X.Y.D., Y.D., Data acquisition and analysis: Z.W.Y., B.B.C., M.L., X.W., W.P.W., Z.Q.L., J.W.X., Statistical analysis: Z.F.H., Z.W.Y., Manuscript preparation: Z.F.H., R.Y.T., X.Y.D., Y.D., Manuscript editing and review: X.W., Z.F.H. Data Availability The datasets generated during and analyzed during the current study are available from the corresponding author on reasonable request. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4343032","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":300950571,"identity":"9b4bc382-bef7-4b33-89cc-48f37a2d2d15","order_by":0,"name":"Zengfa Huang","email":"","orcid":"","institution":"The Central Hospital of Wuhan, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Zengfa","middleName":"","lastName":"Huang","suffix":""},{"id":300950572,"identity":"b8cf758b-fbc4-46c6-91a9-b2080f1e3d70","order_by":1,"name":"Ruiyao Tang","email":"","orcid":"","institution":"The Central Hospital of Wuhan, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Ruiyao","middleName":"","lastName":"Tang","suffix":""},{"id":300950577,"identity":"e866a3fe-c2d1-48fa-8155-690312d6f360","order_by":2,"name":"Xinyu Du","email":"","orcid":"","institution":"The Central Hospital of Wuhan, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xinyu","middleName":"","lastName":"Du","suffix":""},{"id":300950579,"identity":"9954ebf4-5432-4316-afad-29d9a62ee99b","order_by":3,"name":"Yi Ding","email":"","orcid":"","institution":"The Central Hospital of Wuhan, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Ding","suffix":""},{"id":300950583,"identity":"6346456b-8391-4d44-82f3-063d9630ab8a","order_by":4,"name":"ZhiWen Yang","email":"","orcid":"","institution":"ShuKun Technology Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"ZhiWen","middleName":"","lastName":"Yang","suffix":""},{"id":300950585,"identity":"6eaf8088-16ed-4b1a-8f44-4a3e23480791","order_by":5,"name":"Beibei Cao","email":"","orcid":"","institution":"Hanyang District Center For Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Beibei","middleName":"","lastName":"Cao","suffix":""},{"id":300950589,"identity":"e38e6f69-897f-4790-a6f5-c811474b008d","order_by":6,"name":"Mei Li","email":"","orcid":"","institution":"Hanyang District Center For Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Mei","middleName":"","lastName":"Li","suffix":""},{"id":300950591,"identity":"84d5e886-4e99-4b55-a5fe-077742387c11","order_by":7,"name":"Xi Wang","email":"","orcid":"","institution":"The Central Hospital of Wuhan, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xi","middleName":"","lastName":"Wang","suffix":""},{"id":300950594,"identity":"aab01075-91f5-4e84-b15c-d0f59eb6c95c","order_by":8,"name":"Wanpeng Wang","email":"","orcid":"","institution":"The Central Hospital of Wuhan, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Wanpeng","middleName":"","lastName":"Wang","suffix":""},{"id":300950598,"identity":"d79adb5c-d21d-4e97-98df-726efd6e71e7","order_by":9,"name":"Zuoqin Li","email":"","orcid":"","institution":"The Central Hospital of Wuhan, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Zuoqin","middleName":"","lastName":"Li","suffix":""},{"id":300950599,"identity":"7d1916a3-46a9-468a-a77f-61a4fa4f2430","order_by":10,"name":"Jianwei Xiao","email":"","orcid":"","institution":"The Central Hospital of Wuhan, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Jianwei","middleName":"","lastName":"Xiao","suffix":""},{"id":300950600,"identity":"d28f5276-1dd7-4964-86fb-90fb21c88e37","order_by":11,"name":"Xiang Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFElEQVRIiWNgGAWjYNACAxDBw8DMYGBT38be2PjwA/FaCtIY+3gONxtLEGcVSMuHw4zzJNLbBHjwmX/87OHXPAV2efIRuQc/FxgcZmaTfNjGIMFgJ6fbgEPLmbw0yxkGycWGN/KSpWcYpLOxSSe2PShgSDY2O4Bdi9mBHDODDwbMiRtn55gx8xhY8wC1tBtIMBxI3IZLy/k3ZgYJBvUwLcwSbJIH2yR48Gm5kWP84IPB4cT50mAtzgZsEoz4tdjfeGPGOMPgeOIG+TfG0jwGaQlsPInAQDbA7RfJ/hzjzzx/qhPn95wxBDJsEuTbjz98+KHCTg6XFiBgA8ebAaoCA5zKQYAZnDrkG/AqGgWjYBSMgpEMADwQWyHv0QWKAAAAAElFTkSuQmCC","orcid":"","institution":"The Central Hospital of Wuhan, Huazhong University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Xiang","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-04-29 13:00:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4343032/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4343032/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56548056,"identity":"79e57fa6-6fc9-444a-9e17-9be251ba0fe6","added_by":"auto","created_at":"2024-05-15 15:40:00","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":171978,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative event survivals of PCAT_RCA and plaque volume in the study patients.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4343032/v1/0064b2176c6a8343453af493.jpeg"},{"id":56548058,"identity":"d895c39b-a1c3-46d0-970f-8d4939638518","added_by":"auto","created_at":"2024-05-15 15:40:00","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":174510,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative event survivals of PCAT_LAD and PCAT_LCX in the study patients.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4343032/v1/4d19650bc4747d2ee111fbe6.jpeg"},{"id":56548061,"identity":"142aa9c8-dbd0-4b90-8f76-b778bc9050d0","added_by":"auto","created_at":"2024-05-15 15:40:00","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":188119,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative event survivals of CT-FFR in the study patients.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4343032/v1/a0c8bbef30706c96ae82fbd0.jpeg"},{"id":56548066,"identity":"e1a549bf-9323-45ba-8549-3c6cbb6ee4b1","added_by":"auto","created_at":"2024-05-15 15:40:01","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":288273,"visible":true,"origin":"","legend":"\u003cp\u003eThe area under ROC curve for predicting MACE in four models. Model 1 = clinical model (age, gender, smoke, drink, hypertension, diabetes); Model 2 = clinical model + PCAT_RCA CT attenuation; Model 3 = clinical model + plaque volume; Model 4 = clinical model + PCAT_RCA + plaque volume.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4343032/v1/bd1aa3377238c5d9d5dfb1b8.jpeg"},{"id":56549861,"identity":"187fa3db-766c-422f-a877-05270110bc42","added_by":"auto","created_at":"2024-05-15 15:48:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1421057,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4343032/v1/4cba7fd8-0686-4d23-887a-6b59c720cb35.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic value of deep learning based RCA PCAT and plaque volume beyond CT-FFR in patients with stent implantation","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePercutaneous coronary intervention (PCI) is a recommended as the first-line treatment for patients with high-complexity coronary artery disease (CAD) in ESC and AHA guidelines (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). More than 500,000 PCI procedures are performed annually worldwide for stable CAD (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). According to the statistical data from National Center For Cardiovascular Disease (NCCD), The total number of registered cases of PCI therapy in mainland China was 1,164,117, which increase 20.18% than 2020 (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). However, the incidence of MACE including myocardial infarction, revascularizations and all-cause mortality after PCI remains up to 50\u0026ndash;15% (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). In addition, previous study has reported that the recurrent rate of chest pain is up to 50% (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Previous studies have used clinical models and SYNTAX II score to evaluate the prognosis in patients with PCI (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Unfortunately, so far, limited studies have reported effective method contains coronary computed tomography angiography (CTA) features to assess the prognostic value in those patients (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCoronary CTA was regarded as the first-line examination for evaluation of patients with PCI. Recently, coronary CTA -derived fractional flow reserve (CT-FFR), plaque volume and perivascular fat attenuation index has been introduced as a novel imaging biomarker in patients with CAD. Previous study has indicated that post-PCI invasive FFR values revealed closely association with repeat PCI and poor prognosis during follow-up (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Furthermore, a recent study with small sample has showed that CT-FFR was highly correlated with FFR and performed good prognostic value in predicting MACE in patients with PCI (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Another novel imaging biomarker measured in coronary CTA is perivascular fat attenuation index (FAI) which reflects the coronary vascular inflammation. The increased FAI value was reported to be associated with increased risk of MACE (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Recent coronary CTA studies have demonstrated that plaque volume provides independent further MACE events prediction during follow-up (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). However, little is known about the difference between CT-FFR, FAI and plaque volume for predicting MACE in patients with PCI. Thus, the present study aimed to investigate the prognostic value of deep learning based CT-FFR, pericoronary adipose tissue attenuation computed tomography (PCAT) and plaque volume in patients with PCI.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient Selection and Study Design\u003c/h2\u003e \u003cp\u003e The retrospective and observational study complied with the Declaration of Helsinki. The study protocol was approved by the institutional ethics committee and written informed consent was waived by institutional ethics committee because of its retrospective observational nature of the study. Between November 2018 and December 2020, consecutive patients who underwent coronary CTA for evaluating percutaneous coronary intervention (PCI) were retrospectively enrolled from our two hospitals (Nanjing Road and Houhu districts). The inclusion criterion was age above 18 years. Exclusion criteria were history of myocardial infraction, coronary revascularization (coronary artery bypass grafting, CABG), low quality coronary CTA images, missing coronary CTA images or reports, or reports without stenosis information, missing CT-FFR values, loss of follow-up.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCoronary CTA Acquisition Protocol\u003c/h2\u003e \u003cp\u003eAll coronary CTA examinations were performed with prospectively or retrospectively ECG-triggered on dual-scanner CT scanner (Somatom Definition, Siemens Medical Solutions, Forchheim, Germany) or ICT scanner (Philips Brilliance 64, Philips Medical Systems, Best, the Netherlands). Detailed coronary CTA protocol and parameters were presented in previous reports (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCT-FFR, PCAT CT Attenuation, Plaque Volume Measurement and Analysis\u003c/h2\u003e \u003cp\u003eCT-FFR values were calculated through commercial software based on a deep learning algorithm which has been described in our previous reports (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Plaque volume was performed on an AI ML platform (CoronaryDoc Premium, Shukun Technology Co, Beijing, China) and the total plaque volume was the sum of the separate plaque volumes in each coronary artery segment. PCAT measurements and analysis was performed using a dedicated workstation (Perivascular Fat Analysis Tool, Shukun Technology Co, Beijing, China).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eFollow up\u003c/h2\u003e \u003cp\u003e The local institutional review boards approved the follow-up procedures of the study. The primary endpoint was defined as MACE, including all-cause mortality, MI and stroke. MACE status was assessed through a query of the local Community Health Service Centers. If the MACE occurred outside of the city, MACE was ascertained by reviewing the medical records or contacting with the patients\u0026rsquo; family by telephone to confirm the outcome. Two experts in the centers for disease control and prevention (CDC) performed the process of the follow-up with blinding to other information of the patient. The deadline date of follow-up was September 30, 2022.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eContinuous variables are shown as mean (\u0026plusmn;\u0026thinsp;SD) and categorical variables are expressed as frequencies and percentages. Student\u0026rsquo;s t-test was used to compare continuous variables between groups and chi-square test was used for the comparison of categorical variables. Cumulative event-free survival was estimated by the Kaplan-Meier method and the log-rank test was used for comparison between groups. Hazard ratio (HR) with 95% confidence intervals (95% CI) was calculated by univariate and multivariate Cox proportional hazard analysis. Multivariate Cox analysis was adjusted by gender, age, smoking status, alcohol consumption, hypertension, diabetes and total cholesterol levels. Maximally selected rank statistics method was used to evaluate the prognostic threshold of PCAT and plaque volume. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered to be statistically significant. All statistical analyses were carried out using R statistical package (version 4.3, R foundation for Statistical Computing, Vienna, Austria), SPSS (version 18, SPSS, Inc., Chicago, IL, USA) and MedCalc Statistical Software (version16.8.4 Ostend, Belgium).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePatient Basic Characteristics and Coronary CTA Features\u003c/h2\u003e \u003cp\u003eA total of 183 patients with PCI who underwent coronary CTA were included in the final analyses and 22 patients occurred MACE during a median follow-up period of 38.0 months (IQR 34.6\u0026ndash;54.6 months). Overall, the average age of 183 patients was 68.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.3 years with 111 (60.7%) male patients. Patients occurred MACE showed significantly higher percentage rate in plaque volume\u0026thinsp;\u0026gt;\u0026thinsp;20 mm\u003csup\u003e3\u003c/sup\u003e (14 (63.6%) vs. 56 (34.8%), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009) and PCAT_RCA \u0026lt; -64 HU (4 (18.2%) vs. 7 (4.3%), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010) than those in patients without MACE. However, CT-FFR, PCAT _LAD, PCAT _LCX and PCAT volume showed no significant differences between patients with and without MACE. The patients\u0026rsquo; characteristics and coronary CTA features were displayed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristic and Coronary CTA features stratified by the occurrence of MACE\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (N\u0026thinsp;=\u0026thinsp;183)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWithout MACE (N\u0026thinsp;=\u0026thinsp;161)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWith MACE (N\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.2\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (Male, n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111 (60.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97 (60.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (63.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.760\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 (27.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (27.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (31.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrink\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (14.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e138 (75.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118 (73.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (90.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78 (42.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (41.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (54.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary CTA features\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT-FFR\u0026thinsp;\u0026le;\u0026thinsp;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101 (55.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89 (55.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (54.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlaque Volume\u0026thinsp;\u0026gt;\u0026thinsp;20 mm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 (38.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 (34.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (63.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFAI_LAD \u0026lt;-77 HU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68 (37.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (35.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFAI_LCX \u0026lt;-67 HU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (23.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (21.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (36.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFAI_RCA \u0026lt;-64 HU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolume_ PCAT_LAD\u0026thinsp;\u0026gt;\u0026thinsp;1006 mm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e172 (94.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150 (93.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolume_ PCAT_LCX\u0026thinsp;\u0026gt;\u0026thinsp;1754 mm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (18.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (22.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.594\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolume_ PCAT_RCA\u0026thinsp;\u0026gt;\u0026thinsp;1663 mm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e159 (86.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e140 (87.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (86.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003ePCAT\u0026thinsp;=\u0026thinsp;pericoronary adipose tissue attenuation; CT-FFR\u0026thinsp;=\u0026thinsp;coronary computed-derived fractional flow reserve;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eLAD\u0026thinsp;=\u0026thinsp;left anterior descending; LCX\u0026thinsp;=\u0026thinsp;left circumflex branch; RCA\u0026thinsp;=\u0026thinsp;right coronary artery; HR\u0026thinsp;=\u0026thinsp;Hazard ratio;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eCI\u0026thinsp;=\u0026thinsp;confidence intervals; HU\u0026thinsp;=\u0026thinsp;Hounsfield unit; MACE\u0026thinsp;=\u0026thinsp;major adverse cardiovascular events.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCT-FFR, PCAT CT Attenuation and Plaque Volume with MACE\u003c/h3\u003e\n\u003cp\u003eThe prognostic threshold of PCAT CT attenuation was \u0026gt; -77 HU for LAD, \u0026gt; -67 HU for LCX, \u0026gt; -64 HU for RCA, respectively, according to Youden index analysis. From Youden index analysis, \u0026gt; 20 mm\u003csup\u003e3\u003c/sup\u003e was the prognostic threshold of the plaque volume. The Kaplan-Meier survival curves indicated that plaque volume and PCAT_RCA CT attenuation were associated with increasing rate of MACE (log-rank \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008 for plaque volume and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007 for PCAT_RCA, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). However, PCAT_LAD and PCAT_LCX CT attenuation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), as well as CT-FFR showed no association with the increasing rate of MACE (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eUnivariable and Multivariable Cox Regression Analysis\u003c/h2\u003e \u003cp\u003eUnivariable Cox regression analysis showed that PCAT_RCA CT attenuation (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012) and plaque volume (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013) were associated with MACE (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In multivariable Cox regression analysis, after adjusting clinical risk factors (age, gender, smoke, drink, hypertension, diabetes), PCAT_RCA CT attenuation was associated with MACE in model 1 (HR, 4.03; 95% CI: 1.16\u0026ndash;13.98, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028) and plaque volume was associated with MACE in model 2 (HR, 2.63; 95% CI: 1.08\u0026ndash;6.44, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034). After adjusting clinical risk factors and all coronary CTA features, PCAT_RCA CT attenuation (HR, 7.05; 95% CI: 1.44\u0026ndash;34.63, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016) and plaque volume (HR, 3.84; 95% CI: 1.44\u0026ndash;10.27, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) remained independent significant predictors of MACE in model 4 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The area under ROC curve (AUC) for predicting MACE was 0.678 (95% CI: 0.566\u0026ndash;0.791), 0.697 (95% CI: 0.590\u0026ndash;0.804), 0.727 (95% CI: 0.621\u0026ndash;0.833), 0.745 (95% CI: 0.643\u0026ndash;0.846) for clinical model (age, gender, smoke, drink, hypertension, diabetes), clinical model\u0026thinsp;+\u0026thinsp;PCAT_RCA CT attenuation, clinical model\u0026thinsp;+\u0026thinsp;plaque volume and clinical model\u0026thinsp;+\u0026thinsp;PCAT_RCA CT attenuation\u0026thinsp;+\u0026thinsp;plaque volume, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariable Cox regression analysis for predicting MACE\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03 (0.99\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.15 (0.48\u0026ndash;2.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.750\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.24 (0.50\u0026ndash;3.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.642\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrink\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.90 (0.27\u0026ndash;3.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.41 (0.90\u0026ndash;14.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.64 (0.71\u0026ndash;3.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT-FFR\u0026thinsp;\u0026le;\u0026thinsp;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.96 (0.41\u0026ndash;2.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.916\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlaque Volume\u0026thinsp;\u0026gt;\u0026thinsp;20 mm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.06 (1.28\u0026ndash;7.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCAT_LAD \u0026lt;-77 HU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.78 (0.77\u0026ndash;4.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCAT_LCX \u0026lt;-67 HU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.95 (0.82\u0026ndash;4.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCAT_RCA \u0026lt;-64 HU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.98 (1.35\u0026ndash;11.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolume_ PCAT_LAD\u0026thinsp;\u0026gt;\u0026thinsp;1006 mm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.25 (0.01\u0026ndash;40781)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.418\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolume_ PCAT_LCX\u0026thinsp;\u0026gt;\u0026thinsp;1754 mm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.36 (0.50\u0026ndash;3.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.544\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolume_ PCAT_RCA\u0026thinsp;\u0026gt;\u0026thinsp;1663 mm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.30\u0026ndash;3.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.982\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003ePCAT\u0026thinsp;=\u0026thinsp;pericoronary adipose tissue attenuation; CT-FFR\u0026thinsp;=\u0026thinsp;coronary computed-derived fractional flow reserve; LAD\u0026thinsp;=\u0026thinsp;left anterior descending; LCX\u0026thinsp;=\u0026thinsp;left circumflex branch; RCA\u0026thinsp;=\u0026thinsp;right coronary artery; HR\u0026thinsp;=\u0026thinsp;Hazard ratio; CI\u0026thinsp;=\u0026thinsp;confidence intervals; HU\u0026thinsp;=\u0026thinsp;Hounsfield unit; MACE\u0026thinsp;=\u0026thinsp;major adverse cardiovascular events.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate Cox regression analysis for predicting MACE\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.04 (0.99\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.03 (0.98\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.01 (0.96\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.683\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.04 (0.40\u0026ndash;2.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.10 (0.43\u0026ndash;2.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.73 (0.25\u0026ndash;2.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.571\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.05 (0.32\u0026ndash;3.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.64 (0.54\u0026ndash;4.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.49 (0.45\u0026ndash;4.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.519\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrink\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.24 (0.27\u0026ndash;5.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.67 (0.15\u0026ndash;3.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00 (0.18\u0026ndash;5.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.08 (0.71\u0026ndash;13.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.00 (0.69\u0026ndash;13.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.86 (0.74\u0026ndash;20.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.55 (0.66\u0026ndash;3.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.38 (0.59\u0026ndash;3.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.42 (0.58\u0026ndash;3.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.445\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT-FFR\u0026thinsp;\u0026le;\u0026thinsp;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.48 (0.18\u0026ndash;1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlaque Volume\u0026thinsp;\u0026gt;\u0026thinsp;20 mm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.63 (1.08\u0026ndash;6.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.84 (1.44\u0026ndash;10.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCAT_LAD \u0026lt;-77 HU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.50 (0.48\u0026ndash;4.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.484\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCAT_LCX \u0026lt;-67 HU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.37 (0.35\u0026ndash;5.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCAT_RCA \u0026lt;-64 HU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.03 (1.16\u0026ndash;13.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.05 (1.44\u0026ndash;34.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolume_ PCAT_LAD\u0026thinsp;\u0026gt;\u0026thinsp;1006 mm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolume_ PCAT_LCX\u0026thinsp;\u0026gt;\u0026thinsp;1754 mm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.90 (0.63\u0026ndash;5.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolume_ PCAT_RCA\u0026thinsp;\u0026gt;\u0026thinsp;1663 mm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.90 (0.22\u0026ndash;3.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.881\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003ePCAT\u0026thinsp;=\u0026thinsp;pericoronary adipose tissue attenuation; CT-FFR\u0026thinsp;=\u0026thinsp;coronary computed-derived fractional flow reserve;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eLAD\u0026thinsp;=\u0026thinsp;left anterior descending; LCX\u0026thinsp;=\u0026thinsp;left circumflex branch; RCA\u0026thinsp;=\u0026thinsp;right coronary artery; HR\u0026thinsp;=\u0026thinsp;Hazard ratio;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eCI\u0026thinsp;=\u0026thinsp;confidence intervals; HU\u0026thinsp;=\u0026thinsp;Hounsfield unit; MACE\u0026thinsp;=\u0026thinsp;major adverse cardiovascular events.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAs far as we know, this is the first study to explore the association between deep learning based CT-FFR, PCAT CT attenuation and plaque volume and MACE in patients with PCI. The current study investigated the prognostic potential of deep learning based CT-FFR, PCAT CT attenuation and plaque volume in patients with PCI. Our results demonstrated that PCAT_RCA CT attenuation and plaque volume were independently associated with increasing risk of MACE.\u003c/p\u003e \u003cp\u003ePrevious studies have elaborated the prognostic value of invasive FFR (iFFR). The FAME (Fractional Flow Reserve Versus Angiography for Multivessel Evaluation) 1 and 2 studies revealed that a higher post-PCI iFFR value predicted a better clinical outcome (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Furthermore, Angarwal \u003cem\u003eet al\u003c/em\u003e indicated that post-PCI iFFR showed incremental prognostic value beyond clinical and angiographic factors in predicting MACE (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). In additional, meta-analysis has demonstrated that post-PCI iFFR value revealed an inverse relationship with composite MACE (MI, death, revascularization) (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). However, the prognostic value of deep learning based CT-FFR in patients with PCI has not been clearly explored. The present study showed that deep learning based CT-FFR was not associated with MACE in patients with PCI.\u003c/p\u003e \u003cp\u003ePCAT CT attenuation has been regarded as an imaging biomarker of capturing coronary inflammation in coronary CTA. Previous studies have investigated the prognostic value of PCAT CT attenuation (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).the Cardiovascular RISk Prediction using Computed Tomography (CRISP-CT) study showed that PCAT_RCA CT attenuation (HR, 1.49\u0026ndash;1.84), PCAT_LAD CT attenuation (HR, 1.77\u0026ndash;1.78) and PCAT_LCX CT attenuation (HR, 1.37\u0026ndash;1.47) were independently associated with all-cause death in both derivation and validation cohorts during a median follow-up of 72 months (derivation cohort) and 54 months (validation cohort), respectively (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). However, recent studies indicated that only PCAT_RCA CT attenuation was associated with poor clinical outcome. Diemen \u003cem\u003eet al\u003c/em\u003e revealed that PCAT_RCA CT attenuation remained as an independently predictor after adjusting for clinical and imaging factors (HR: 2.45, 95%CI: 1.23\u0026ndash;4.93, p\u0026thinsp;=\u0026thinsp;0.011), whereas PCAT_LAD and PCAT_LCX CT attenuation were not associated with the endpoint (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Tzolos \u003cem\u003eet al\u003c/em\u003e further indicated that PCAT_RCA CT attenuation, not PCAT_LAD or PCAT_LCX CT attenuation was predictive of further MI (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The present study also indicated that only PCAT_RCA CT attenuation was independently associated with MACE after adjusting clinical and imaging factors in patients with PCI. The possible reason for this phenomenon may be because that there is more fat structure around RCA compared to LAD and LCX. In addition, compared with LAD and LCX, there are fewer side branches of RCA. These together made the measurement and analysis of PCAT CT attenuation easier (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePlaque volumes derived from coronary CTA have been demonstrated with high prognostic value for adverse cardiovascular events (\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Some studies revealed this prognostic value was higher than clinical risk and lumen stenosis factors (\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). The present study showed similar results. The current measurements of plaque volume in coronary CTA were mainly dependent with various semi-automated research software. Although, these platforms showed high correlations with intravascular ultrasound, the measurements and analysis of plaque volume is time-consuming, as this required a large amount of manual input from expert readers (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Therefore, it limited its implementation in clinical practice. Our deep learning based-plaque volume measurement improves this process time-saving, thus increasing its potential of clinical application in the future. The present results indicated that plaque volumes of the coronary tree quantified by automatic measurement have an independent and strong prognostic value for MACE in patients with PCI, which has not been reported previously. Moreover, we determined an optimum cutoff (\u0026gt;\u0026thinsp;20 mm\u003csup\u003e3\u003c/sup\u003e), exceeding this value leads to a sharp increase in the risk of events.\u003c/p\u003e \u003cp\u003eThere are some limitations in the current study. First, the current study is a single center retrospective study. This study lacks information on lifestyle changes, such as exercise, sleep or dietary habits and medical therapy after coronary CTA examination, this information may influence future outcome in the present study, thus may lead to biased results. Second, the high-risk plaque (HRP) has not been evaluated because the current AI version still has shortcomings in identifying and interpreting HRP. Third, other widely classification based on cardio CT scan (coronary artery calcification scores, CACS), or clinical information (SYNTAX score) have not been included. Further large multi-center investigations are needed to determine our findings.\u003c/p\u003e \u003cp\u003eIn conclusion, deep learning based RCA PCAT and plaque volume derived from coronary CTA, not CT-FFR was found to be associated with MACE in patients with PCI.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCAT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epericoronary adipose tissue attenuation computed tomography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCTA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecomputed tomography angiography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCT-FFR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecomputed tomography-derived fractional flow reserve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMACE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emajor adverse cardiovascular events\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emyocardial infraction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eright coronary artery\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePercutaneous coronary intervention\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecoronary artery disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eAdditional Information\u003c/h2\u003e \u003cp\u003eCorrespondence and requests for materials should be addressed to Z.F.H. or X.W.\u003c/p\u003e \u003ch2\u003eCompeting Interest\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eStudy concepts: X.W., Z.F.H., Study design: X.W., Z.F.H., R.Y.T., X.Y.D., Y.D., Data acquisition and analysis: Z.W.Y., B.B.C., M.L., X.W., W.P.W., Z.Q.L., J.W.X., Statistical analysis: Z.F.H., Z.W.Y., Manuscript preparation: Z.F.H., R.Y.T., X.Y.D., Y.D., Manuscript editing and review: X.W., Z.F.H.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKnuuti J, Wijns W, Saraste A, et al. 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes. European heart journal. 2020; 41(3):407\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWriting Committee M, Virani SS, Newby LK, et al. 2023 AHA/ACC/ACCP/ASPC/NLA/PCNA Guideline for the Management of Patients With Chronic Coronary Disease: A Report of the American Heart Association/American College of Cardiology Joint Committee on Clinical Practice Guidelines. Journal of the American College of Cardiology. 2023; 82(9):833\u0026ndash;955.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNowbar AN, Rajkumar C, Foley M, et al. A double-blind randomised placebo-controlled trial of percutaneous coronary intervention for the relief of stable angina without antianginal medications: design and rationale of the ORBITA-2 trial. EuroIntervention: journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology. 2022; 17(18):1490\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNational Center for Cardiovascular Diseases. 2022 Report on Cardiovascular Health and Diseases in China (in Chinese). Beijing: Peking Union Medical College Press, 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTao S, Tang X, Yu L, et al. Prognosis of coronary heart disease after percutaneous coronary intervention: a bibliometric analysis over the period 2004\u0026ndash;2022. European journal of medical research. 2023; 28(1):311.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbbate A, Biondi-Zoccai GG, Agostoni P, Lipinski MJ, Vetrovec GW. Recurrent angina after coronary revascularization: a clinical challenge. European heart journal. 2007; 28(9):1057\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAuthors/Task Force m, Windecker S, Kolh P, et al. 2014 ESC/EACTS Guidelines on myocardial revascularization: The Task Force on Myocardial Revascularization of the European Society of Cardiology (ESC) and the European Association for Cardio-Thoracic Surgery (EACTS)Developed with the special contribution of the European Association of Percutaneous Cardiovascular Interventions (EAPCI). European heart journal. 2014; 35(37):2541\u0026ndash;619.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFarooq V, van Klaveren D, Steyerberg EW, et al. Anatomical and clinical characteristics to guide decision making between coronary artery bypass surgery and percutaneous coronary intervention for individual patients: development and validation of SYNTAX score II. Lancet. 2013; 381(9867):639\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHossain A, Small G, Crean AM, et al. Prognostic value of coronary computed tomography angiography in patients with prior percutaneous coronary intervention. Journal of cardiovascular computed tomography. 2021; 15(3):268\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRimac G, Fearon WF, De Bruyne B, et al. Clinical value of post-percutaneous coronary intervention fractional flow reserve value: A systematic review and meta-analysis. American heart journal. 2017; 183:1\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang CX, Guo BJ, Schoepf JU, et al. Feasibility and prognostic role of machine learning-based FFR(CT) in patients with stent implantation. European radiology. 2021; 31(9):6592\u0026ndash;604.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOikonomou EK, Marwan M, Desai MY, et al. 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JACC Cardiovascular imaging. 2024; 17(3):269\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMin JK, Chang HJ, Andreini D, et al. Coronary CTA plaque volume severity stages according to invasive coronary angiography and FFR. Journal of cardiovascular computed tomography. 2022; 16(5):415\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang Z, Yang Y, Wang Z, et al. Comparison of prognostic value between CAD-RADS 1.0 and CAD-RADS 2.0 evaluated by convolutional neural networks based CCTA. Heliyon. 2023; 9(5):e15988.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang Z, Xiao J, Wang X, et al. Clinical Evaluation of the Automatic Coronary Artery Disease Reporting and Data System (CAD-RADS) in Coronary Computed Tomography Angiography Using Convolutional Neural Networks. 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Circulation. 2020; 141(18):1452\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"coronary computed tomography angiography, pericoronary adipose tissue attenuation computed tomography, percutaneous coronary intervention, prognosis","lastPublishedDoi":"10.21203/rs.3.rs-4343032/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4343032/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe study aims to investigate the prognostic value of deep learning based pericoronary adipose tissue attenuation computed tomography (PCAT) and plaque volume beyond coronary computed tomography angiography (CTA) -derived fractional flow reserve (CT-FFR) in patients with percutaneous coronary intervention (PCI). A total of 183 patients with PCI who underwent coronary CTA were included in this retrospectively study. Imaging assessment included PCAT, plaque volume and CT-FFR which were performed using an artificial intelligence (AI) assisted workstation. Kaplan-Meier and multivariate Cox regression were used to estimate major adverse cardiovascular events (MACE) including non-fatal myocardial infraction (MI), stroke and mortality. In total, 22 (12%) MACE occurred during the median follow-up of 38.0 months (interquartile range 34.6\u0026ndash;54.6 months). Kaplan-Meier survival curves indicated that right coronary artery (RCA) PCAT (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) and plaque volume (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008) were significantly associated with the increasing of MACE. Multivariable Cox regression analysis showed that RCA PCAT [hazard ratios (\u003cem\u003eHR\u003c/em\u003e): 2.94, \u003cem\u003e95%CI\u003c/em\u003e: 1.15\u0026ndash;7.50, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025] and plaque volume (\u003cem\u003eHR\u003c/em\u003e: 3.91, \u003cem\u003e95%CI\u003c/em\u003e: 1.20-12.75, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024) were independent predictors of MACE after adjusting for clinical risk factors. However, CT-FFR was not independently associated with MACE in multivariable Cox regression (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.271). Deep learning based RCA PCAT and plaque volume derived from coronary CTA was found to be more strongly associated with MACE than CT-FFR in patients with PCI.\u003c/p\u003e","manuscriptTitle":"Prognostic value of deep learning based RCA PCAT and plaque volume beyond CT-FFR in patients with stent implantation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-15 15:39:55","doi":"10.21203/rs.3.rs-4343032/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3ab8298b-8282-4f4b-85b3-665944b693ba","owner":[],"postedDate":"May 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":31755324,"name":"Health sciences/Cardiology"},{"id":31755325,"name":"Health sciences/Diseases/Cardiovascular diseases"}],"tags":[],"updatedAt":"2024-05-15T15:39:59+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-15 15:39:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4343032","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4343032","identity":"rs-4343032","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}