Utility of the sum of CT-derived fractional flow reserve in three coronary arteries for predicting long-term prognosis in patients with newly diagnosed unstable angina

Utility of the sum of CT-derived fractional flow reserve in three coronary arteries for predicting long-term prognosis in patients with newly diagnosed unstable angina | 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 Research Article Utility of the sum of CT-derived fractional flow reserve in three coronary arteries for predicting long-term prognosis in patients with newly diagnosed unstable angina yao li, jun wang, zhuoya yao, chuan jin, miaonan li, hongju wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9147746/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 17 You are reading this latest preprint version Abstract Objective: To evaluate the utility of the sum of computed tomography-derived fractional flow reserve in three coronary arteries (3v-CT-FFR) for predicting major adverse cardiac and cerebrovascular events (MACCEs) in patients with newly diagnosed unstable angina (UA). Methods: This retrospective study included 157 consecutive patients who were diagnosed with UA via coronary CT angiography and concurrent invasive coronary angiography between January 2021 and December 2022 at the First Affiliated Hospital of Bengbu Medical University. The 3v-CT-FFR was defined as the sum of the CT-FFR values for the left anterior descending artery, left circumflex artery, and right coronary artery. The primary endpoint was the occurrence of MACCEs, including all-cause death, cardiac death, nonfatal myocardial infarction, recurrent angina, heart failure, unplanned revascularization, and/or stroke. The optimal cutoff value was determined using receiver operating characteristic curve analysis. Kaplan–Meier survival curves and Cox proportional hazards models were constructed to evaluate the independent utility of the 3v-CT-FFR for predicting MACCEs. Results: A total of 157 patients were included in this study, with a median follow-up duration of 30 months. During follow-up, 43 MACCEs (27.4%) were reported. The optimal 3v-CT-FFR cutoff value for predicting MACCEs was 2.50, with an area under the curve of 0.729 (95% confidence interval [CI]: 0.646–0.812, P 2.50 (38.0% vs. 16.7%, P = 0.003). Multivariate Cox regression analysis revealed that a 3v-CT-FFR ≤ 2.50 was an independent risk factor for MACCEs (hazard ratio: 4.121; 95% CI: 2.108–8.054; P 2.50 group (log-rank P < 0.001). Conclusion: A 3v-CT-FFR value ≤ 2.50 is a significant predictor of mid- to long-term MACCEs in patients with newly diagnosed UA. It holds potential value for clinical risk stratification and may help guide interventional decision-making. unstable angina CT-derived fractional flow reserve coronary computed tomography angiography clinical prognosis Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Coronary atherosclerotic heart disease (CAHD) is a leading cause of cardiovascular mortality worldwide. Among acute coronary syndromes, unstable angina (UA) is a particular concern given its rapid progression and high short-term risk of cardiovascular events, making it a key focus for acute intervention and long-term management strategies [ 1 , 2 ] . Accurate and noninvasive identification of high-risk patients with CAHD, followed by individualized treatment, is critical for reducing mortality and hospital readmission rates [ 3 – 5 ] . Traditional coronary angiography (CAG) and coronary computed tomography angiography (CCTA) can clearly delineate the anatomical severity of stenosis. However, both modalities have limitations in assessing functional ischemia, as anatomical findings alone may not accurately reflect hemodynamic significance. Fractional flow reserve (FFR), an invasive measure based on the ratio of distal to proximal coronary pressure across a stenotic lesion, has been validated as an effective guide for interventional decisions and prognostic improvement when the FFR is ≤ 0.80 and is regarded as the gold standard for evaluating functional stenosis [ 6 – 8 ] . Nevertheless, its clinical application is limited by the use of pressure wires, adenosine administration, and invasive procedures. CT-derived FFR (CT-FFR), which is based on computational fluid dynamics or machine learning algorithms applied to CCTA data, offers a noninvasive option for obtaining three-dimensional hemodynamic information. CT-FFR enables simultaneous anatomical and functional assessment and has shown high diagnostic concordance with invasive FFR [ 9 ] . Patients with UA often present with multivessel coronary artery disease (CAD), yet single-vessel CT-FFR reflects only localized functional status and fails to provide a comprehensive assessment of the overall myocardial ischemic burden. Invasive FFR studies have introduced the concept of the “sum of triple vessel FFR” (3v-FFR), which allows for an integrated evaluation of global myocardial perfusion risk. Compared with single-vessel FFR, this metric has demonstrated superior predictive value for mid- to long-term major adverse cardiac and cerebrovascular events (MACCEs) in multicenter cohorts [ 10 ] . However, the prognostic significance of a corresponding CT–FFR-based triple vessel index in patients with newly diagnosed UA has yet to be systematically investigated. This study is the first to use the sum of the CT-FFR values from the left anterior descending artery, left circumflex artery, and right coronary artery—referred to as triple vessel CT-FFR (3v-CT-FFR)—to evaluate its predictive performance for mid- to long-term MACCEs in patients with newly diagnosed UA. The 3v-CT-FFR was compared with conventional clinical and imaging parameters to assess its potential as a quantitative tool for noninvasive risk stratification and therapeutic decision-making. We hypothesize that 3v-CT-FFR reflects the functional burden of the total plaque load and may offer significant value in identifying high-risk patients, thereby supporting early intervention or intensified pharmacological intervention. MATERIALS AND METHODS 1.1 Study Subjects This retrospective study included consecutive patients with UA who underwent both CCTA and CAG at the First Affiliated Hospital of Bengbu Medical University between January 2021 and December 2022. The assessment of coronary artery stenosis severity and plaque characteristics via CCTA was based on the 2024 consensus statement by the Asian Society of Cardiovascular Imaging on coronary artery stenosis and plaque evaluation in CCTA, as well as the 2021 expert consensus from the US National Institutes of Health on CCTA [11, 12] . The study protocol was reviewed and approved by the Ethics Committee of Bengbu Medical University (Ethics Approval No. [2023]KY046). 1.1.1 Inclusion Criteria: (1) completion of both CCTA and initial CAG; (2) first-time diagnosis of UA; (3) availability of complete medical history and follow-up data; and (4) follow-up duration ≥ 12 months. 1.1.2 Exclusion Criteria: (1) acute myocardial infarction within the past week; (2) previously diagnosed CAD based on prior CAG; (3) presence of left main CAD or chronic occlusion on CCTA; (4) severe liver or kidney dysfunction, including aspartate aminotransferase or alanine aminotransferase levels higher than three times the upper limit of normal, liver cirrhosis or other significant hepatic diseases, kidney insufficiency with estimated glomerular filtration rate ≤ 60 mL/min/1.73 m²; (5) acute or decompensated chronic heart failure with New York Heart Association functional class III–IV; (6) other active inflammatory conditions, including acute infections or uncontrolled autoimmune diseases; and (7) incomplete clinical data or poor-quality CCTA images precluding accurate plaque characterization and data analysis. 1.2 Follow-up and Clinical Endpoints As of September 31, 2024, follow-up data were obtained for all patients through a review of hospital medical records and telephone interviews. The median follow-up duration was 30 months. MACCEs were defined as the occurrence of any of the following: all-cause mortality, cardiac death, myocardial infarction, recurrent angina, in-stent restenosis, in-stent thrombosis, or stroke. 1.3 Instruments and Methods 1.3.1 Blood Tests On the morning of the day following admission, 5 mL of venous blood was drawn from the cubital vein of each patient into heparinized tubes. The samples were sent to the hospital’s central laboratory for analysis of blood glucose, lipid profile, comprehensive metabolic panel, cardiac enzyme panel, cardiac troponin, and N-terminal pro-B-type natriuretic peptide. 1.3.2 CAG CAG was performed by experienced cardiologists using the Judkins technique. The interpretation of angiographic findings was based on the 2001 guidelines from the American College of Cardiology and American Heart Association for the diagnosis and management of CAD. The severity of coronary artery stenosis in each vessel was quantitatively assessed using the Gensini scoring system. Two experienced cardiologists independently evaluated the angiograms, and the average score was used for analysis. 1.3.3 CT CCTA was performed using either a 128-slice spiral CT scanner (iCT, Philips) or a 256-slice spiral CT scanner (Revolution CT, GE Healthcare, USA). Retrospective electrocardiogram-gated scanning was employed, covering the region from 1 cm below the carinal bifurcation to 1 cm below the diaphragm. Iodinated contrast medium was injected at a flow rate of 5.3–5.6 mL/s, with a total volume of 50–70 mL. For iCT scanning, the primary parameters were as follows: tube voltage of 120 kV, tube current of 800 mA, collimator and detector width of 128 mm × 0.625 mm, slice thickness of 0.9 mm, and gantry rotation time of 0.27 s. For Revolution CT, the key parameters were as follows: tube voltage of 120 kV/100 kV, automated tube current modulation, collimator width of 160 mm, and gantry rotation time of 0.28 s. The image slice thickness was set at 0.625 mm. All CCTA datasets were transferred to a postprocessing workstation (ADW 4.7, GE) for analysis. CT-FFR values were calculated using deep learning-based software (DEEPVESSELFFR, Keya Medical, Beijing, China). The software uses a diastolic-phase image of optimal quality to compute CT-FFR values. The core principle involves training a validated model through deep learning that integrates anatomical and hemodynamic features extracted from large-scale vascular data, enabling rapid and accurate computation of the FFR across the entire coronary tree [13] . CT-FFR measurements were taken at 1.5–2 cm proximal and distal to the stenotic lesions. In cases of multiple lesions within a single vessel, the value at the most distal lesion was used [14] . The 3v-CT-FFR was defined as the sum of the CT-FFR values for the left anterior descending artery, left circumflex artery, and right coronary artery. 1.5 Statistical Methods Statistical analyses were performed using SPSS27, MedCalc version 20.0.4, and GraphPad Prism 9.0. Normally distributed data are expressed as mean ± standard deviation ( x̄ ± s ), and comparisons between two groups were conducted using independent samples t tests. For nonnormally distributed data, values are presented as medians with interquartile ranges (M [P25, P75]), and comparisons were made using the Mann–Whitney U test. Enumeration data are expressed as counts and percentages ( n [%]), and comparisons between groups were performed using the chi-square (χ²) test. Pearson correlation analysis was used to evaluate the relationship between the degree of coronary lumen stenosis and the CT-FFR values. Multivariate Cox regression analysis was employed to identify factors associated with the occurrence of MACCEs. Receiver operating characteristic (ROC) curves were constructed to compare the predictive performance of coronary lumen stenosis and CT-FFR for MACCEs. All probability values were two-sided, and a P value < 0.05 was considered statistically significant. RESULTS 2.1 ROC Curve Analysis of the 3v-CT-FFR for Predicting MACCEs The ROC curve analysis identified 2.5 as the optimal cutoff value for 3v-CT-FFR in predicting MACCEs, yielding a sensitivity of 81.4%, specificity of 57.0%, and an area under the curve (AUC) of 0.727 (95% confidence interval [CI]: 0.644–0.810, P 2.5, n = 75) and a low 3v-CT-FFR group (≤ 2.5, n = 78). Variable AUC 95% CI Sensitivity Specificity Optimal cutoff Youden index 3v-CT-FFR 0.727 0.644–0.810 0.814 0.570 2.5 0.57 2.2 Comparison of Baseline Characteristics Between the Two Group Among the 157 patients included in this study, 97 (61.8%) were male and 60 (38.2%) were female, with a mean age of 61.71 ± 11.32 years. Compared with those in the 3v-CT-FFR > 2.5 group, patients in the 3v-CT-FFR ≤ 2.5 group ( n = 79) had significantly higher levels of B-type natriuretic peptide (BNP), a greater proportion of patients who underwent percutaneous coronary intervention (PCI), and more frequent postoperative use of aspirin and ticagrelor (all P < 0.05) (Table 1 ). Among the 79 patients in the 3v-CT-FFR ≤ 2.5 group, 59 underwent PCI. These patients had significantly longer survival than those who received conservative treatment did ( P 2.5 group, 36 underwent PCI, and they also had significantly longer survival than those receiving conservative treatment did ( P 0.05), the incidence of MACCEs was significantly greater in the ≤ 2.5 group ( P 2.5 groups in this subgroup ( P > 0.05) (Table 5 ). Patients were further categorized on the basis of the occurrence of MACCEs into the MACCEs group ( n = 43) and the non-MACCEs group ( n = 114). Compared with the non-MACCEs group, patients in the MACCEs group had significantly greater proportions of males, individuals with diabetes, and smokers. Additionally, the levels of serum creatinine and BNP were significantly elevated, whereas high-density lipoprotein levels and survival time were significantly lower (all P 0.05) (Table 6 ). 2.3 Comparison of Coronary Lesions between the MACCEs and Non-MACCEs Groups Among the 157 patients, 60 (38.2%) had triple-vessel CAD. The median 3v-CT-FFR across the cohort was 2.50 (2.29, 2.64), and the median Gensini score was 32.00 (14.00, 52.75). Compared with the non-MACCEs group, the MACCEs group had a significantly greater proportion of patients with triple vessel disease, lower 3v-CT-FFR values, and higher Gensini scores (all P 2.5 (n = 78) P value 61.76 ± 11.30 61.53 ± 12.26 61.99 ± 10.30 0.801 Male (%, n) 97 (61.8%) 51 (64.6%) 46 (59.0%) 0.475 Hypertension (%, n) 91 (58.0%) 49 (62.0%) 42 (53.8%) 0.299 Diabetes (%, n) 40 (25.5%) 17 (21.5%) 23 (29.5%) 0.252 Atrial fibrillation (%, n) 9 (5.7%) 5 (6.3%) 4 (5.1%) 1 Cerebral infarction (%, n) 30 (19.1%) 15 (19.0%) 15 (19.2%) 0.969 Smoking history (%, n) 84 (53.5%) 40 (50.6%) 44 (56.4%) 0.468 Aspirin (%, n) 143 (91.1%) 77 (97.5%) 66 (84.6%) 0.005 Clopidogrel (%, n) 51 (32.5%) 25 (31.6%) 26 (33.3%) 0.821 Ticagrelor (%, n) 77 (49.0%) 48 (60.8%) 29 (37.2%) 0.003 Statins (%, n) 154 (98.1%) 77 (97.5%) 77 (98.7%) 1 ARNI (%, n) 76 (48.4%) 40 (50.6%) 36 (46.2%) 0.574 Survival time(day) 938.00 (692.00, 1198.00) 997.00 (415.00, 1225.00) 932.50 (759.50, 1174.00) 0.733 PCI (%, n) 95 (60.5%) 59 (74.7%) 36 (46.2%) <0.001 Glucose(mmol/L) 5.35 (4.42, 6.23) 5.28 (4.38, 6.48) 5.30 (4.69, 6.23) 0.104 Uric acid(umol/L) 305.43 ± 84.29 302.79 ± 86.52 308.07 ± 82.48 0.701 Creatinine(umol/L) 68.00 (59.00, 75.00) 67.00 (59.00, 74.00) 67.50 (59.00, 76.75) 0.62 Total cholesterol(mmol/L) 3.95 (3.16, 4.41) 3.67 (3.04, 4.17) 3.95 (3.39, 4.84) 0.579 Triglycerides(mmol/L) 1.42 (0.96, 1.75) 1.32 (1.02, 1.83) 1.37 (0.89, 2.00) 0.968 Low-density lipoprotein(mmol/L) 2.31 (1.72, 2.83) 2.04 (1.57, 2.72) 2.20 (1.77, 2.96) 0.826 High-density lipoprotein(mmol/L) 1.02 (0.86, 1.16) 0.91 (0.82, 1.20) 1.01 (0.85, 1.18) 0.37 Lipoprotein a(mmol/L) 205.00 (104.50, 331.50) 184.00 (88.75, 353.25) 145.50 (76.00, 397.00) 0.963 N-terminal pro-B-type-natriuretic peptide(pg/ml) 115.50 (43.70, 241.81) 79.30 (51.30, 172.00) 44.20 (32.15, 127.75) 0.02 Creatine kinase(U/L) 87.50 (53.00, 93.50) 75.50 (49.75, 95.00) 64.00 (44.00, 111.00) 0.398 MACCEs (%, n) 43 (27.4%) 30 (38.0%) 13 (16.7%) 0.003 Table 2 PCI vs. non-PCI outcomes in patients with a 3v-CT-FFR ≤ 2.5 Age (year) Total 3v-CT-FFR ≤ 2.5 + PCI ( n = 59) 3v-CT-FFR ≤ 2.5 + non-PCI (n = 20) P value 61.53 ± 12.26 61.49 ± 11.39 61.65 ± 14.88 0.961 Male (%, n) 51 (64.6%) 36 (61.0%) 15 (75.0%) 0.259 Hypertension (%, n) 49 (62.0%) 39 (66.1%) 10 (50.0%) 0.200 Diabetes (%, n) 17 (21.5%) 13 (22.0%) 4 (20.0%) >0.999 Atrial fibrillation (%, n) 5 (6.3%) 3 (5.1%) 2 (10.0%) 0.803 Cerebral infarction (%, n) 15 (19.0%) 13 (22.0%) 2 (10.0%) 0.392 Smoking history (%, n) 40 (50.6%) 27 (45.8%) 13 (65.0%) 0.137 Aspirin 77(97.5%) 58(98.3%) 19(95.0%) 0.416 Ticagrelor 48(60.8%) 40(67.8%) 8(40.0%) 0.028 Clopidogrel (%, n) 25 (31.6%) 19 (32.2%) 6 (30.0%) 0.855 Statins (%, n) 77 (97.5%) 57 (96.6%) 20 (100.0%) >0.999 ARNI (%, n) 40 (50.6%) 30 (50.8%) 10 (50.0%) 0.948 Glucose(mmol/L) 5.28 (4.38, 6.48) 5.42 (4.38, 6.76) 4.67 (4.38, 5.82) 0.656 Uric acid(umol/L) 302.79 ± 86.52 298.86 ± 83.09 315.44 ± 98.27 0.481 Creatinine(umol/L) 67.00 (59.00, 74.00) 69.00 (65.00, 92.00) 67.00 (59.00, 71.00) 0.293 Total cholesterol(mmol/L) 3.67 (3.04, 4.17) 3.68 (3.62, 4.33) 3.60 (3.02, 4.16) 0.332 Triglycerides(mmol/L) 1.32 (1.02, 1.83) 1.48 (0.98, 2.02) 1.30 (1.03, 1.80) 0.856 Low-density lipoprotein(mmol/L) 2.04 (1.57, 2.72) 2.15 (1.83, 2.81) 1.97 (1.54, 2.53) 0.261 High-density lipoprotein(mmol/L) 0.91 (0.82, 1.21) 0.93 (0.84, 1.34) 0.89 (0.81, 1.20) 0.345 Lipoprotein a(mmol/L) 184.00 (88.75, 353.25) 138.00 (53.00, 372.00) 192.00 (100.00, 350.00) 0.681 N-terminal pro-B-type-natriuretic peptide(pg/ml) 79.30 (51.30, 172.00) 131.90 (54.80, 361.00) 75.60 (50.40, 169.00) 0.403 Creatine kinase(U/L) 75.50 (49.75, 95.00) 90.00 (73.00, 184.00) 65.00 (46.00, 95.00) 0.048 Survival time(day) 997.00(415.00, 1225.00) 1080.50(775.00, 1276.50) 868.00(724.35, 990.00) 0.045 MACCEs 30(38.0%) 21(35.6%) 9(45.0%) 0.454 Table 3 PCI vs. non-PCI outcomes in patients with a 3v-CT-FFR > 2.5 Age (year) Total 3v-CT-FFR > 2.5 + PCI (n = 36) 3v-CT-FFR > 2.5 + non-PCI (n = 42) P value 61.99 ± 10.30 62.00 ± 11.79 61.98 ± 8.98 0.992 Male (%, n) 46 (59.0%) 20 (55.6%) 26 (61.9%) 0.570 Hypertension (%, n) 42 (53.8%) 21 (58.3%) 21 (50.0%) 0.462 Diabetes (%, n) 23 (29.5%) 8 (22.2%) 15 (35.7%) 0.193 Atrial fibrillation (%, n) 4 (5.1%) 1 (2.8%) 3 (7.1%) 0.722 Cerebral infarction (%, n) 15 (19.2%) 6 (16.7%) 9 (21.4%) 0.595 Smoking history (%, n) 44 (56.4%) 18 (50.0%) 26 (61.9%) 0.291 Aspirin 66 (84.6%) 36 (100%) 30 (71.4%) < 0.001 Ticagrelor 29 (37.2%) 22 (61.1%) 7 (16.7%) 0.999 ARNI (%, n) 36 (46.2%) 16 (44.4%) 20 (47.6%) 0.779 Glucose(mmol/L) 5.13 (4.43, 5.83) 5.49 (4.31, 6.06) 5.24 (4.76, 6.47) 0.696 Uric acid(umol/L) 308.07 ± 82.48 298.81 ± 88.06 316.40 ± 77.28 0.610 Creatinine(umol/L) 68.00 (59.00, 76.00) 66.00 (57.50, 82.00) 67.50 (62.50, 74.25) 0.305 Total cholesterol(mmol/L) 3.88 (3.22, 4.79) 3.76 (3.17, 5.31) 3.95 (3.39, 4.60) 0.736 Triglycerides(mmol/L) 1.37 (0.87, 1.82) 1.18 (0.85, 2.25) 1.43 (0.91, 1.89) 0.881 Low-density lipoprotein(mmol/L) 2.20 (1.70, 2.94) 2.36 (1.49, 3.28) 2.05 (1.79, 2.78) 0.49 High-density lipoprotein(mmol/L) 1.01 (0.87, 1.18) 0.95 (0.83, 1.17) 1.08 (0.87, 1.18) 0.337 Lipoprotein a(mmol/L) 172.50 (93.50, 396.00) 282.00 (115.50, 413.00) 110.00 (56.00, 240.50) 0.068 N-terminal pro-B-type-natriuretic peptide(pg/ml) 46.55 (33.20, 133.00) 47.00 (33.00, 153.10) 42.35 (27.78, 97.75) 0.282 Creatine kinase(U/L) 64.00 (44.00, 111.00) 48.00 (39.00, 67.35) 89.00 (52.25, 118.50) 0.013 Survival time(day) 932.50 (759.50, 1174.00) 1080.50 (775.00, 1276.50) 868.00 (724.35, 990.00) 0.008 MACCEs 13 (16.7%) 7 (19.4%) 6 (14.3%) 0.542 Table 4 Outcomes in non-PCI patients by 3v-CT-FFR value Age (year) Total Non-PCI + 3v-CT-FFR > 2.5 ( n = 42) Non-PCI + 3v-CT-FFR ≤ 2.5 ( n = 20) P value 61.87 ± 11.10 61.98 ± 8.98 61.65 ± 14.88 0.915 Male (%, n) 41 (66.1%) 26 (61.9%) 15 (75.0%) 0.308 Hypertension (%, n) 31 (50.0%) 21(50.0%) 10 (50.0%) >0.999 Diabetes (%, n) 19 (30.6%) 15 (35.7%) 4 (20.0%) 0.210 Atrial fibrillation (%, n) 5 (8.1%) 3 (7.1%) 2 (10.0%) >0.999 Cerebral infarction (%, n) 11 (17.7%) 9 (21.4%) 2 (10.0%) 0.456 Smoking history (%, n) 39 (62.9%) 26 (61.9%) 13 (65.0%) 0.814 Aspirin (%, n) 49 (79.0%) 30 (71.4%) 19 (95.0%) 0.072 Clopidogrel (%, n) 18 (29.0%) 12 (28.6%) 6 (30.0%) 0.908 Ticagrelor (%, n) 15 (24.2%) 7 (16.7%) 8 (40.0%) 0.901 Statins (%, n) 61 (98.4%) 41 (97.6%) 20 (100.0%) >0.999 ARNI (%, n) 30 (48.4%) 20 (47.6%) 10 (50.0%) 0.861 Glucose(mmol/L) 5.16(4.63, 6.02) 5.24 (4.76, 6.47) 4.67(4.38, 5.82) 0.585 Uric acid(umol/L) 316.10.±82.46 316.40 ± 77.28 315.44 ± 98.27 0.968 Creatinine(umol/L) 68.00 (64.50, 77.50) 67.50 (62.50, 74.25) 69.00 (65.00, 92.00) 0.827 Total cholesterol(mmol/L) 3.86 (3.43, 4.43) 3.95 (3.39, 4.60) 3. 68(3.62, 4.33) 0.785 Triglycerides(mmol/L) 1.43(0.96, 1.92) 1.43 (0.91, 1.89) 1.48 (0.98, 2.02) 0.925 Low-density lipoprotein(mmol/L) 2.08 (1.82, 2.79) 2.05 (1.79, 2.78) 2.15 (1.83, 2.81) 0.353 High-density lipoprotein(mmol/L) 1.00(0.85, 1.19) 0.94 (0.83, 1.17) 0.89 (0.81, 1.20) 0.971 Lipoprotein a(mmol/L) 206.00 (101.00, 394.00) 110.00 (56.00, 240.50) 138.00 (53.00, 372.00) 0.649 N-terminal pro-B-type-natriuretic peptide(pg/ml) 113.00 (56.00, 268.00) 42.35 (27.78, 97.75) 131.90 (54.80, 361.00) 0.067 Creatine kinase(U/L) 90.00 (58.50, 123.00) 89.00 (52.25, 118.50) 90.00 (73.00, 184.00) 0.355 Survival time(day) 844.50 (679.00, 1009.00) 840.36 ± 244.37 680.90 ± 423.60 0.130 MACCEs (%, n) 15 (24.2%) 6 (14.3%) 9 (45.0%) 0.008 Table 5 Outcomes in PCI-treated patients by 3v-CT-FFR value Age (year) Total PCI + 3v-CT-FFR > 2.5 ( n = 36) PCI + 3v-CT-FFR ≤ 2.5 ( n = 59) P value 61.68 ± 11.48 58.00(51.00, 67.75) 65.00(53.00, 71.00) 0.966 Male (%, n) 56 (58.9%) 20 (55.6%) 36 (61.0%) 0.600 Hypertension (%, n) 60 (63.2%) 21(58.3%) 39 (66.1%) 0.446 Diabetes (%, n) 21 (22.1%) 8 (22.2%) 13 (22.0%) 0.983 Atrial fibrillation (%, n) 4 (4.2%) 1 (2.8%) 3 (5.1%) 0.987 Cerebral infarction (%, n) 19 (20.0%) 6 (16.7%) 13 (22.0%) 0.526 Smoking history (%, n) 45 (47.4%) 18 (50.0%) 27 (45.8%) 0.688 Aspirin (%, n) 94 (98.9%) 36 (100%) 58 (98.3%) 0.432 Clopidogrel (%, n) 33 (34.7%) 14 (38.9%) 19 (32.2%) 0.507 Ticagrelor (%, n) 62 (65.3%) 22 (61.1%) 40 (67.8%) 0.507 Statins (%, n) 93 (97.9%) 36 (100%) 57 (96.6%) 0.704 ARNI (%, n) 46 (48.4%) 30 (50.8%) 16 (44.4%) 0.545 Glucose(mmol/L) 5.49(4.31, 6.35) 5.51 (4.30, 6.15) 5.42 (4.38, 6.76) 0.198 Uric acid(umol/L) 298.84 ± 84.56 298.81 ± 88.06 298.86 ± 83.09 0.672 Creatinine(umol/L) 67.00 (59.00, 75.00) 68.00 (58.25, 83.50) 67.00 (59.00, 71.00) 0.898 Total cholesterol(mmol/L) 3.70 (3.03, 4.41) 3.92 (3.28, 5.41) 3. 02(3.60, 4.16) 0.62 Triglycerides(mmol/L) 1.23(0.97, 1.92) 0.84 (1.20, 2.35) 1.30 (1.03, 1.80) 0.997 Low-density lipoprotein(mmol/L) 2.04 (1.57, 3.04) 2.51 (1.62, 3.30) 1.97 (1.54, 2.53) 0.425 High-density lipoprotein(mmol/L) 0.92(0.81, 1.18) 0.94 (0.83, 1.17) 0.89 (0.81, 1.20) 0.542 Lipoprotein a(mmol/L) 206.00 (101.00, 394.00) 280.00 (113.25, 416.50) 192.00 (100.00, 350.00) 0.36 N-terminal pro-B-type-natriuretic peptide(pg/ml) 68.50 (37.20, 162.00) 46.35 (32.65, 133.50) 75.60 (50.40, 169.00) 0.372 Creatine kinase(U/L) 61.00 (43.00, 82.00) 48.00 (37.50, 70.03) 65.00 (46.00, 95.00) 0.103 MACCEs (%, n) 43 (27.4%) 30 (38.0%) 13 (16.7%) 0.003 Survival time(day) 1117.00 (711.00, 1266.00) 1080.50 (775.00, 1276.50) 1140.00 (452.00, 1256.00) 0.432 Table 6 Comparison of clinical characteristics between the MACCEs and non-MACCEs groups Age (year) 总计 MACCEs(n = 43) Non- MACCEs(n = 114) P value 61.71 ± 11.32 62.95 ± 11.64 61.31 ± 11.19 0.417 Male (%, n) 97 (61.8%) 32 (74.4%) 65 (57.0%) 0.045 Hypertension (%, n) 91 (58.0%) 24 (55.8%) 67 (58.8%) 0.738 Diabetes (%, n) 40 (25.5%) 23 (53.5%) 17 (14.9%) <0.001 Atrial fibrillation (%, n) 9 (5.7%) 3 (7.0%) 6 (5.3%) 0.978 Cerebral infarction (%, n) 30 (19.1%) 10 (23.3%) 20 (17.5%) 0.417 Smoking history (%, n) 84 (53.5%) 30 (69.8%) 54 (47.4%) 0.012 Aspirin (%, n) 143 (91.1%) 41 (95.3%) 102 (89.5%) 0.402 Clopidogrel (%, n) 51 (32.5%) 18 (41.9%) 33 (28.9%) 0.123 Ticagrelor (%, n) 77 (49.0%) 22 (51.2%) 55 (48.2%) 0.744 Statins (%, n) 154 (98.1%) 42 (97.7%) 112 (98.2%) >0.999 ARNI (%, n) 76 (48.4%) 22 (51.2%) 54 (47.4%) 0.671 Survival time(day) 30.00 (22.00, 38.00) 302.00 (149.00, 517.00) 1082.50 (860.75, 1242.25) <0.001 PCI (%, n) 95 (60.5%) 28 (65.1%) 67 (58.8%) 0.468 Glucose(mmol/L) 5.35 (4.42, 6.23) 4.97 (4.38, 6.35) 5.38 (4.43, 6.15) 0.502 Uric Acid(umol/L) 295.00 (247.50, 345.00) 300.00 (267.00, 367.00) 294.00 (242.00, 343.00) 0.255 Creatinine(umol/L) 68.00 (59.00, 75.00) 70.00 (66.00, 81.00) 66.00 (57.50, 72.00) 0.005 Total cholesterol(mmol/L) 3.95 (3.16, 4.41) 3.94 (3.16, 4.07) 3.99 (3.17, 4.81) 0.364 Triglycerides(mmol/L) 1.42 (0.96, 1.75) 1.32 (0.87, 1.75) 1.43 (0.97, 1.77) 0.585 Low-density lipoprotein(mmol/L) 2.31 (1.72, 2.83) 2.31 ± 0.73 2.39 ± 1.03 0.672 High-density lipoprotein(mmol/L) 1.02 (0.86, 1.16) 0.96 ± 0.20 1.05 ± 0.23 0.027 Lipoprotein a(mmol/L) 205.00 (104.50, 331.50) 229.00 (98.00, 422.00) 204.00 (105.00, 282.00) 0.462 N-terminal pro-B-type-natriuretic peptide(pg/ml) 115.50 (43.70, 241.81) 148.00 (65.10, 241.81) 103.00 (37.05, 241.81) 0.044 Creatine kinase(U/L) 87.50 (53.00, 93.50) 90.90 (58.00, 95.00) 79.00 (52.00, 90.95) 0.200 Table 7 Comparison of coronary lesion characteristics between the MACCEs and non-MACCEs groups 3v-CT-FFR ≤ 2.5 (%, n) Total MACCEs group ( n = 43) Non-MACCEs group ( n = 114) P value 79 (50.3%) 30 (69.8%) 49 (43.0%) 0.003 Triple vessel CAD (%, n) 60 (38.2%) 28 (65.1%) 32 (28.1%) < 0.001 Grace 97.73 ± 22.17 99.33 ± 24.54 97.12 ± 21.29 0.581 Gensini 32.00 (14.00, 52.75) 42.00 (20.00, 73.00) 28.00 (12.00, 46.00) 0.002 2.4 Survival Analysis and Multivariate Cox Regression Analysis To evaluate the predictors of MACCEs, survival analysis and multivariate Cox proportional hazards regression analysis were performed. The occurrence of MACCEs was used as the dependent variable (coded as 1 for event occurrence and 0 for no event), and the following variables were included as independent factors: presence of diabetes mellitus, smoking status, serum creatinine level, number of diseased coronary vessels, and 3v-CT-FFR value. The results identified the following as independent predictors of MACCEs: (1) 3v-CT-FFR ≤ 2.5: hazard ratio (HR) = 4.121, 95% CI: 2.108–8.054, P < 0.001; (2) triple-vessel CAD: HR = 2.714, 95% CI: 1.362–5.407, P = 0.005; (3) diabetes mellitus: HR = 2.133, 95% CI: 1.094–4.159, P = 0.026; and (4) smoking: HR = 5.085, 95% CI: 1.851–13.965, P = 0.002. Kaplan–Meier survival analysis further demonstrated that patients with a 3v-CT-FFR > 2.5 had significantly longer long-term event-free survival than did those with a 3v-CT-FFR ≤ 2.5, which was supported by the log-rank test ( P < 0.05). These findings indicate that a higher 3v-CT-FFR is associated with a more favorable mid- to long-term prognosis. DISCUSSION This study is the first to incorporate the sum of noninvasive CT-FFR values from the 3v-CT-FFR into the prognostic evaluation of patients with UA. A 3v-CT-FFR ≤ 2.5 was significantly associated with an increased long-term risk of MACCEs, demonstrating considerable predictive accuracy. Compared with single-vessel or anatomical scoring systems, the 3v-CT-FFR offers a more comprehensive representation of global functional imbalance in multivessel disease, thereby enhancing risk stratification for patients with UA. Although CAG is widely regarded as the “gold standard” for diagnosing CAD and is extensively used in minimally invasive interventions, its inherent limitations should not be overlooked. CAG relies on two-dimensional angiographic projections and can display only the longitudinal section of the coronary lumen, making it difficult to reconstruct the three-dimensional spatial course of the vessel. Its assessment of stenosis is susceptible to variations in projection angles and reference vessel selection, and it provides purely anatomical information without quantitative evaluation of physiological function. Systematic reviews and meta-analyses have shown that approximately one-third of patients with visually significant stenosis on CAG do not exhibit evidence of myocardial ischemia; conversely, more than 10% of patients with nonsignificant anatomical narrowing do in fact have functionally significant ischemia [ 15 ] . These findings underscore the inadequacy of relying solely on anatomical stenosis to fully reflect myocardial perfusion status or to guide individualized interventional decisions. With the rapid development of intravascular imaging modalities, such as intravascular ultrasound and optical coherence tomography, and coronary physiological assessment techniques, such as FFR, quantitative flow ratio, and CT-FFR, integrated anatomical and perfusion assessment through multidimensional and multimodal approaches has become an inevitable trend in precision medicine [ 16 – 18 ] . Considering the role of significant myocardial ischemia in the selection of interventional therapy, there is an urgent need to complement routine CCTA or CAG with noninvasive or minimally invasive functional assessments, thereby facilitating more precise and individualized treatment strategies. In current clinical practice, patients with CAD often present with multivessel involvement. Studies focusing on a single vessel are insufficient for comprehensively assessing the overall ischemic burden and its impact on MACCEs. Most previous CT-FFR research has concentrated on the functional evaluation of individual lesions or has explored prognostic implications using invasive 3v-FFR. However, several studies have demonstrated that quantifying overall lesion burden using the sum of FFR values from all three major coronary arteries ( i.e. , 3v-FFR) can effectively distinguish between high- and low-risk populations for long-term adverse cardiovascular events. This prognostic difference is attributed primarily to the interplay between severely ischemic vessels and functionally preserved vessels [ 10 , 19 , 20 ] . Despite its clinical value, the wider use of the FFR is limited because of the procedural risks associated with adenosine administration and pressure wire manipulation. Recently, a multicenter study validated the prognostic significance of the three-vessel contrast-flow-based quantitative flow ratio (3V-cQFR) in a cohort of 549 patients with stable CAD. The results indicated that a lower 3V-cQFR was significantly associated with a greater incidence of MACCEs during the 2.2-year follow-up period. Multivariate analysis identified 3V-cQFR, high-sensitivity cardiac troponin I, and a history of myocardial infarction as independent predictors of MACCEs. In contrast, traditional angiographic scoring systems did not demonstrate significant predictive power. The study confirmed that calculating 3V-cQFR from routine invasive CAG is not only feasible but also offers superior risk stratification capability compared with conventional angiographic assessment [ 21 ] . Nonetheless, the application of 3V-cQFR in high-risk outpatient noninvasive screening remains challenging. Building on the aforementioned findings, the global functional status of the coronary arteries has emerged as a key indicator of overall prognosis in patients with CAD, offering a robust physiological foundation for developing individualized treatment strategies. This study employed the deep learning–based CT-FFR algorithm DeepVESSEL-FFR to enable rapid, noninvasive global function assessment of the three major coronary arteries. The results indicated high concordance with invasive 3v-FFR while eliminating the need for adenosine-induced hyperemia or guidewire manipulation. This approach significantly broadens the clinical potential of functional assessment in UA management. In this study, patients with newly diagnosed UA were included to investigate the association between a 3v-CT-FFR ≤ 2.5 and the occurrence of MACCEs. The results revealed that a 3v-CT-FFR ≤ 2.5 serves as a significant independent risk factor, indicating high sensitivity and specificity in predicting MACCEs. Further analysis revealed that patients with a 3v-CT-FFR ≤ 2.5 had a significantly greater incidence of MACCEs than did those with a 3v-CT-FFR > 2.5 (69.8% vs. 43.0%, P = 0.003). Notably, all deaths recorded in this study occurred in patients with a 3v-CT-FFR ≤ 2.5. Among the 79 patients with a 3v-CT-FFR ≤ 2.5, 59 underwent PCI, and their survival time was significantly longer than that of those who received conservative treatment ( P 2.5 among the 62 patients who did not undergo PCI, the incidence of MACCEs remained significantly greater in the 3v-CT-FFR ≤ 2.5 group. These findings suggest that further clinical intervention should be considered for patients with a 3v-CT-FFR ≤ 2.5. In addition, a total of 60 patients (38.2%) in this study had triple vessel disease. Among those without triple vessel involvement, a substantial proportion had double-vessel disease. Even after stent implantation in a single vessel, patients may still experience MACCEs, indicating that relying solely on a single-vessel CT-FFR value 2.5 is associated with a significantly reduced risk of adverse cardiovascular events, its application in clinical practice should still be weighed against the patient’s overall condition, prognostic factors, and individualized treatment strategy. Therefore, a 3v-CT-FFR ≤ 2.5, as a novel functional assessment parameter, holds considerable clinical value in guiding individualized treatment and prognostic management for patients with CAD. Although coronary obstruction in patients with UA is significantly associated with the risk of developing MACCEs [ 22 ] , anatomical stenosis does not always correlate with actual hemodynamic impairment. The use of 3v-CT-FFR values derived from CCTA allows for noninvasive and quantitative reconstruction of coronary hemodynamics, effectively addressing the limitations of purely anatomical assessments. Clinically, combining 3v-CT-FFR with individual patient characteristics enables the precise identification of high-risk populations and helps determine the optimal timing for safely deferring or avoiding invasive CAG, thereby reducing healthcare costs and procedural complications. Consequently, for patients with newly diagnosed UA, an abnormal 3v-CT-FFR (≤ 2.5) detected on initial CCTA should prompt timely consideration of further functional validation or interventional therapy. LIMITATIONS First, this study adopted a retrospective cohort design, which may be subject to potential selection bias. Additionally, this study was conducted at a single center; therefore, the predictive value of the 3v-CT-FFR requires validation in multicenter, large-sample, prospective cohorts. Second, the CT-FFR measurements were based solely on the deep learning algorithm DeepVESSEL-FFR, without simultaneous comparisons with invasive indicators such as the wire-based FFR or the quantitative flow ratio. Thus, measurement discrepancies cannot be ruled out. Future studies should incorporate concurrent invasive FFR or hemodynamic modeling data to assess the accuracy and consistency of the CT-FFR and 3v-CT-FFR. Third, the 3v-CT-FFR was measured only once during CCTA and was not dynamically monitored during follow-up. Given that cardiac status and plaque characteristics may change over time or in response to pharmacological or interventional treatment, future studies may consider serial measurements or pre- and postintervention comparisons to explore dynamic trends and their prognostic implications. Fourth, this study did not incorporate multimodal parameters such as plaque composition, inflammation, or wall shear stress. Our focus was on functional assessment using the CT-FFR, without high-resolution plaque characterization using intravascular ultrasound or optical coherence tomography, limiting the development of a deeper understanding of the underlying pathophysiological mechanisms. Finally, patients with atrial fibrillation, arrhythmias, or other nonsinus rhythms were excluded; thus, the applicability of the 3v-CT-FFR in these high-risk populations remains unclear. Moreover, this cohort included only patients with UA and did not include those with stable or acute myocardial infarction. Therefore, further investigation is needed before generalizing these findings to patients with stable or acute myocardial infarction. CONCLUSION As a noninvasive and quantitative indicator of global coronary functional status, a summed 3v-CT-FFR ≤ 2.5 can be used to independently predict the long-term risk of MACCEs in patients with UA. This parameter provides a valuable basis for individualized interventional decision-making. Future multicenter, prospective studies are needed to validate its predictive utility and to explore its applicability across different subgroups of CAD patients, thereby advancing precision cardiovascular imaging and therapy. Declarations Acknowledgements Funding This work was supported by Clinical research transformation project ofAnhui Province (202304295107020086) and Key Project of NaturaScience Research of the Anhui Provincial Department of Education(2022AH051477). Availability of data and materials Individual participant data that underlie the results reported in thisarticle, after de-identification can be obtained from the corresponding author upon reasonable request. Compliance with Ethical Standards Guarantor： The scientific guarantor of this publication is Hongju Wang. Conflict of Interest The authors of this manuscript declare no relationships with anycompanies, whose products or services may be related to the subjectmatter of the article. Informed Consent Written informed consent was obtained from all subjects (patients) in this study. Ethical Approval This study was approved by the Ethics Committee of the First Affiliated Hospital of Bengbu Medical University ([2023]KY046). Study subjects or cohorts overlap None of. Authors contributions Zhuoya Yao conceived and performed the study. Jun Wang,Miaonan Li and Hongju Wang participated in the design of the study and performed the clinical study. Yao Li and Chuan Jin wrote the manuscript and analysis and interpretation of the data.All authors agree to be accountable for all aspects of the work References Mensah GA, Fuster V, Murray C, Roth GA. Global Burden of Cardiovascular Diseases and Risks Collaborators. Global Burden of Cardiovascular Diseases and Risks, 1990–2022. J Am Coll Cardiol. 2023;82(25):2350–473. Byrne RA, Rossello X, Coughlan JJ, et al. 2023 ESC Guidelines for the management of acute coronary syndromes. Eur Heart J. 2023;44(38):3720–826. Tang L, Wu M, Xu Y, et al. Multimodal data-driven prognostic model for predicting new-onset ST-elevation myocardial infarction following emergency percutaneous coronary intervention. Inflamm Res. 2023;72(9):1799–809. Wang J, Wang Y, Duan S, et al. 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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-9147746","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":617509881,"identity":"1c897bd7-203a-45d8-b945-1ea8057f6792","order_by":0,"name":"yao li","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"yao","middleName":"","lastName":"li","suffix":""},{"id":617509882,"identity":"043a9fa3-441a-4cc8-8dfe-a94d2a975efb","order_by":1,"name":"jun wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"jun","middleName":"","lastName":"wang","suffix":""},{"id":617509883,"identity":"6a8ecfbe-2026-4ae6-9c0a-eda0a2be0b0f","order_by":2,"name":"zhuoya yao","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"zhuoya","middleName":"","lastName":"yao","suffix":""},{"id":617509884,"identity":"fd646ab6-11a3-4777-81e1-d659a2725331","order_by":3,"name":"chuan jin","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"chuan","middleName":"","lastName":"jin","suffix":""},{"id":617509885,"identity":"00fb7471-c14d-4877-bf25-d8d407732d97","order_by":4,"name":"miaonan li","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"miaonan","middleName":"","lastName":"li","suffix":""},{"id":617509886,"identity":"49281d12-1245-4051-8dd9-059e6f94aae6","order_by":5,"name":"hongju wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIie3RMQrCQBCF4QVhYjGkXomsJxACgRAbwZvsGEgVsbUMCGuTI+QWgvVWqTxASoOQKoJ26TSlVlk7wf37D2Z4jNlsPxg4GV1kBwjOXpsRF/XVr3NXuFhKMyL4qplcQASCp77hYXyUcIkhKZ4+qpYtxTwbIp6KIxklpPB2XBQsDkI9RKZnqiSWpJzNyUOm6TRI+PbOJTxJsbQxJXLdEwhgnIIhQR37lIMALINF4Rv8MjtkVHf9lLPDvq7a3VIMko84Gk7zTr4VNpvN9he9AFrKP+apmBxqAAAAAElFTkSuQmCC","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"hongju","middleName":"","lastName":"wang","suffix":""}],"badges":[],"createdAt":"2026-03-17 10:41:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9147746/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9147746/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106546352,"identity":"b3d01d99-227d-4ec0-a5c3-930fca555bab","added_by":"auto","created_at":"2026-04-09 16:57:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":45093,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered image in the Result section.\u003c/p\u003e","description":"","filename":"unnumber1.png","url":"https://assets-eu.researchsquare.com/files/rs-9147746/v1/e0205d773ab85b17cb879c8a.png"},{"id":106546351,"identity":"87b5f6ec-abe4-4d92-beb0-6ba63a5b8caf","added_by":"auto","created_at":"2026-04-09 16:57:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":55237,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered image in the Result section.\u003c/p\u003e","description":"","filename":"unnumber2.png","url":"https://assets-eu.researchsquare.com/files/rs-9147746/v1/493dce9a8f36e04ad25602c6.png"},{"id":106546353,"identity":"625ee7fc-b0b2-48ca-80c9-6cdebb23ad34","added_by":"auto","created_at":"2026-04-09 16:57:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":29636,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered image in the Result section.\u003c/p\u003e","description":"","filename":"unnumber3.png","url":"https://assets-eu.researchsquare.com/files/rs-9147746/v1/f9c96158f47985f6ce4e5316.png"},{"id":106725925,"identity":"a8bcc37d-a88d-41ac-8760-2d48f71774c7","added_by":"auto","created_at":"2026-04-12 18:34:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1811591,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9147746/v1/e27c65d4-b387-4c76-9534-33537bc6736e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eUtility of the sum of CT-derived fractional flow reserve in three coronary arteries for predicting long-term prognosis in patients with newly diagnosed unstable angina\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eCoronary atherosclerotic heart disease (CAHD) is a leading cause of cardiovascular mortality worldwide. Among acute coronary syndromes, unstable angina (UA) is a particular concern given its rapid progression and high short-term risk of cardiovascular events, making it a key focus for acute intervention and long-term management strategies \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Accurate and noninvasive identification of high-risk patients with CAHD, followed by individualized treatment, is critical for reducing mortality and hospital readmission rates \u003csup\u003e[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTraditional coronary angiography (CAG) and coronary computed tomography angiography (CCTA) can clearly delineate the anatomical severity of stenosis. However, both modalities have limitations in assessing functional ischemia, as anatomical findings alone may not accurately reflect hemodynamic significance. Fractional flow reserve (FFR), an invasive measure based on the ratio of distal to proximal coronary pressure across a stenotic lesion, has been validated as an effective guide for interventional decisions and prognostic improvement when the FFR is \u0026le;\u0026thinsp;0.80 and is regarded as the gold standard for evaluating functional stenosis \u003csup\u003e[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Nevertheless, its clinical application is limited by the use of pressure wires, adenosine administration, and invasive procedures. CT-derived FFR (CT-FFR), which is based on computational fluid dynamics or machine learning algorithms applied to CCTA data, offers a noninvasive option for obtaining three-dimensional hemodynamic information. CT-FFR enables simultaneous anatomical and functional assessment and has shown high diagnostic concordance with invasive FFR \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePatients with UA often present with multivessel coronary artery disease (CAD), yet single-vessel CT-FFR reflects only localized functional status and fails to provide a comprehensive assessment of the overall myocardial ischemic burden. Invasive FFR studies have introduced the concept of the \u0026ldquo;sum of triple vessel FFR\u0026rdquo; (3v-FFR), which allows for an integrated evaluation of global myocardial perfusion risk. Compared with single-vessel FFR, this metric has demonstrated superior predictive value for mid- to long-term major adverse cardiac and cerebrovascular events (MACCEs) in multicenter cohorts \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. However, the prognostic significance of a corresponding CT\u0026ndash;FFR-based triple vessel index in patients with newly diagnosed UA has yet to be systematically investigated.\u003c/p\u003e \u003cp\u003eThis study is the first to use the sum of the CT-FFR values from the left anterior descending artery, left circumflex artery, and right coronary artery\u0026mdash;referred to as triple vessel CT-FFR (3v-CT-FFR)\u0026mdash;to evaluate its predictive performance for mid- to long-term MACCEs in patients with newly diagnosed UA. The 3v-CT-FFR was compared with conventional clinical and imaging parameters to assess its potential as a quantitative tool for noninvasive risk stratification and therapeutic decision-making. We hypothesize that 3v-CT-FFR reflects the functional burden of the total plaque load and may offer significant value in identifying high-risk patients, thereby supporting early intervention or intensified pharmacological intervention.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003e1.1 Study Subjects\u003c/p\u003e\n\u003cp\u003eThis retrospective study included consecutive patients with UA who underwent both CCTA and CAG at the First Affiliated Hospital of Bengbu Medical University between January 2021 and December 2022. The assessment of coronary artery stenosis severity and plaque characteristics via CCTA was based on the 2024 consensus statement by the Asian Society of Cardiovascular Imaging on coronary artery stenosis and plaque evaluation in CCTA, as well as the 2021 expert consensus from the US National Institutes of Health on CCTA\u003csup\u003e\u0026nbsp;[11, 12]\u003c/sup\u003e. The study protocol was reviewed and approved by the Ethics Committee of Bengbu Medical University (Ethics Approval No. [2023]KY046).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.1.1 Inclusion Criteria:\u003c/strong\u003e (1) completion of both CCTA and initial CAG; (2) first-time diagnosis of UA; (3) availability of complete medical history and follow-up data; and (4) follow-up duration ≥ 12 months.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.1.2 Exclusion Criteria:\u0026nbsp;\u003c/strong\u003e(1) acute myocardial infarction within the past week; (2) previously diagnosed CAD based on prior CAG; (3) presence of left main CAD or chronic occlusion on CCTA; (4) severe liver or kidney dysfunction, including aspartate aminotransferase or alanine aminotransferase levels higher than three times the upper limit of normal, liver cirrhosis or other significant hepatic diseases, kidney insufficiency with estimated glomerular filtration rate ≤ 60 mL/min/1.73 m²; (5) acute or decompensated chronic heart failure with New York Heart Association functional class III–IV; (6) other active inflammatory conditions, including acute infections or uncontrolled autoimmune diseases; and (7) incomplete clinical data or poor-quality CCTA images precluding accurate plaque characterization and data analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.2 Follow-up and Clinical Endpoints\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs of September 31, 2024, follow-up data were obtained for all patients through a review of hospital medical records and telephone interviews. The median follow-up duration was 30 months. MACCEs were defined as the occurrence of any of the following: all-cause mortality, cardiac death, myocardial infarction, recurrent angina, in-stent restenosis, in-stent thrombosis, or stroke.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3 Instruments and Methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1.3.1 Blood Tests\u003c/p\u003e\n\u003cp\u003eOn the morning of the day following admission, 5 mL of venous blood was drawn from the cubital vein of each patient into heparinized tubes. The samples were sent to the hospital’s central laboratory for analysis of blood glucose, lipid profile, comprehensive metabolic panel, cardiac enzyme panel, cardiac troponin, and N-terminal pro-B-type natriuretic peptide.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3.2 CAG\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCAG was performed by experienced cardiologists using the Judkins technique. The interpretation of angiographic findings was based on the 2001 guidelines from the American College of Cardiology and American Heart Association for the diagnosis and management of CAD. The severity of coronary artery stenosis in each vessel was quantitatively assessed using the Gensini scoring system. Two experienced cardiologists independently evaluated the angiograms, and the average score was used for analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3.3 CT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCCTA was performed using either a 128-slice spiral CT scanner (iCT, Philips) or a 256-slice spiral CT scanner (Revolution CT, GE Healthcare, USA). Retrospective electrocardiogram-gated scanning was employed, covering the region from 1 cm below the carinal bifurcation to 1 cm below the diaphragm. Iodinated contrast medium was injected at a flow rate of 5.3–5.6 mL/s, with a total volume of 50–70 mL. For iCT scanning, the primary parameters were as follows: tube voltage of 120 kV, tube current of 800 mA, collimator and detector width of 128 mm × 0.625 mm, slice thickness of 0.9 mm, and gantry rotation time of 0.27 s. For Revolution CT, the key parameters were as follows: tube voltage of 120 kV/100 kV, automated tube current modulation, collimator width of 160 mm, and gantry rotation time of 0.28 s. The image slice thickness was set at 0.625 mm. All CCTA datasets were transferred to a postprocessing workstation (ADW 4.7, GE) for analysis. CT-FFR values were calculated using deep learning-based software (DEEPVESSELFFR, Keya Medical, Beijing, China). The software uses a diastolic-phase image of optimal quality to compute CT-FFR values. The core principle involves training a validated model through deep learning that integrates anatomical and hemodynamic features extracted from large-scale vascular data, enabling rapid and accurate computation of the FFR across the entire coronary tree\u003csup\u003e\u0026nbsp;[13]\u003c/sup\u003e. CT-FFR measurements were taken at 1.5–2 cm proximal and distal to the stenotic lesions. In cases of multiple lesions within a single vessel, the value at the most distal lesion was used\u003csup\u003e\u0026nbsp;[14]\u003c/sup\u003e. The 3v-CT-FFR was defined as the sum of the CT-FFR values for the left anterior descending artery, left circumflex artery, and right coronary artery.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.5 Statistical Methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analyses were performed using SPSS27, MedCalc version 20.0.4, and GraphPad Prism 9.0. Normally distributed data are expressed as mean ± standard deviation (\u003cem\u003ex̄\u003c/em\u003e±\u003cem\u003es\u003c/em\u003e), and comparisons between two groups were conducted using independent samples t tests. For nonnormally distributed data, values are presented as medians with interquartile ranges (M [P25, P75]), and comparisons were made using the Mann–Whitney U test. Enumeration data are expressed as counts and percentages (\u003cem\u003en\u0026nbsp;\u003c/em\u003e[%]), and comparisons between groups were performed using the chi-square (χ²) test. Pearson correlation analysis was used to evaluate the relationship between the degree of coronary lumen stenosis and the CT-FFR values. Multivariate Cox regression analysis was employed to identify factors associated with the occurrence of MACCEs. Receiver operating characteristic (ROC) curves were constructed to compare the predictive performance of coronary lumen stenosis and CT-FFR for MACCEs. All probability values were two-sided, and a \u003cem\u003eP\u003c/em\u003e value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.1 ROC Curve Analysis of the 3v-CT-FFR for Predicting MACCEs\u003c/h2\u003e \u003cp\u003eThe ROC curve analysis identified 2.5 as the optimal cutoff value for 3v-CT-FFR in predicting MACCEs, yielding a sensitivity of 81.4%, specificity of 57.0%, and an area under the curve (AUC) of 0.727 (95% confidence interval [CI]: 0.644\u0026ndash;0.810, \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001). On the basis of this cutoff, patients were stratified into a high 3v-CT-FFR group (\u0026gt;\u0026thinsp;2.5, \u003cem\u003en\u0026thinsp;=\u003c/em\u003e\u0026thinsp;75) and a low 3v-CT-FFR group (\u0026le;\u0026thinsp;2.5, \u003cem\u003en\u0026thinsp;=\u003c/em\u003e\u0026thinsp;78).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"7\"\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOptimal cutoff\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYouden index\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3v-CT-FFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.644\u0026ndash;0.810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Comparison of Baseline Characteristics Between the Two Group\u003c/h2\u003e \u003cp\u003eAmong the 157 patients included in this study, 97 (61.8%) were male and 60 (38.2%) were female, with a mean age of 61.71\u0026thinsp;\u0026plusmn;\u0026thinsp;11.32 years. Compared with those in the 3v-CT-FFR\u0026thinsp;\u0026gt;\u0026thinsp;2.5 group, patients in the 3v-CT-FFR\u0026thinsp;\u0026le;\u0026thinsp;2.5 group (\u003cem\u003en\u0026thinsp;=\u003c/em\u003e\u0026thinsp;79) had significantly higher levels of B-type natriuretic peptide (BNP), a greater proportion of patients who underwent percutaneous coronary intervention (PCI), and more frequent postoperative use of aspirin and ticagrelor (all \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among the 79 patients in the 3v-CT-FFR\u0026thinsp;\u0026le;\u0026thinsp;2.5 group, 59 underwent PCI. These patients had significantly longer survival than those who received conservative treatment did (\u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among the 78 patients in the 3v-CT-FFR\u0026thinsp;\u0026gt;\u0026thinsp;2.5 group, 36 underwent PCI, and they also had significantly longer survival than those receiving conservative treatment did (\u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Among the 62 patients who did not undergo PCI, 20 had a 3v-CT-FFR\u0026thinsp;\u0026le;\u0026thinsp;2.5. Although survival time did not differ significantly between the low- and high-3v-CT-FFR subgroups (\u003cem\u003eP\u0026thinsp;\u0026gt;\u003c/em\u003e\u0026thinsp;0.05), the incidence of MACCEs was significantly greater in the \u0026le;\u0026thinsp;2.5 group (\u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Among the 95 patients who underwent PCI, 59 had a 3v-CT-FFR\u0026thinsp;\u0026le;\u0026thinsp;2.5. There were no statistically significant differences in survival time or MACCEs incidence between the \u0026le;\u0026thinsp;2.5 and \u0026gt;\u0026thinsp;2.5 groups in this subgroup (\u003cem\u003eP\u0026thinsp;\u0026gt;\u003c/em\u003e\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePatients were further categorized on the basis of the occurrence of MACCEs into the MACCEs group (\u003cem\u003en\u0026thinsp;=\u003c/em\u003e\u0026thinsp;43) and the non-MACCEs group (\u003cem\u003en\u0026thinsp;=\u003c/em\u003e\u0026thinsp;114). Compared with the non-MACCEs group, patients in the MACCEs group had significantly greater proportions of males, individuals with diabetes, and smokers. Additionally, the levels of serum creatinine and BNP were significantly elevated, whereas high-density lipoprotein levels and survival time were significantly lower (all \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05). No significant differences were observed between the two groups in terms of age, hypertension status, postoperative aspirin use, or triglyceride levels (all \u003cem\u003eP\u0026thinsp;\u0026gt;\u003c/em\u003e\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Comparison of Coronary Lesions between the MACCEs and Non-MACCEs Groups\u003c/h2\u003e \u003cp\u003eAmong the 157 patients, 60 (38.2%) had triple-vessel CAD. The median 3v-CT-FFR across the cohort was 2.50 (2.29, 2.64), and the median Gensini score was 32.00 (14.00, 52.75). Compared with the non-MACCEs group, the MACCEs group had a significantly greater proportion of patients with triple vessel disease, lower 3v-CT-FFR values, and higher Gensini scores (all \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\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\u003eComparison of baseline characteristics between the two groups\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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge (year)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3v-CT-FFR\u0026thinsp;\u0026le;\u0026thinsp;2.5 (n\u0026thinsp;=\u0026thinsp;79)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3v-CT-FFR\u0026gt;2.5 (n\u0026thinsp;=\u0026thinsp;78)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.76\u0026thinsp;\u0026plusmn;\u0026thinsp;11.30\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.53\u0026thinsp;\u0026plusmn;\u0026thinsp;12.26\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.99\u0026thinsp;\u0026plusmn;\u0026thinsp;10.30\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e97 (61.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51 (64.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46 (59.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.475\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e91 (58.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49 (62.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42 (53.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40 (25.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17 (21.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23 (29.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrial fibrillation (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9 (5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (6.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4 (5.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebral infarction (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30 (19.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15 (19.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15 (19.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.969\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking history (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e84 (53.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40 (50.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44 (56.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.468\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspirin (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e143 (91.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77 (97.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66 (84.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClopidogrel (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51 (32.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25 (31.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTicagrelor (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77 (49.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48 (60.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29 (37.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatins (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e154 (98.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77 (97.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77 (98.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARNI (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e76 (48.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40 (50.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36 (46.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.574\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurvival time(day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e938.00 (692.00, 1198.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e997.00 (415.00, 1225.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e932.50 (759.50, 1174.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.733\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCI (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95 (60.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59 (74.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36 (46.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.35 (4.42, 6.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.28 (4.38, 6.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.30 (4.69, 6.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid(umol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e305.43\u0026thinsp;\u0026plusmn;\u0026thinsp;84.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e302.79\u0026thinsp;\u0026plusmn;\u0026thinsp;86.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e308.07\u0026thinsp;\u0026plusmn;\u0026thinsp;82.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine(umol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68.00 (59.00, 75.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67.00 (59.00, 74.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e67.50 (59.00, 76.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.95 (3.16, 4.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.67 (3.04, 4.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.95 (3.39, 4.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.579\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.42 (0.96, 1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.32 (1.02, 1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.37 (0.89, 2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.968\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-density lipoprotein(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.31 (1.72, 2.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.04 (1.57, 2.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.20 (1.77, 2.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-density lipoprotein(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02 (0.86, 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.91 (0.82, 1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.01 (0.85, 1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLipoprotein a(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e205.00 (104.50, 331.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e184.00 (88.75, 353.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e145.50 (76.00, 397.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.963\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN-terminal pro-B-type-natriuretic peptide(pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e115.50 (43.70, 241.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79.30 (51.30, 172.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.20 (32.15, 127.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatine kinase(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e87.50 (53.00, 93.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75.50 (49.75, 95.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64.00 (44.00, 111.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMACCEs (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43 (27.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30 (38.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\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\u003ePCI \u003cem\u003evs.\u003c/em\u003e non-PCI outcomes in patients with a 3v-CT-FFR\u0026thinsp;\u0026le;\u0026thinsp;2.5\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge (year)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3v-CT-FFR\u0026thinsp;\u0026le;\u0026thinsp;2.5\u0026thinsp;+\u0026thinsp;PCI (\u003cem\u003en\u0026thinsp;=\u003c/em\u003e\u0026thinsp;59)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3v-CT-FFR\u0026thinsp;\u0026le;\u0026thinsp;2.5\u0026thinsp;+\u0026thinsp;non-PCI (n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.53\u0026thinsp;\u0026plusmn;\u0026thinsp;12.26\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.49\u0026thinsp;\u0026plusmn;\u0026thinsp;11.39\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61.65\u0026thinsp;\u0026plusmn;\u0026thinsp;14.88\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.961\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51 (64.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36 (61.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15 (75.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49 (62.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39 (66.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17 (21.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13 (22.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4 (20.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrial fibrillation (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (6.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3 (5.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2 (10.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebral infarction (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15 (19.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13 (22.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2 (10.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.392\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking history (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40 (50.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27 (45.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13 (65.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspirin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77(97.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58(98.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19(95.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTicagrelor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48(60.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40(67.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8(40.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClopidogrel (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25 (31.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19 (32.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6 (30.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatins (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77 (97.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57 (96.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARNI (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40 (50.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30 (50.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.28 (4.38, 6.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.42 (4.38, 6.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.67 (4.38, 5.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.656\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid(umol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e302.79\u0026thinsp;\u0026plusmn;\u0026thinsp;86.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e298.86\u0026thinsp;\u0026plusmn;\u0026thinsp;83.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e315.44\u0026thinsp;\u0026plusmn;\u0026thinsp;98.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.481\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine(umol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67.00 (59.00, 74.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69.00 (65.00, 92.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e67.00 (59.00, 71.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.293\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.67 (3.04, 4.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.68 (3.62, 4.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.60 (3.02, 4.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.332\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.32 (1.02, 1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.48 (0.98, 2.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.30 (1.03, 1.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.856\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-density lipoprotein(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.04 (1.57, 2.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.15 (1.83, 2.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.97 (1.54, 2.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.261\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-density lipoprotein(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.91 (0.82, 1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.93 (0.84, 1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.89 (0.81, 1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.345\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLipoprotein a(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e184.00 (88.75, 353.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e138.00 (53.00, 372.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e192.00 (100.00, 350.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.681\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN-terminal pro-B-type-natriuretic peptide(pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79.30 (51.30, 172.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e131.90 (54.80, 361.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e75.60 (50.40, 169.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatine kinase(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75.50 (49.75, 95.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90.00 (73.00, 184.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65.00 (46.00, 95.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurvival time(day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e997.00(415.00, 1225.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1080.50(775.00, 1276.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e868.00(724.35, 990.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMACCEs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30(38.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21(35.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9(45.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.454\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\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\u003ePCI \u003cem\u003evs.\u003c/em\u003e non-PCI outcomes in patients with a 3v-CT-FFR\u0026thinsp;\u0026gt;\u0026thinsp;2.5\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=\"char\" char=\".\" 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=\"char\" char=\".\" 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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge (year)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3v-CT-FFR\u0026thinsp;\u0026gt;\u0026thinsp;2.5\u0026thinsp;+\u0026thinsp;PCI (n\u0026thinsp;=\u0026thinsp;36)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3v-CT-FFR\u0026thinsp;\u0026gt;\u0026thinsp;2.5\u0026thinsp;+\u0026thinsp;non-PCI (n\u0026thinsp;=\u0026thinsp;42)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.99\u0026thinsp;\u0026plusmn;\u0026thinsp;10.30\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.00\u0026thinsp;\u0026plusmn;\u0026thinsp;11.79\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.98\u0026thinsp;\u0026plusmn;\u0026thinsp;8.98\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46 (59.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (55.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26 (61.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.570\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42 (53.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (58.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.462\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23 (29.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (22.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15 (35.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrial fibrillation (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4 (5.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.722\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebral infarction (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15 (19.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9 (21.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking history (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44 (56.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26 (61.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.291\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspirin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66 (84.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30 (71.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTicagrelor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29 (37.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (61.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClopidogrel (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (38.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12 (28.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.335\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatins (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77 (98.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41 (97.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARNI (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36 (46.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (44.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20 (47.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.779\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.13 (4.43, 5.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.49 (4.31, 6.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.24 (4.76, 6.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.696\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid(umol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e308.07\u0026thinsp;\u0026plusmn;\u0026thinsp;82.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e298.81\u0026thinsp;\u0026plusmn;\u0026thinsp;88.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e316.40\u0026thinsp;\u0026plusmn;\u0026thinsp;77.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.610\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine(umol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68.00 (59.00, 76.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.00 (57.50, 82.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e67.50 (62.50, 74.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.88 (3.22, 4.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.76 (3.17, 5.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.95 (3.39, 4.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.736\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.37 (0.87, 1.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.18 (0.85, 2.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.43 (0.91, 1.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.881\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-density lipoprotein(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.20 (1.70, 2.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.36 (1.49, 3.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.05 (1.79, 2.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-density lipoprotein(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.87, 1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95 (0.83, 1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.08 (0.87, 1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.337\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLipoprotein a(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e172.50 (93.50, 396.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e282.00 (115.50, 413.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e110.00 (56.00, 240.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN-terminal pro-B-type-natriuretic peptide(pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46.55 (33.20, 133.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.00 (33.00, 153.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.35 (27.78, 97.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.282\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatine kinase(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64.00 (44.00, 111.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.00 (39.00, 67.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e89.00 (52.25, 118.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurvival time(day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e932.50 (759.50, 1174.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1080.50 (775.00, 1276.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e868.00 (724.35, 990.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMACCEs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (19.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6 (14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.542\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eOutcomes in non-PCI patients by 3v-CT-FFR value\u003c/b\u003e\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=\"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=\"char\" char=\".\" 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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge (year)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-PCI\u0026thinsp;+\u0026thinsp;3v-CT-FFR\u0026thinsp;\u0026gt;\u0026thinsp;2.5 (\u003cem\u003en\u0026thinsp;=\u003c/em\u003e\u0026thinsp;42)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-PCI\u0026thinsp;+\u0026thinsp;3v-CT-FFR\u0026thinsp;\u0026le;\u0026thinsp;2.5 (\u003cem\u003en\u0026thinsp;=\u003c/em\u003e\u0026thinsp;20)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.87\u0026thinsp;\u0026plusmn;\u0026thinsp;11.10\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.98\u0026thinsp;\u0026plusmn;\u0026thinsp;8.98\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.65\u0026thinsp;\u0026plusmn;\u0026thinsp;14.88\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.915\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41 (66.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26 (61.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15 (75.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21(50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19 (30.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15 (35.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4 (20.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.210\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrial fibrillation (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5 (8.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2 (10.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebral infarction (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11 (17.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9 (21.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2 (10.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.456\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking history (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39 (62.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26 (61.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13 (65.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspirin (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49 (79.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30 (71.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19 (95.0%)\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\u003eClopidogrel (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18 (29.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12 (28.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6 (30.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTicagrelor (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15 (24.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8 (40.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatins (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61 (98.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41 (97.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARNI (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30 (48.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20 (47.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.16(4.63, 6.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.24 (4.76, 6.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.67(4.38, 5.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.585\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid(umol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e316.10.\u0026plusmn;82.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e316.40\u0026thinsp;\u0026plusmn;\u0026thinsp;77.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e315.44\u0026thinsp;\u0026plusmn;\u0026thinsp;98.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.968\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine(umol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68.00 (64.50, 77.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67.50 (62.50, 74.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69.00 (65.00, 92.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.827\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.86 (3.43, 4.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.95 (3.39, 4.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3. 68(3.62, 4.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.43(0.96, 1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.43 (0.91, 1.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.48 (0.98, 2.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.925\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-density lipoprotein(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.08 (1.82, 2.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.05 (1.79, 2.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.15 (1.83, 2.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.353\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-density lipoprotein(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00(0.85, 1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.94 (0.83, 1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.89 (0.81, 1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.971\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLipoprotein a(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e206.00 (101.00, 394.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e110.00 (56.00, 240.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e138.00 (53.00, 372.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.649\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN-terminal pro-B-type-natriuretic peptide(pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e113.00 (56.00, 268.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42.35 (27.78, 97.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e131.90 (54.80, 361.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatine kinase(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90.00 (58.50, 123.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89.00 (52.25, 118.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90.00 (73.00, 184.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.355\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurvival time(day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e844.50 (679.00, 1009.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e840.36\u0026thinsp;\u0026plusmn;\u0026thinsp;244.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e680.90\u0026thinsp;\u0026plusmn;\u0026thinsp;423.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMACCEs (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15 (24.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6 (14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9 (45.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOutcomes in PCI-treated patients by 3v-CT-FFR value\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=\"char\" char=\".\" 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=\"char\" char=\".\" 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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge (year)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePCI\u0026thinsp;+\u0026thinsp;3v-CT-FFR\u0026thinsp;\u0026gt;\u0026thinsp;2.5 (\u003cem\u003en\u0026thinsp;=\u003c/em\u003e\u0026thinsp;36)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePCI\u0026thinsp;+\u0026thinsp;3v-CT-FFR\u0026thinsp;\u0026le;\u0026thinsp;2.5 (\u003cem\u003en\u0026thinsp;=\u003c/em\u003e\u0026thinsp;59)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.68\u0026thinsp;\u0026plusmn;\u0026thinsp;11.48\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.00(51.00, 67.75)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65.00(53.00, 71.00)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.966\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56 (58.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (55.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36 (61.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60 (63.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21(58.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39 (66.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.446\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21 (22.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (22.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13 (22.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrial fibrillation (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4 (4.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3 (5.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebral infarction (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19 (20.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13 (22.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.526\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking history (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45 (47.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27 (45.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.688\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspirin (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e94 (98.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58 (98.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.432\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClopidogrel (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33 (34.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (38.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19 (32.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.507\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTicagrelor (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62 (65.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (61.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40 (67.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.507\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatins (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93 (97.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57 (96.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.704\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARNI (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46 (48.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (50.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16 (44.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.545\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.49(4.31, 6.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.51 (4.30, 6.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.42 (4.38, 6.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid(umol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e298.84\u0026thinsp;\u0026plusmn;\u0026thinsp;84.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e298.81\u0026thinsp;\u0026plusmn;\u0026thinsp;88.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e298.86\u0026thinsp;\u0026plusmn;\u0026thinsp;83.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.672\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine(umol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67.00 (59.00, 75.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.00 (58.25, 83.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e67.00 (59.00, 71.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.70 (3.03, 4.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.92 (3.28, 5.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3. 02(3.60, 4.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.23(0.97, 1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.84 (1.20, 2.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.30 (1.03, 1.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-density lipoprotein(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.04 (1.57, 3.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.51 (1.62, 3.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.97 (1.54, 2.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.425\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-density lipoprotein(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.92(0.81, 1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94 (0.83, 1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.89 (0.81, 1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.542\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLipoprotein a(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e206.00 (101.00, 394.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e280.00 (113.25, 416.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e192.00 (100.00, 350.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN-terminal pro-B-type-natriuretic peptide(pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68.50 (37.20, 162.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.35 (32.65, 133.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75.60 (50.40, 169.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.372\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatine kinase(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61.00 (43.00, 82.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.00 (37.50, 70.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.00 (46.00, 95.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMACCEs (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43 (27.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (38.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurvival time(day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1117.00 (711.00, 1266.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1080.50 (775.00, 1276.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1140.00 (452.00, 1256.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.432\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of clinical characteristics between the MACCEs and non-MACCEs groups\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=\"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=\"char\" char=\".\" 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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge (year)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e总计\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMACCEs(n\u0026thinsp;=\u0026thinsp;43)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon- MACCEs(n\u0026thinsp;=\u0026thinsp;114)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.71\u0026thinsp;\u0026plusmn;\u0026thinsp;11.32\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.95\u0026thinsp;\u0026plusmn;\u0026thinsp;11.64\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.31\u0026thinsp;\u0026plusmn;\u0026thinsp;11.19\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e97 (61.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32 (74.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65 (57.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e91 (58.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24 (55.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e67 (58.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40 (25.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23 (53.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17 (14.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrial fibrillation (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9 (5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (7.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.978\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebral infarction (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30 (19.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10 (23.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20 (17.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking history (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e84 (53.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30 (69.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54 (47.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspirin (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e143 (91.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41 (95.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e102 (89.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.402\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClopidogrel (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51 (32.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18 (41.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33 (28.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTicagrelor (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77 (49.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22 (51.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55 (48.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatins (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e154 (98.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42 (97.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e112 (98.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARNI (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e76 (48.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22 (51.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54 (47.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.671\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurvival time(day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.00 (22.00, 38.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e302.00 (149.00, 517.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1082.50 (860.75, 1242.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCI (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95 (60.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28 (65.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e67 (58.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.468\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.35 (4.42, 6.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.97 (4.38, 6.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.38 (4.43, 6.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.502\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric Acid(umol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e295.00 (247.50, 345.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e300.00 (267.00, 367.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e294.00 (242.00, 343.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine(umol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68.00 (59.00, 75.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70.00 (66.00, 81.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66.00 (57.50, 72.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.95 (3.16, 4.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.94 (3.16, 4.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.99 (3.17, 4.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.364\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.42 (0.96, 1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.32 (0.87, 1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.43 (0.97, 1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.585\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-density lipoprotein(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.31 (1.72, 2.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.39\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.672\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-density lipoprotein(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02 (0.86, 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLipoprotein a(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e205.00 (104.50, 331.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e229.00 (98.00, 422.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e204.00 (105.00, 282.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.462\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN-terminal pro-B-type-natriuretic peptide(pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e115.50 (43.70, 241.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e148.00 (65.10, 241.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e103.00 (37.05, 241.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatine kinase(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e87.50 (53.00, 93.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90.90 (58.00, 95.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e79.00 (52.00, 90.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of coronary lesion characteristics between the MACCEs and non-MACCEs groups\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=\"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=\"char\" char=\".\" 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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3v-CT-FFR\u0026thinsp;\u0026le;\u0026thinsp;2.5 (%, n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMACCEs group (\u003cem\u003en\u0026thinsp;=\u003c/em\u003e\u0026thinsp;43)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-MACCEs group (\u003cem\u003en\u0026thinsp;=\u003c/em\u003e\u0026thinsp;114)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79 (50.3%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (69.8%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49 (43.0%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriple vessel CAD (%, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60 (38.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28 (65.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32 (28.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e97.73\u0026thinsp;\u0026plusmn;\u0026thinsp;22.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99.33\u0026thinsp;\u0026plusmn;\u0026thinsp;24.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97.12\u0026thinsp;\u0026plusmn;\u0026thinsp;21.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.581\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGensini\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.00 (14.00, 52.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42.00 (20.00, 73.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.00 (12.00, 46.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Survival Analysis and Multivariate Cox Regression Analysis\u003c/h2\u003e \u003cp\u003eTo evaluate the predictors of MACCEs, survival analysis and multivariate Cox proportional hazards regression analysis were performed. The occurrence of MACCEs was used as the dependent variable (coded as 1 for event occurrence and 0 for no event), and the following variables were included as independent factors: presence of diabetes mellitus, smoking status, serum creatinine level, number of diseased coronary vessels, and 3v-CT-FFR value. The results identified the following as independent predictors of MACCEs: (1) 3v-CT-FFR\u0026thinsp;\u0026le;\u0026thinsp;2.5: hazard ratio (HR)\u0026thinsp;=\u0026thinsp;4.121, 95% CI: 2.108\u0026ndash;8.054, \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001; (2) triple-vessel CAD: HR\u0026thinsp;=\u0026thinsp;2.714, 95% CI: 1.362\u0026ndash;5.407, \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.005; (3) diabetes mellitus: HR\u0026thinsp;=\u0026thinsp;2.133, 95% CI: 1.094\u0026ndash;4.159, \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.026; and (4) smoking: HR\u0026thinsp;=\u0026thinsp;5.085, 95% CI: 1.851\u0026ndash;13.965, \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.002. Kaplan\u0026ndash;Meier survival analysis further demonstrated that patients with a 3v-CT-FFR\u0026thinsp;\u0026gt;\u0026thinsp;2.5 had significantly longer long-term event-free survival than did those with a 3v-CT-FFR\u0026thinsp;\u0026le;\u0026thinsp;2.5, which was supported by the log-rank test (\u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05). These findings indicate that a higher 3v-CT-FFR is associated with a more favorable mid- to long-term prognosis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study is the first to incorporate the sum of noninvasive CT-FFR values from the 3v-CT-FFR into the prognostic evaluation of patients with UA. A 3v-CT-FFR\u0026thinsp;\u0026le;\u0026thinsp;2.5 was significantly associated with an increased long-term risk of MACCEs, demonstrating considerable predictive accuracy. Compared with single-vessel or anatomical scoring systems, the 3v-CT-FFR offers a more comprehensive representation of global functional imbalance in multivessel disease, thereby enhancing risk stratification for patients with UA.\u003c/p\u003e \u003cp\u003eAlthough CAG is widely regarded as the \u0026ldquo;gold standard\u0026rdquo; for diagnosing CAD and is extensively used in minimally invasive interventions, its inherent limitations should not be overlooked. CAG relies on two-dimensional angiographic projections and can display only the longitudinal section of the coronary lumen, making it difficult to reconstruct the three-dimensional spatial course of the vessel. Its assessment of stenosis is susceptible to variations in projection angles and reference vessel selection, and it provides purely anatomical information without quantitative evaluation of physiological function. Systematic reviews and meta-analyses have shown that approximately one-third of patients with visually significant stenosis on CAG do not exhibit evidence of myocardial ischemia; conversely, more than 10% of patients with nonsignificant anatomical narrowing do in fact have functionally significant ischemia \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. These findings underscore the inadequacy of relying solely on anatomical stenosis to fully reflect myocardial perfusion status or to guide individualized interventional decisions. With the rapid development of intravascular imaging modalities, such as intravascular ultrasound and optical coherence tomography, and coronary physiological assessment techniques, such as FFR, quantitative flow ratio, and CT-FFR, integrated anatomical and perfusion assessment through multidimensional and multimodal approaches has become an inevitable trend in precision medicine \u003csup\u003e[\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Considering the role of significant myocardial ischemia in the selection of interventional therapy, there is an urgent need to complement routine CCTA or CAG with noninvasive or minimally invasive functional assessments, thereby facilitating more precise and individualized treatment strategies.\u003c/p\u003e \u003cp\u003eIn current clinical practice, patients with CAD often present with multivessel involvement. Studies focusing on a single vessel are insufficient for comprehensively assessing the overall ischemic burden and its impact on MACCEs. Most previous CT-FFR research has concentrated on the functional evaluation of individual lesions or has explored prognostic implications using invasive 3v-FFR. However, several studies have demonstrated that quantifying overall lesion burden using the sum of FFR values from all three major coronary arteries (\u003cem\u003ei.e.\u003c/em\u003e, 3v-FFR) can effectively distinguish between high- and low-risk populations for long-term adverse cardiovascular events. This prognostic difference is attributed primarily to the interplay between severely ischemic vessels and functionally preserved vessels \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Despite its clinical value, the wider use of the FFR is limited because of the procedural risks associated with adenosine administration and pressure wire manipulation. Recently, a multicenter study validated the prognostic significance of the three-vessel contrast-flow-based quantitative flow ratio (3V-cQFR) in a cohort of 549 patients with stable CAD. The results indicated that a lower 3V-cQFR was significantly associated with a greater incidence of MACCEs during the 2.2-year follow-up period. Multivariate analysis identified 3V-cQFR, high-sensitivity cardiac troponin I, and a history of myocardial infarction as independent predictors of MACCEs. In contrast, traditional angiographic scoring systems did not demonstrate significant predictive power. The study confirmed that calculating 3V-cQFR from routine invasive CAG is not only feasible but also offers superior risk stratification capability compared with conventional angiographic assessment \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Nonetheless, the application of 3V-cQFR in high-risk outpatient noninvasive screening remains challenging. Building on the aforementioned findings, the global functional status of the coronary arteries has emerged as a key indicator of overall prognosis in patients with CAD, offering a robust physiological foundation for developing individualized treatment strategies. This study employed the deep learning\u0026ndash;based CT-FFR algorithm DeepVESSEL-FFR to enable rapid, noninvasive global function assessment of the three major coronary arteries. The results indicated high concordance with invasive 3v-FFR while eliminating the need for adenosine-induced hyperemia or guidewire manipulation. This approach significantly broadens the clinical potential of functional assessment in UA management.\u003c/p\u003e \u003cp\u003eIn this study, patients with newly diagnosed UA were included to investigate the association between a 3v-CT-FFR\u0026thinsp;\u0026le;\u0026thinsp;2.5 and the occurrence of MACCEs. The results revealed that a 3v-CT-FFR\u0026thinsp;\u0026le;\u0026thinsp;2.5 serves as a significant independent risk factor, indicating high sensitivity and specificity in predicting MACCEs. Further analysis revealed that patients with a 3v-CT-FFR\u0026thinsp;\u0026le;\u0026thinsp;2.5 had a significantly greater incidence of MACCEs than did those with a 3v-CT-FFR\u0026thinsp;\u0026gt;\u0026thinsp;2.5 (69.8% \u003cem\u003evs.\u003c/em\u003e 43.0%, \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.003). Notably, all deaths recorded in this study occurred in patients with a 3v-CT-FFR\u0026thinsp;\u0026le;\u0026thinsp;2.5. Among the 79 patients with a 3v-CT-FFR\u0026thinsp;\u0026le;\u0026thinsp;2.5, 59 underwent PCI, and their survival time was significantly longer than that of those who received conservative treatment (\u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05). Although there was no statistically significant difference in survival time between patients with a 3v-CT-FFR\u0026thinsp;\u0026le;\u0026thinsp;2.5 and those with a 3v-CT-FFR\u0026thinsp;\u0026gt;\u0026thinsp;2.5 among the 62 patients who did not undergo PCI, the incidence of MACCEs remained significantly greater in the 3v-CT-FFR\u0026thinsp;\u0026le;\u0026thinsp;2.5 group. These findings suggest that further clinical intervention should be considered for patients with a 3v-CT-FFR\u0026thinsp;\u0026le;\u0026thinsp;2.5. In addition, a total of 60 patients (38.2%) in this study had triple vessel disease. Among those without triple vessel involvement, a substantial proportion had double-vessel disease. Even after stent implantation in a single vessel, patients may still experience MACCEs, indicating that relying solely on a single-vessel CT-FFR value\u0026thinsp;\u0026lt;\u0026thinsp;0.8 for clinical decision-making is insufficient. This highlights the necessity of conducting a more comprehensive evaluation that accounts for multivessel disease. Notably, while a 3v-CT-FFR\u0026thinsp;\u0026gt;\u0026thinsp;2.5 is associated with a significantly reduced risk of adverse cardiovascular events, its application in clinical practice should still be weighed against the patient\u0026rsquo;s overall condition, prognostic factors, and individualized treatment strategy. Therefore, a 3v-CT-FFR\u0026thinsp;\u0026le;\u0026thinsp;2.5, as a novel functional assessment parameter, holds considerable clinical value in guiding individualized treatment and prognostic management for patients with CAD. Although coronary obstruction in patients with UA is significantly associated with the risk of developing MACCEs \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e, anatomical stenosis does not always correlate with actual hemodynamic impairment. The use of 3v-CT-FFR values derived from CCTA allows for noninvasive and quantitative reconstruction of coronary hemodynamics, effectively addressing the limitations of purely anatomical assessments. Clinically, combining 3v-CT-FFR with individual patient characteristics enables the precise identification of high-risk populations and helps determine the optimal timing for safely deferring or avoiding invasive CAG, thereby reducing healthcare costs and procedural complications. Consequently, for patients with newly diagnosed UA, an abnormal 3v-CT-FFR (\u0026le;\u0026thinsp;2.5) detected on initial CCTA should prompt timely consideration of further functional validation or interventional therapy.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLIMITATIONS\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFirst, this study adopted a retrospective cohort design, which may be subject to potential selection bias. Additionally, this study was conducted at a single center; therefore, the predictive value of the 3v-CT-FFR requires validation in multicenter, large-sample, prospective cohorts. Second, the CT-FFR measurements were based solely on the deep learning algorithm DeepVESSEL-FFR, without simultaneous comparisons with invasive indicators such as the wire-based FFR or the quantitative flow ratio. Thus, measurement discrepancies cannot be ruled out. Future studies should incorporate concurrent invasive FFR or hemodynamic modeling data to assess the accuracy and consistency of the CT-FFR and 3v-CT-FFR. Third, the 3v-CT-FFR was measured only once during CCTA and was not dynamically monitored during follow-up. Given that cardiac status and plaque characteristics may change over time or in response to pharmacological or interventional treatment, future studies may consider serial measurements or pre- and postintervention comparisons to explore dynamic trends and their prognostic implications. Fourth, this study did not incorporate multimodal parameters such as plaque composition, inflammation, or wall shear stress. Our focus was on functional assessment using the CT-FFR, without high-resolution plaque characterization using intravascular ultrasound or optical coherence tomography, limiting the development of a deeper understanding of the underlying pathophysiological mechanisms. Finally, patients with atrial fibrillation, arrhythmias, or other nonsinus rhythms were excluded; thus, the applicability of the 3v-CT-FFR in these high-risk populations remains unclear. Moreover, this cohort included only patients with UA and did not include those with stable or acute myocardial infarction. Therefore, further investigation is needed before generalizing these findings to patients with stable or acute myocardial infarction.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eAs a noninvasive and quantitative indicator of global coronary functional status, a summed 3v-CT-FFR\u0026thinsp;\u0026le;\u0026thinsp;2.5 can be used to independently predict the long-term risk of MACCEs in patients with UA. This parameter provides a valuable basis for individualized interventional decision-making. Future multicenter, prospective studies are needed to validate its predictive utility and to explore its applicability across different subgroups of CAD patients, thereby advancing precision cardiovascular imaging and therapy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Clinical research transformation project ofAnhui Province (202304295107020086) and Key Project of NaturaScience Research of the Anhui Provincial Department of Education(2022AH051477).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIndividual participant data that underlie the results reported in thisarticle, after de-identification can be obtained from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompliance with Ethical Standards\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGuarantor：\u003c/p\u003e\n\u003cp\u003eThe scientific guarantor of this publication is Hongju Wang.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors of this manuscript declare no relationships with anycompanies, whose products or services may be related to the subjectmatter of the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all subjects (patients) in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of the First Affiliated Hospital of Bengbu Medical University ([2023]KY046).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy subjects or cohorts overlap\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone of.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZhuoya Yao conceived and performed the study. Jun Wang,Miaonan Li and Hongju Wang participated in the design of the study and performed the clinical study. Yao Li and Chuan Jin wrote the manuscript and analysis and interpretation of the data.All authors agree to be accountable for all aspects of the work\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMensah GA, Fuster V, Murray C, Roth GA. Global Burden of Cardiovascular Diseases and Risks Collaborators. Global Burden of Cardiovascular Diseases and Risks, 1990\u0026ndash;2022. J Am Coll Cardiol. 2023;82(25):2350\u0026ndash;473.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eByrne RA, Rossello X, Coughlan JJ, et al. 2023 ESC Guidelines for the management of acute coronary syndromes. Eur Heart J. 2023;44(38):3720\u0026ndash;826.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang L, Wu M, Xu Y, et al. Multimodal data-driven prognostic model for predicting new-onset ST-elevation myocardial infarction following emergency percutaneous coronary intervention. Inflamm Res. 2023;72(9):1799\u0026ndash;809.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang J, Wang Y, Duan S, et al. Multimodal Data-Driven Prognostic Model for Predicting Long-Term Prognosis in Patients With Ischemic Cardiomyopathy and Heart Failure With Preserved Ejection Fraction After Coronary Artery Bypass Grafting: A Multicenter Cohort Study. J Am Heart Assoc. 2024;13(23):e036970.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu Y, Wang J, Zhou Z, Yang Y, Tang L. Multimodal Prognostic Model for Predicting Chronic Coronary Artery Disease in Patients Without Obstructive Sleep Apnea Syndrome. Arch Med Res. 2024;55(1):102926.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTonino PA, De Bruyne B, Pijls NH, et al. Fractional flow reserve versus angiography for guiding percutaneous coronary intervention. N Engl J Med. 2009;360(3):213\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Bruyne B, Fearon WF, Pijls NH, et al. Fractional flow reserve-guided PCI for stable coronary artery disease. N Engl J Med. 2014;371(13):1208\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXaplanteris P, Fournier S, Pijls N, et al. Five-Year Outcomes with PCI Guided by Fractional Flow Reserve. N Engl J Med. 2018;379(3):250\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTesche C, De Cecco CN, Albrecht MH, et al. Coronary CT Angiography-derived Fractional Flow Reserve. Radiology. 2017;285(1):17\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee JM, Koo BK, Shin ES, et al. Clinical implications of three-vessel fractional flow reserve measurement in patients with coronary artery disease. Eur Heart J. 2018;39(11):945\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim C, Park CH, Lee BY, et al. 2024 Consensus Statement on Coronary Stenosis and Plaque Evaluation in CT Angiography From the Asian Society of Cardiovascular Imaging-Practical Tutorial (ASCI-PT). Korean J Radiol. 2024;25(4):331\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNarula J, Chandrashekhar Y, Ahmadi A et al. SCCT 2021 Expert Consensus Document on Coronary Computed Tomographic Angiography: A Report of the Society of Cardiovascular Computed Tomography. J Cardiovasc Comput Tomogr. 2021. (4): 15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Qiu H, Hou Z, et al. Additional value of deep learning computed tomographic angiography-based fractional flow reserve in detecting coronary stenosis and predicting outcomes. Acta Radiol. 2022;63(1):133\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTesche C, De Cecco CN, Baumann S, et al. Coronary CT Angiography-derived Fractional Flow Reserve: Machine Learning Algorithm versus Computational Fluid Dynamics Modeling. Radiology. 2018;288(1):64\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFoy AJ, Dhruva SS, Peterson B, Mandrola JM, Morgan DJ, Redberg RF. Coronary Computed Tomography Angiography vs Functional Stress Testing for Patients With Suspected Coronary Artery Disease: A Systematic Review and Meta-analysis. JAMA Intern Med. 2017;177(11):1623\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTruesdell AG, Alasnag MA, Kaul P, et al. Intravascular Imaging During Percutaneous Coronary Intervention: JACC State-of-the-Art Review. J Am Coll Cardiol. 2023;81(6):590\u0026ndash;605.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang J, Liu W, Chen H, et al. Novel Insights Into the Interaction Between the Autonomic Nervous System and Inflammation on Coronary Physiology: A Quantitative Flow Ratio Study. Front Cardiovasc Med. 2021;8:700943.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang J, Li X, Pu J et al. Association between Gamma-Glutamyl Transferase and Coronary Atherosclerotic Plaque Vulnerability: An Optical Coherence Tomography Study. Biomed Res Int. 2019. 2019: 9602783.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKubo T, Emori H, Katayama Y, Terada K. Three-vessel fractional flow reserve measurement for predicting clinical prognosis in patients with coronary artery disease. J Thorac Dis. 2018;10(Suppl 26):S3115\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoi KH, Lee JM, Koo BK, et al. Prognostic Implication of Functional Incomplete Revascularization and Residual Functional SYNTAX Score in Patients With Coronary Artery Disease. JACC Cardiovasc Interv. 2018;11(3):237\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHamaya R, Hoshino M, Kanno Y, et al. Prognostic implication of three-vessel contrast-flow quantitative flow ratio in patients with stable coronary artery disease. EuroIntervention. 2019;15(2):180\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou B, Tan W, Duan S, et al. Inflammation Biomarker-Driven Vertical Visualization Model for Predicting Long-Term Prognosis in Unstable Angina Pectoris Patients with Angiographically Intermediate Coronary Lesions. J Inflamm Res. 2024;17:10571\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"unstable angina, CT-derived fractional flow reserve, coronary computed tomography angiography, clinical prognosis","lastPublishedDoi":"10.21203/rs.3.rs-9147746/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9147746/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eTo evaluate the utility of the sum of computed tomography-derived fractional flow reserve in three coronary arteries (3v-CT-FFR) for predicting major adverse cardiac and cerebrovascular events (MACCEs) in patients with newly diagnosed unstable angina (UA).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis retrospective study included 157 consecutive patients who were diagnosed with UA via coronary CT angiography and concurrent invasive coronary angiography between January 2021 and December 2022 at the First Affiliated Hospital of Bengbu Medical University. The 3v-CT-FFR was defined as the sum of the CT-FFR values for the left anterior descending artery, left circumflex artery, and right coronary artery. The primary endpoint was the occurrence of MACCEs, including all-cause death, cardiac death, nonfatal myocardial infarction, recurrent angina, heart failure, unplanned revascularization, and/or stroke. The optimal cutoff value was determined using receiver operating characteristic curve analysis. Kaplan–Meier survival curves and Cox proportional hazards models were constructed to evaluate the independent utility of the 3v-CT-FFR for predicting MACCEs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eA total of 157 patients were included in this study, with a median follow-up duration of 30 months. During follow-up, 43 MACCEs (27.4%) were reported. The optimal 3v-CT-FFR cutoff value for predicting MACCEs was 2.50, with an area under the curve of 0.729 (95% confidence interval [CI]: 0.646–0.812, \u003cem\u003eP \u0026lt;\u003c/em\u003e0.001), a sensitivity of 81.4%, and a specificity of 57.0%. Patients with a 3v-CT-FFR ≤ 2.50 had a significantly greater incidence of MACCEs than did those with a 3v-CT-FFR \u0026gt; 2.50 (38.0% \u003cem\u003evs.\u003c/em\u003e 16.7%, \u003cem\u003eP =\u003c/em\u003e 0.003). Multivariate Cox regression analysis revealed that a 3v-CT-FFR ≤ 2.50 was an independent risk factor for MACCEs (hazard ratio: 4.121; 95% CI: 2.108–8.054; \u003cem\u003eP \u0026lt;\u003c/em\u003e 0.001). Kaplan–Meier analysis showed that the event-free survival rate was significantly lower in the 3v-CT-FFR ≤ 2.50 group than in the \u0026gt;2.50 group (log-rank \u003cem\u003eP \u0026lt;\u003c/em\u003e 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e A 3v-CT-FFR value ≤ 2.50 is a significant predictor of mid- to long-term MACCEs in patients with newly diagnosed UA. It holds potential value for clinical risk stratification and may help guide interventional decision-making.\u003c/p\u003e","manuscriptTitle":"Utility of the sum of CT-derived fractional flow reserve in three coronary arteries for predicting long-term prognosis in patients with newly diagnosed unstable angina","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-09 16:57:40","doi":"10.21203/rs.3.rs-9147746/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"244517747956861110838525995000230457976","date":"2026-04-04T10:04:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-04T10:03:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-04T08:44:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-04T08:31:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"96892516073998454432362656978663874927","date":"2026-04-04T08:16:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"293950001670675341115467788786800125343","date":"2026-04-04T08:09:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"289881577091039431614298231740300787478","date":"2026-04-04T04:28:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"293961133858662116739896960692631355643","date":"2026-04-04T01:46:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"17608516748527221219716968797723334047","date":"2026-04-03T20:45:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"264963165739984243418234708233624564065","date":"2026-04-03T20:45:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"170805629052513982814444527541633557177","date":"2026-04-03T15:11:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"181337787610810584774910373822886206066","date":"2026-04-03T15:11:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-03T15:09:09+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-19T14:25:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-19T06:41:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-19T06:41:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2026-03-17T10:27:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ff987be4-751b-45fc-b97a-298d6eb9f6d5","owner":[],"postedDate":"April 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-09T16:57:40+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-09 16:57:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9147746","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9147746","identity":"rs-9147746","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}