CMR—Quantified Epicardial Adipose Tissue Heterogeneity and Its Predictive Value for Ventricular and Atrial Arrhythmias After Myocardial Infarction

CMR—Quantified Epicardial Adipose Tissue Heterogeneity and Its Predictive Value for Ventricular and Atrial Arrhythmias After Myocardial Infarction | 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 CMR—Quantified Epicardial Adipose Tissue Heterogeneity and Its Predictive Value for Ventricular and Atrial Arrhythmias After Myocardial Infarction Xiaoying Zhao, Yujiao Song, Lujing Wang, Pei Liu, Siwen Chen, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7531155/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Feb, 2026 Read the published version in BMC Medical Imaging → Version 1 posted 10 You are reading this latest preprint version Abstract Background Epicardial adipose tissue (EAT) mediate both electrophysiological disturbances and structural remodeling within substrates. Fibrotic remodeling within EAT under pathological conditions revealed the presence of heterogeneity. Methods and Results The cohort study included 241 consecutive post-myocardial infarction (MI) patients, 49 experienced ventricular arrhythmias (VAs) VAs and 30 experienced atrial tachyarrhythmias (AAs) during the follow-up period. EAT volume, myocardial scar, functional and strain parameters were obtained using CVI42 workstation. EAT heterogeneity was calculated using the entropy formula in Python. Patients in the VAs(+) group showed impaired cardiac pumping function, reduced left ventricular (LV) strain, and a greater extent of myocardial fibrosis. Similarly, patients with elevated left atrial (LA) strain, left atrioventricular coupling index (LACI), total EAT volume, right ventricular (RV) EAT volume, and EAT entropy were more likely to develop AAs. Myocardial fibrosis exhibited modest correlations with EAT entropy. Multivariable stepwise regression models identified EAT entropy, LA storage period strain (Es), infarct core (IC) percentage, and global longitudinal strain (GLS) as independent predictors of VAs. EAT entropy, Es, and EAT thickness were predictors of AAs. Time-dependent receiver operating characteristic (ROC) curves demonstrated that the predictive performance for VAs improved progressively with longer follow-up durations. Conclusion CMR-quantified EAT entropy is a significant indicator for predicting VAs and AAs after MI and shows a linear correlation with myocardial fibrosis. Epicardial adipose tissue Arrhythmias Myocardial infarction Myocardial fibrosis Risk stratification Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Following myocardial infarction (MI), the progressive and extensive loss of myocardial tissue leads to the activation of cardiac fibroblasts, which subsequently differentiate into cardiac myofibroblasts, initiating fibrotic repair within the injured myocardium. The replacement of normal myocardial tissue by fibrotic scar disrupts physiological electrical conduction pathways. Patients are at increased risk of developing arrhythmias. However, current clinical strategies for risk stratification of arrhythmias remain limited 1 , 2 . Arrhythmogenesis is fundamentally linked to pathological alterations in myocardial structure, electrophysiological properties, and functional performance. These inter-related changes create the substrate for cardiac rhythm disturbances 3 . Therefore, current research should focus on leveraging noninvasive imaging techniques to achieve a more comprehensive assessment of cardiac remodeling, with the goal of establishing more accurate prognostic evaluation models. Epicardial adipose tissue (EAT) is visceral adipose tissue, which located between the myocardium and the visceral layer of the pericardium 4 . The absence of a separating membrane between EAT and the myocardium, coupled with shared microcirculation, enables direct interaction that mediates both vascular secretion and paracrine signaling 5 . This anatomic proximity permits EAT to secrete diverse bioactive molecules modulating energy metabolism and vascular inflammation. Under physiological conditions, EAT regulates positive effects, maintaining a balance between anti-inflammatory and pro-inflammatory responses, thus preserving the normal structure and function of the myocardium and coronary arteries 6 . Emerging evidence implicates EAT as an active contributor to arrhythmogenesis, mediating both electrophysiological disturbances and structural remodeling within substrates 7 . EAT can directly infiltrate the myocardium, leading to cardiomyocyte dysfunction, promoting myocardial fibrosis, causing structural changes and functional impairment of the cardiomyocytes 8 . Additionally, studies have suggested that pathological changes in EAT may be associated with abnormal increases in autonomic nervous tension and the occurrence of arrhythmias 9 . Leptin produced by adipocytes can activate sympathetic neurons and increase the release of neuropeptide Y, which then interacts with Y1 receptors to trigger arrhythmias in cardiomyocytes 10 . Imaging techniques are primary means for the quantitative assessment of EAT, including echocardiography, computed tomography (CT), and cardiac magnetic resonance (CMR). Echocardiography is less accurate and less reproducible compared to CT and CMR. Although CT remains the conventional gold standard for EAT quantification, CMR is increasingly preferred due to superior tissue contrast, precise functional analysis, and absence of ionizing radiation 11 . Traditional imaging studies of EAT have prioritized quantitative assessment while largely neglecting heterogeneity analysis. Entropy, an established parameter for evaluating tissue heterogeneity in CMR imaging, noninvasively characterizes tissue heterogeneity through pixel signal distribution variations 12 . Previous studies have demonstrated a strong association between myocardial scar entropy and post-MI arrhythmias 13 . This study quantified EAT entropy using CMR imaging to investigate its association with myocardial fibrosis in post-MI patients and to evaluate its clinical utility for risk stratification. MATERIALS AND METHODS Study population Participants were recruited between June 2017 and February 2022 from The Second Affiliated Hospital of Kunming Medical University. Enrollment criteria: a. previous MI with coronary angiography ≥ 70% stenosis in ≥ 1 coronary artery or ≥ 50% stenosis of the left main stem, b. CMR scanning with myocardial scars identified as ischemic distributions characterized by subendocardial or transmural hyperintensity within the coronary supply territory in the CMR images, c. New York Heart Association (NYHA) class ≤ III. Exclusion criteria: a. myocardial scar on CMR images due to another original and secondary cardiomyopathy, b. history of sustained ventricular arrhythmias (VAs) or atrial tachyarrhythmias (AAs), c. presence of pericardial effusion interference EAT measurement, d. poor CMR image quality for post-processing. The investigation conforms with the principles outlined in the Declaration of Helsinki. The institutional review boards at the hospital approved the study and all participants provided written informed consent. Clinical assessment and follow-up Clinical history, laboratory data, and medications were accessed from electronic medical records by investigator (C.MT). Followed-up information of patients were collected on electronic medical records by two researchers (SW.C., P.L.) blinded to patients’ baseline data. VAs are defined as a composite of ventricular tachycardia (VT), ventricular fibrillation (VF), and ventricular flutter. VT is characterized by more than three consecutive ventricular ectopic beats at a rate exceeding 120 beats per minute. VF and ventricular flutter are defined by a ventricular rate exceeding 180 beats per minute, accompanied by indistinguishable QRS and T waves. AAs are defined as a composite of atrial fibrillation (AF), atrial flutter, and atrial tachycardia (AT). AT is characterized by more than three consecutive atrial ectopic beats at a P wave rate between 150 and 250 beats per minute. AF and atrial flutter are defined by an atrial rate exceeding 250 beats per minute, accompanied by indistinguishable P waves. All patients underwent 24-hour Holter monitoring at baseline and follow-up. Arrhythmias were confirmed via simultaneous 12-lead ECG during documented events. The follow-up period is determined by the date of the last contact during follow-up (if no events occurred) or the date of endpoint events. CMR protocol and postprocessing CMR protocol Images were acquired on a 3.0T Philips MR scanner (Achieva, Philips Medical Systems, the Netherlands) with electrocardiographic gating techniques. The cine imaging was conducted following a standardized protocol in short-axis, two-, three-, and four-chamber long-axis views, utilizing a steady-state free precession sequence. The scan parameters are as follows: repetition time (TR) = 3.1 ms, echo time (TE) = 1.54 ms, flip angle (FA) = 45°, slice thickness = 8mm, field of view (FOV) = 350mm×350mm, and voxel size = 1.8mm×1.4mm×8.0mm. Late gadolinium enhancement (LGE) images were acquired 10–15 minutes after the administration of the contrast agent Gd-HP-DO3A (ProHance, BRACCO S.P.A., Italy) using an inversion recovery gradient-echo imaging sequence. The imaging parameters were: TR = 5.0 ms, TE = 2.4 ms, FA = 25°, slice thickness = 6 mm, FOV = 350mm×350mm, and voxel size = 1.8mm×1.4mm×8.0mm. Cardiac function and strain analysis Two radiologists (XY.Z., YJ.S.) independently analyzed the CMR images offline using the CVI 42 software (Circle Cardiovascular Imaging Inc®, v5.1.4, Canada). The software can automatically track the endocardial and epicardial borders of the left ventricle (LV) on the short-axis cine stack, as well as the borders of the left atrium (LA) on two- and four-chamber long-axis images. The observers evaluated the accuracy of these automatic tracings and manually adjusted the borders when necessary. LV functional parameters: LV ejection fraction (LVEF), LV end-diastolic volume (LVEDV), LV end-systolic volume (LVESV), cardiac output (CO), LV mass (LVM) and LA volume: LA end-diastolic volume (LAEDV), LA end-systolic volume (LAESV) were automatically acquired. Left atrio-ventricular coupling index (LACI) was calculated as the ratio of LAEDV/LVEDV×100% 14 . LV strain analyses were performed using the ‘Tissue Tracking’ module. This process involved delineating the endocardial and epicardial borders of the LV on short-axis, two-, three-, and four-chamber cine images. Peak LV global radial strain (GRS), global circumferential strain (GCS), and global longitudinal strain (GLS) were then automatically calculated as the averages of all wall segments.The endocardium and epicardium of the LA were automatically delineated on the two- and four-chamber cine sequences. The time-strain rate curve for LA longitudinal strain were automatically generated. From the LA time-strain curve, the following parameters were derived: LA storage period strain (Es, corresponding to total LA strain), LA conduit period strain (Ee, corresponding to passive LA strain), and LA contraction period strain (Ea, corresponding to active LA strain), with Ee calculated as Es - Ea. EAT volume, thickness, and entropy EAT was defined as the adipose tissue with high intensity located between the myocardium and the visceral pericardium on standard short-axis cine images. All measurements were performed during the endo-diastolic phase. The epicardial borders and parietal pericardium of LV and right ventricle (RV) were manually delineated on the short-axis stacks. Special attention was given to accurately identify the pericardium and to avoid the inclusion of paracardial fat in the tracing. EAT were meticulously traced on consecutive images, starting from the mitral valve plane and continuing to the cardiac apex, covering 6 to 8 slices depending on the heart size (Fig. 1 ). EAT volumes were calculated using the modified Simpson’s method, where the areas traced from all slices were summed and then multiplied by the slice thickness plus the slice gap (each 6 mm) 15 . EAT thickness was measured perpendicularly from the epicardium to the pericardium at the Rindfleisch fold level on three-chamber long-axis views, traversing coronary artery centers within EAT 16 . EAT entropy analysis was performed using a the Shannon’s formula written in Python (MathWorks 3.8, MA, version) 17 . Signal intensity (SI) for each myocardial voxel was automatically quantified across contoured short-axis images. Entropy values were scaled from 0 to 10, where 0 denoted maximum SI homogeneity and 10 represented maximum SI heterogeneity. P(x) represents the probability distribution of SI, where x denotes the SI normalized according to a predefined range of 0 to 1024. Simultaneously, histogram features describing the distribution of SI across the volume of interest were obtained 18 . LGE extent The grayscale thresholding algorithm was employed to semi-quantitatively segment the myocardium into three distinct tissue zones: infarct core (IC), border zone (BZ), and remote myocardium. IBZ is quantitatively defined as the sum of IC and BZ. The region outside the infarct was manually delineated as precisely as possible, after which the mean and standard deviation (SD) of the voxel intensities within this region were automatically calculated. The grayscale value corresponding to mean + 2 SDs was designated as the upper limit for normal tissue, while mean + 3 SDs was established as the lower limit for IC. BZ was defined as the area between mean + 2 SDs and mean + 3 SDs. The extent of LGE was expressed as a proportion of the total LVM (LGE%). Statistical analysis Variables were expressed as mean ± SD, numbers (percentage), or median (interquartile ranges), depending on distribution characteristics. Differences in continuous variables between groups were compared using either the independent samples t-test or the Wilcoxon rank-sum test, as appropriate. Categorical variables were analyzed using the Chi-squared test or Fisher’s exact test. Spearman rank correlation was used to analyze correlations between EAT parameters and myocardial fibrosis. The receiver operator characteristic (ROC) analysis was employed to evaluate the prognostic value of the parameters. To analyze occurrence of VAs and AAs, the Kaplan–Meier method was applied and log-rank test was used for assessing differences between groups divided by cut-off values. Univariable Cox proportional regression models were constructed. Variables achieving statistical significance (p < 0.05) were included in the multivariable analysis. A stepwise selection approach (retention threshold: p < 0.05; elimination threshold: p ≥ 0.1) was applied during regression modeling. Time-dependent ROC curves were generated to assess the prognostic performance of the models. Intra- and inter-observer variabilities for IC, total EAT volume, thickness, and entropy were assessed in a random subset of 40 patients, with intraclass correlation coefficients (ICCs) calculated.Statistical analyses used GraphPad Prism 9, with significance defined as two-tailed P < 0.05. RESULTS Patient characteristics This study initially recruited 319 post-MI patients, of whom 241 were ultimately included in the analysis. During a mean follow-up of 31 months, 49 patients developed VAs, and 30 patients developed AAs. The baseline characteristics are presented in Table 1 . Patients with VAs were significantly older than VAs (-) (p = 0.040) and had higher NYHA class (p < 0.001). In contrast, AAs (+) and AAs (-) did not differ significantly in NYHA class (p = 0.070). In addition, no significant differences were observed between the groups in terms of gender, underlying comorbidities, types of MI, culprit arteries, or medication (all p > 0.05). Table 1 Clinical characteristics of the patient population * : p<0.05. BMI: body mass index, NYHA: New York Heart Association, MI: myocardial infarction, STEMI: ST-segment elevation myocardial infarction, NSTEMI: non ST-segment elevation myocardial infarction, LAD: left anterior descending branch, LCX: left circumflex artery, RCA: right coronary artery. All patients (n = 241) Ventricular arrhythmias Atrial arrhythmias Characteristics + (n = 49) - (n = 192) P value + (n = 30) - (n = 211) P value Age, y 55 ± 13 59 ± 14 55 ± 13 0.040 * 62 ± 14 54 ± 13 <0.001 * Male 176(73.03) 39(79.59) 137(71.35) 0.236 19(63.33) 157(74.41) 0.201 BMI, kg/m 2 24.38 ± 3.40 24.55 ± 3.54 23.73 ± 2.76 0.136 24.51 ± 3.44 23.49 ± 3.08 0.127 Hypertension 127(52.70) 23(46.94) 104(54.17) 0.366 19(63.33) 108(51.18) 0.212 Hypercholesterolemia 188(78.01) 39(79.59) 149(77.60) 0.764 23(76.67) 165(78.20) 0.850 Diabetes mellitus 62(25.73) 16(32.65) 46(23.96) 0.223 10(33.33) 52(24.64) 0.308 Smoking 115(47.72) 25(51.02) 90(46.88) 0.604 13(43.33) 102(48.34) 0.607 NYHA class <0.001 * 0.070 I 103(42.74) 14(28.57) 89(46.35) 7(23.33) 96(45.50) II 110(45.64) 20(40.82) 90(46.88) 18(60.00) 92(43.60) III 28(11.62) 15(30.61) 13(6.77) 5(16.67) 23(10.90) MI type 0.530 0.527 STEMI 148(61.41) 32(65.31) 116(60.42) 20(66.67) 128(60.66) NSTEMI 93(38.59) 17(34.69) 76(39.58) 10(33.33) 83(39.34) Culprit arteries 0.800 0.839 LAD 108(44.81) 20(40.82) 88(45.83) 13(43.33) 95(45.02) LCX 62(25.73) 13(26.53) 49(25.52) 9(30.00) 53(25.12) RCA 71(29.46) 16(32.65) 55(28.65) 8(26.67) 63(29.86) Medication Beta-blockers 213(88.38) 45(91.84) 168(87.50) 0.398 27(90.00) 186(88.15) 0.768 RAAS-inhibitors 175(72.61) 34(69.39) 141(73.43) 0.570 24(80.00) 151(71.56) 0.332 Statins 221(91.70) 48(97.96) 173(90.10) 0.075 28(93.33) 193(91.47) 0.729 Loop diuretics 88(36.51) 25(51.02) 63(32.81) 0.018 * 11(36.67) 77(36.49) 0.985 Aspirin 205(85.06) 46(93.88) 159(82.81) 0.052 28(93.33) 177(83.89) 0.174 Clopidogrel/Prasugrel/Ticagrelor 150(62.64) 32(65.31) 118(61.46) 0.620 20(66.67) 130(61.61) 0.593 * : p＜0.05. BMI: body mass index, NYHA: New York Heart Association, MI: myocardial infarction, STEMI: ST-segment elevation myocardial infarction, NSTEMI: non ST-segment elevation myocardial infarction, LAD: left anterior descending branch, LCX: left circumflex artery, RCA: right coronary artery. The CMR characteristics are presented in Table 2 . Compared to the VAs (-) group, VAs (+) patients exhibited significantly impaired cardiac pumping function and a greater extent of myocardial fibrosis (all p 0.05). Patients with VAs demonstrated significantly greater impairment of global left ventricular strain in all three directions (radial, circumferential, longitudinal) compared to those without VAs (all p 0.05). The VAs (+) group also exhibited higher rates of ventricular aneurysm (p = 0.018). Additionally, patients with elevated LA strain, LACI, total EAT volume, RV EAT volume, and EAT entropy were more likely to develop both VAs and AAs (all p 0.05). Table 2 CMR characteristics of the patient population All patients (n = 241) Ventricular arrhythmias Aatrial arrhythmias CMR parameters + (n = 49) - (n = 192) P value + (n = 30) - (n = 211) P value CI, L/min·m 2 2.52 ± 0.90 2.36 ± 0.98 2.55 ± 0.88 0.272 2.64 ± 11.16 2.49 ± 0.86 0.331 LVMI, g/m 2 54.92(19.55) 58.16(18.21) 54.46(19.22) 0.206 49.04(16.97) 55.20(17.12) 0.366 LVEDVI, mL/m 2 81.19(49.10) 93.06(65.08) 78.81(44.04) 0.024 * 75.00(43.76) 81.44(48.02) 0.481 LVESVI, mL/m 2 40.90(40.69) 57.12(76.23) 37.31(64.30) 0.003 * 34.66(59.68) 41.48(66.44) 0.350 LVEF, % 46.83(30.63) 35.94(8.37) 49.51(12.42) 0.003 * 50.2314.99) 46.68(24.46) 0.263 Es, % (reservoir) 25.04(9.02) 22.04(6.11) 26.14(9.87) <0.001 * 20.51(7.51) 15.14(7.99) <0.001 * Ee, % (conduit) 15.12(7.07) 12.98(3.13) 16.21(14.87) <0.001 * 12.13(11.97) 16.08(13.87) <0.001 * Ea, % (contraction) 13.23(6.02) 9.93(6.07) 13.07(7.24) <0.001 * 10.07(5.21) 12.89(7.13) <0.001 * LACI, % 22.79(11.92) 27.12(4.78) 20.46(11.91) <0.001 * 27.84(12.15) 21.33(11.98) <0.001 * Degree of transmural 0.159 0.545 ≤ 25% 103(42.74) 23(46.94) 80(41.67) 13(43.34) 90(42.65) 26%−50% 39(16.18) 9(18.37) 30(15.62) 7(23.33) 32(15.17) 51%−75% 54(22.41) 4(8.16) 50(26.04) 6(20.00) 48(22.45) ≥ 76% 45(18.67) 13(26.53) 32(16.67) 4(13.33) 41(19.43) IC, % 14.35(13.02) 19.00(3.31) 12.84(7.39) <0.001 * 12.15(6.25) 14.40(7.32) 0.535 BZ, % 6.64(7.28) 9.26(6.83) 5.69(5.99) <0.001 * 6.70(6.92) 6.63(6.72) 0.734 IBZ, % 22.23(20.21) 29.32(15.12) 19.26(17.46) <0.001 * 18.88(18.77) 22.49(17.74) 0.711 GRS, % 22.79 ± 10.86 18.28 ± 9.52 23.94 ± 10.91 <0.001 * 25.37 ± 12.57 22.43 ± 10.58 0.165 GCS, % -15.23(8.07) -11.12(7.69) -15.87(7.10) 0.002 * -16.89(6.73) -15.03(8.03) 0.113 GLS, % -10.49 ± 4.02 -8.77 ± 3.70 -11.79 ± 4.03 <0.001 * -12.13 ± 3.29 -11.04 ± 4.24 0.178 Total EAT volume, cm 3 65.72 ± 20.20 72.82 ± 20.52 65.02 ± 19.78 0.016 * 78.85 ± 18.68 64.87 ± 19.77 <0.001 * EAT thickness, mm 5.10(2.45) 6.20(3.40) 5.10(2.40) 0.012 * 7.00(2.55) 5.10(2.40) <0.001 * LV EAT volume, cm 3 21.57 ± 9.18 22.91 ± 9.74 21.60 ± 9.05 0.382 27.13 ± 7.64 21.12 ± 9.16 <0.001 * RV EAT volume, cm 3 44.15 ± 14.42 49.91 ± 15.33 43.42 ± 13.84 0.005 * 51.72 ± 14.23 43.75 ± 14.14 0.004 * EAT entropy 6.73(0.81) 7.19(0.35) 6.54(0.73) <0.001 * 7.09(0.47) 6.66(0.83) <0.001 * mitral regurgitation 76(31.54) 17(34.69) 59(30.73) 0.594 13(43.33) 63(29.86) 0.137 aortic regurgitation 38(15.77) 11(22.45) 27(14.06) 0.150 5(16.67) 33(15.64) 0.885 MVO 13(5.39) 4(8.16) 9(18.37) 0.259 0(0) 13(100.00) 0.774 ventricular aneurysm 30(12.45) 11(22.45) 19(38.78) 0.018 * 4(13.33) 26(12.32) 0.379 * : p<0.05. CMR: cardiac magnetic resonance, CI: cardiac index, LV: left ventricle, LVMI: LV mass index, LVEDVI: LV end-diastolic volume index, LVESVI: LV end-systolic volume index, LVEF:LV functional parameters, Es: LA storage period strain, Ee: LA conduit period strain, Ea: LA contraction period strain, LACI: left atrio-ventricular coupling index, IC: infarct core, BZ: border zone, IBZ: IC + BZ, GRS: global radial strain, GCS: global circumferential strain, GLS: global longitudinal strain, EAT: epicardial adipose tissue, RV: right ventricle, MVO: microvascular obstruction. EAT and myocardial fibrosis Figure 2 illustrates the correlations between EAT entropy and myocardial fibrosis. Notably, IC%, BZ%, and IBZ% exhibited modest correlations with EAT entropy (r = 0.23, 0.27, and 0.26, respectively, all p 0.05). Arrhythmias prediction ROC curve analysis (Table 3 ) identified multiple predictors of VAs: LVEF, LA and LV strain, LACI, LGE extent, total and RV EAT volume, EAT thickness, and EAT entropy (all p < 0.05). For AAs, significant predictors included LA strain, LACI, total LV and RV EAT volumes, EAT thickness, and EAT entropy(all p < 0.05). Kaplan-Meier analysis using optimal EAT entropy thresholds determined by Youden Index significantly improved prognostic risk stratification in high-risk patients with both VAs and AAs (both p < 0.001) (Figure. 3). Table 3 ROC analysis results of arrhythmias prediction Ventricular arrhythmias Aatrial arrhythmias Parameters AUC P value AUC P value LVEF, % 0.635 0.004 * —— 0.252 Es, % (reservoir) 0.696 <0.001 * 0.752 <0.001 * Ee, % (conduit) 0.672 <0.001 * 0.751 <0.001 * Ea, % (contraction) 0.661 <0.001 * 0.711 <0.001 * LACI, % 0.681 <0.001 * 0.712 <0.001 * IC, % 0.743 <0.001 * —— 0.569 BZ, % 0.710 <0.001 * —— 0.599 IBZ, % 0.739 <0.001 * —— 0.798 GRS, % 0.652 0.001 * —— 0.243 GCS, % 0.644 0.002 * —— 0.098 GLS, % 0.725 <0.001 * —— 0.093 Total EAT volume, cm 3 0.618 0.012 * 0.701 <0.001 * EAT thickness, mm 0.617 0.018 * 0.727 <0.001 * LV EAT volume, cm 3 —— 0.397 0.694 <0.001 * RV EAT volume, cm 3 0.633 0.005 * 0.650 0.005 * EAT entropy 0.809 <0.001 * 0.771 <0.001 * * : p<0.05. LV: left ventricle, LVEF:LV functional parameters, Es: LA storage period strain, Ee: LA conduit period strain, Ea: LA contraction period strain, LACI: left atrio-ventricular coupling index, IC: infarct core, BZ: border zone, IBZ: IC + BZ, GRS: global radial strain, GCS: global circumferential strain, GLS: global longitudinal strain, EAT: epicardial adipose tissue, RV: right ventricle. In the univariable Cox proportional regression analysis, LVEF, LA and LV strain, LGE extent, total and RV EAT volume, EAT thickness, and EAT entropy emerged as significant predictors of VAs (all p < 0.05). Similarly, LA strain, LACI, total LV and RV EAT volume, EAT thickness, and EAT entropy were identified as univariable predictors of AAs (all p < 0.05). In the multivariable stepwise regression analysis, EAT entropy, Es, IC%, and GLS were selected as independent predictors for VAs (all p < 0.05) after excluding other parameters (Table 4 ). EAT entropy, Es, and EAT thickness were identified as independent predictors for AAs (all p < 0.05). Table 4 Univariable and multivariable regression to identify variables associated with arrhythmias Ventricular arrhythmias Aatrial arrhythmias Parameters Unadjusted Hazard Ratio P value Adjusted Hazard Ratio P value Unadjusted Hazard Ratio P value Adjusted Hazard Ratio P value LVEF, % 0.969(0.949,0.990) 0.008 * —— —— 1.015(0.989,1.041) 0.264 —— —— Es, % (reservoir) 0.912(0.872,0.955) <0.001 * 0.928(0.881,0.977) 0.004 * 0.883(0.833,0.937) <0.001 * 0.872(0.809,0.939) <0.001 * Ee, % (conduit) 0.896(0.842,0.953) <0.001 * —— —— 0.855(0.789,0.926) <0.001 * —— —— Ea, % (contraction) 0.893(0.832,0.957) 0.001 * —— —— 0.846(0.772,0.926) <0.001 * —— —— LACI, % 1.017(0.996,1.039) 0.118 —— —— 1.030(1.003,1.058) 0.032 * —— —— IC, % 1.087(1.049,1.126) <0.001 * 1.049(1.008,1.093) 0.020 * 0.987(0.947,1.029) 0.534 —— —— BZ, % 1.151(1.076,1.230) <0.001 * —— —— 1.014(0.936,1.098) 0.733 —— —— IBZ, % 1.065(1.038,1.093) <0.001 * —— —— 0.994(0.966,1.024) 0.709 —— —— GRS, % 0.944(0.911,0.978) 0.001 * —— —— 1.024(0.990,1.058) 0.167 —— —— GCS, % 1.097(1.033,1.164) 0.002 * —— —— 0.941(0.873,1.014) 0.113 —— —— GLS, % 1.209(1.110,1.316) <0.001 * 1.134(1.016,1.265) 0.025 * 0.937(0.852,1.030) 0.178 —— —— Total EAT volume, cm 3 1.020(1.003,1.036) 0.017 * —— —— 1.037(1.016,1.059) <0.001 * —— —— EAT thickness, mm 1.265(1.074,1.490) 0.005 * —— —— 1.565(1.270,1.929) <0.001 * 1.455(1.140,1.856) 0.003 * LV EAT volume, cm 3 1.015(0.981,1.050) 0.381 —— —— 1.073(1.028,1.121) <0.001 * —— —— RV EAT volume, cm 3 1.032(1.009,1.056) 0.005 * —— —— 1.040(1.012,1.068) 0.005 * —— —— EAT entropy 1.033(1.021,1.0440 <0.001 * 1.033(1.020,1.046) <0.001 * 1.028(1.015,1.041) <0.001 * 1.026(1.011,1.041) <0.001 * * : p<0.05. LV: left ventricle, LVEF:LV functional parameters, Es: LA storage period strain, Ee: LA conduit period strain, Ea: LA contraction period strain, LACI: left atrio-ventricular coupling index, IC: infarct core, BZ: border zone, IBZ: IC + BZ, GRS: global radial strain, GCS: global circumferential strain, GLS: global longitudinal strain, EAT: epicardial adipose tissue, RV: right ventricle. Furthermore, we fitted the regression models and constructed time-dependent ROC curves (Fig. 4 ) to evaluate the predictive efficiency of the models more comprehensively. Excellent predictive performance was demonstrated in both regression models for VAs (AUC: 0.881, p < 0.001) and AAs (AUC: 0.879, p < 0.001). For VAs, the predictive performance improved progressively with the extension of follow-up time. In contrast, the predictive performance of the regression model for AAs was better for follow-up periods less than 30 months compared to longer durations. Patients were stratified into low-/high-entropy EAT groups (median cutoff) to evaluate arrhythmia susceptibility. High-entropy patients exhibited significantly increased scar burden (larger IC, BZ, and IBZ; p < 0.05), greater MVO prevalence, and higher STEMI proportion (Tables S1,S2), indicating more severe pathology. They also demonstrated impaired left atrial function and atrioventricular mechanical discoordination (p < 0.05). Collectively, these findings suggest EAT entropy elevation expands the proarrhythmic substrate through structural and functional derangements. Reproducibility An inter-observer reliability analysis was conducted on 40 randomly selected patients by two independent observers, along with an intra-observer reliability analysis. The reproducibility of IC%, total EAT volume, EAT thickness, and EAT entropy was confirmed at both the inter-observer and intra-observer levels. The inter-observer ICC was 0.857 for IC%, 0.883 for total EAT volume, 0.903 for EAT thickness, and 0.926 for EAT entropy (all p<0.05). For the intra-observer ICC, the first observer achieved 0.870 for IC%, 0.858 for total EAT volume, 0.892 for EAT thickness, and 0.924 for EAT entropy, while the second observer recorded 0.834 for IC%, 0.873 for total EAT volume, 0.903 for EAT thickness, and 0.931 for EAT entropy (all p < 0.001). DISCUSSION Our study presents novel predictive models incorporating EAT entropy for arrhythmia risk stratification in post-MI patients. Key findings include: (1). EAT entropy significantly enhanced predictive value for VAs and AAs through stepwise regression analysis. (2).The optimal VA prediction model combined EAT entropy, Es, IC%, and GLS. The optimal AA prediction model integrated EAT entropy, EAT thickness, and Es. (3). EAT entropy showed significant linear correlation with quantified myocardial fibrosis. EAT characteristic parameters in VAs and AAs cohorts EAT infiltration into the myocardium can impede cardiac excitation, while the adipokines it secretes regulate myocardial remodeling, making it a potential target for preventing cardiac remodeling and arrhythmias 19 . While previous research has primarily focused on EAT volume or thickness, our study is innovative in quantitatively assessing EAT entropy on CMR images. A 2021 study confirmed fibrotic remodeling within EAT under pathological conditions and its association with myocardial fibrosis, revealed the presence of heterogeneity within the EAT 20 . As entropy effectively reflects tissue heterogeneity and has been used to assess myocardial scar remodeling post-MI, this study quantified EAT entropy to evaluate its heterogeneity 13 . Statistical results showed that patients who developed VAs or AAs after MI had significantly higher EAT volume, thickness, and heterogeneity compared to those without arrhythmias. A 2011 study revealed that total EAT volume and thickness were significantly increased in patients with AAs 21 . A recent study also showed that EAT thickness was significantly greater in patients with AAs compared to the control group 22 . Meta-analysis confirmed that EAT volume was greater in patients with AAs compared to the control group 23 . These findings are consistent with ours, suggesting that EAT may be involved in the development of AAs. Limited research focused on the relationship between EAT and VAs. Although extensive data suggest that EAT may play a role in the initiation and propagation of VAs, there is no direct evidence 24 . A 2016 study found a strong correlation between EAT and the frequency of ventricular premature beats, suggesting that EAT has the potential to contribute to VAs 25 . The presence of EAT and pericardial fat affects the adipocytes within the myocardium, which in turn influences the conduction velocity in the subepicardial myocardium. This lead to the loss of the ventricular action potential conduction gradient, potentially explaining the mechanism by which EAT contributes to VAs 26 . Our study suggests that there is a statistically significant difference in RV EAT volume between patients with and without VAs, whereas LV EAT volume does not show such a difference. Evidence indicates that patients with ventricular premature beats originating from the LV have increased LV EAT volume, while those with VAs originating from the RV have larger RV EAT volumes. However, since we were unable to accurately determine the origin of VAs, we cannot confirm whether this result is due to the predominance of RV-originating VAs in our study cohort 27 . EAT entropy and myocardial fibrosis LGE can using for predicting VAs in post-MI patients by identifying myocardial fibrosis. Based on the degree of ischemic damage and histological characteristics, fibrosis scar can be further categorized into IC and BZ. Previous studies have regarded the BZ as a key region for the development of VAs 28 . EAT can directly infiltrate the myocardium, causing dysfunction of myocardial cells, promoting myocardial fibrosis, and leading to structural changes and functional disturbances in the myocardial cells 29 . EAT volume is independently associated with increased myocardial fibrosis 30 . A 2020 study conducted histological analysis of the right atrial appendage in post-MI patients to characterize EAT and atrial fibrosis. The results showed that a larger EAT volume was clinically associated with slower myocardial conduction and increased fibrosis, which demonstrated that excessive EAT exacerbates atrial myocardial fibrosis and disrupts conduction between myocardial cells 31 . Abnormal deposition of EAT, through excessive secretion of inflammatory cytokines, vascular endothelial growth factors, and matrix metalloproteinases, excessively regulates extracellular matrix activity, promotes collagen deposition, and ultimately leads to atrial myocardial fibrosis 32 . A 2021 study proved that EAT volume was higher in MI patients compared to healthy controls, and EAT volume was correlated with the extent of ventricular myocardial fibrosis 33 . Hao et al. observed a significant increase in EAT in a MI rat model and found a positive correlation between EAT mass and the extent of ventricular myocardial fibrosis measured at 2 and 4 weeks after MI 34 .While EAT's relationship with myocardial fibrosis after MI remains unquantified by CMR, our study is the first to establish this correlation using entropy-based tissue heterogeneity assessment. This method objectively quantifies pixel signal distribution on MR images and has been demonstrated to complement CMR diagnosis in ischemic and non-ischemic cardiomyopathy 13 . EAT entropy shows a linear correlation with IC%, BZ%, and IBZ%, whereas other EAT parameters do not demonstrate significant correlations with fibrosis. This indicates that, compared to volume and thickness, heterogeneity may have a deeper intrinsic connection with fibrosis. Given the clear association between scar burden and the incidence of VAs, it is possible that both factors jointly contribute to the development of VAs. However, due to the limited number of cases in this study, further research is needed to draw more conclusive and guiding conclusions. Risk stratification models for VAs and AAs in post-MI patients Current guidelines for predicting VAs in post-MI patients rely on reduced LVEF. However, the accuracy and specificity of this criterion are not ideal 35 . Research focusing on AAs prediction post-MI remains limited, with underlying pathological mechanisms incompletely defined. As VA and AA risk stratification involves complex multifactorial interactions, we integrated comprehensive clinical and CMR-derived parameters—including scar heterogeneity, functional indices, and EAT entropy—to develop predictive models using stepwise regression analysis. The final model for predicting VAs included EAT entropy, Es, IC%, and GLS, while the model for predicting AAs included EAT entropy, EAT thickness, and Es. Both models demonstrated high predictive efficacy, confirming that CMR-quantified EAT entropy has significant additional predictive value for both AAs and VAs in post-MI patients beyond conventional parameters. When EAT accumulates abnormally, it releases a large number of exosomes, which act on coronary arteries and myocardial cells through paracrine and vascular secretion mechanisms 36 . Changes in the composition of adipose tissue and stroma within EAT may indicate abnormal activation processes. On CMR images, EAT typically appears as a nearly homogeneous high-signal tissue on visual evaluation. The value of entropy quantifies the degree of variation in pixel signals, enabling the detection of signal heterogeneity. In contrast to static anatomical metrics, EAT entropy captures dynamic inflammatory-adipogenic activity. This novel biomarker may resolve clinical dilemmas where current parameters yield ambiguous risk profiles - particularly arrhythmogenic remodeling post-MI. The results of this study demonstrate the superior prognostic value of EAT entropy in post-MI patients. For ICD decision-making, we propose EAT entropy could refine guidelines in two domains: a. for preserved LVEF (> 35% ) + low EAT entropy, ICD implantation is not considered; b. for decreased LVEF + high EAT entropy, warranting prophylactic ICD per shared decision-making. However, such decisions must be grounded in a comprehensive individualized evaluation of the patient before finalization. Consistent with our study, Muhib et al. showed CMR-measured EAT volume increase predicts AAs in hypertrophic cardiomyopathy, independent of conventional risk factors 37 . A meta-analysis further confirmed the association between increased EAT thickness and volume with AAs 38 . Maryam et al. demonstrated that an increase in EAT volume is associated with a higher risk of VAs in patients with non-ischemic cardiomyopathy 39 . In a study of patients with idiopathic VT and no structural heart disease, EAT volume was identified as an independent predictor of VT recurrence 40 . These studies confirm that EAT plays a role in the occurrence of both atrial and ventricular arrhythmias. Notably, our VAs prediction model gained accuracy with prolonged follow-up, whereas the AAs model peaked before 30 months—potentially reflecting EAT-driven fibrofatty replacement inducing delayed activation and pro-arrhythmic electrophysiological remodeling during inflammatory progression 41 . EAT primarily distributes along the atrioventricular groove and the interventricular septum, extending along the branches of the coronary arteries, including the circumflex and left anterior descending arteries, and surrounding the atrium 42 . Given that the mechanisms of EAT primarily involve local infiltration, EAT may affect atrial myocardial fibrosis earlier. Although EAT can also impact ventricular myocardial fibrosis, this process likely requires a longer duration of influence. It may help explain the results of our study, but further evidence is needed to support this hypothesis. Study limitations As a single-center cohort study without external validation, generalizability is limited. While arrhythmia outcomes were predefined in the original observational registry, current CMR technology precluded left atrial fibrosis quantification—a key substrate for atrial arrhythmogenesis. Future studies employing 3D-LGE imaging could further explore the relationship between left atrial fibrosis and EAT. Further analysis could focus separately on persistent arrhythmias. Conclusions The present study is the first to quantify EAT heterogeneity using CMR images and to explore its association with VAs and AAs after MI, and we developed prediction models and evaluated their performance over different follow-up periods. Additionally, we proved the relationship between CMR-derived EAT heterogeneity and myocardial fibrosis in post-MI patients. Our data suggest that EAT heterogeneity can offer valuable prognostic and predictive information for post-MI patients. Declarations Funding Basic Research Program Supported by Yunnan Fundamental Research Kunming Medical University Joint Projects 202501AY070001-103 Yunnan Science and Technology Platform and Talent Project (Academician Expert Workstation) 202305AF150033 Innovation Fund for Doctoral Education of Kunming Medical University in 2025 2025B027 Yunnan medical and health personnel special ‘Xing Dian talent’ plan XDYC-YLWS-2023-0022 IRB Information The investigation of present study conforms with the principles outlined in the Declaration of Helsinki and was approved by Ethics Committee of the Second Affiliated Hospital of Kunming Medical University (reference number: 审-PJ-科-2023-30). Competing interests The authors declare that they have no competing interests. Data Availability Statement The deidentified participant data will be shared on a request basis. Please directly contact the corresponding author to request data sharing. 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Comp Biochem Physiol B 1989;94:225–232. Additional Declarations No competing interests reported. Supplementary Files supplementarytables.docx Cite Share Download PDF Status: Published Journal Publication published 14 Feb, 2026 Read the published version in BMC Medical Imaging → Version 1 posted Editorial decision: Revision requested 05 Nov, 2025 Reviews received at journal 19 Oct, 2025 Reviews received at journal 15 Oct, 2025 Reviewers agreed at journal 15 Oct, 2025 Reviewers agreed at journal 08 Oct, 2025 Reviewers invited by journal 06 Oct, 2025 Editor invited by journal 08 Sep, 2025 Editor assigned by journal 05 Sep, 2025 Submission checks completed at journal 05 Sep, 2025 First submitted to journal 03 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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12:36:09","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1800,"visible":true,"origin":"","legend":"","description":"","filename":"Onlineimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7531155/v1/8365736b2b11e41bd5c24e11.png"},{"id":93775126,"identity":"1c787945-849b-423c-a2ab-99469438c514","added_by":"auto","created_at":"2025-10-17 12:28:09","extension":"xml","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":175667,"visible":true,"origin":"","legend":"","description":"","filename":"efe173a610ed447786d81194869bbe001structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7531155/v1/0f2c0daf9e844be0c92416b9.xml"},{"id":93775127,"identity":"b56c02e4-951d-4933-ac68-821be46a3172","added_by":"auto","created_at":"2025-10-17 12:28:09","extension":"html","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":183630,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7531155/v1/994f2e1e6d0d4179fdba831e.html"},{"id":93775110,"identity":"6da70075-330e-4031-a0b8-b086d7ad68a5","added_by":"auto","created_at":"2025-10-17 12:28:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":265720,"visible":true,"origin":"","legend":"\u003cp\u003eEAT defination in SAX CMR images. \u0026nbsp;Yellow areas represent quantified EAT.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7531155/v1/9801babc0fb79da2a9310d0d.png"},{"id":93775109,"identity":"f57ec454-1dbe-4cae-91d5-78897882c067","added_by":"auto","created_at":"2025-10-17 12:28:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":148928,"visible":true,"origin":"","legend":"\u003cp\u003eSpearman correlation analysis of EAT entropy and LGE%. IC% (r = 0.24, p＜0.001), BZ% (r=0.27, p＜0.001), IBZ% (r=0.26, p＜0.001) were correlated with EAT entropy.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7531155/v1/eb83d819996815fd16152ea3.png"},{"id":93775112,"identity":"58b822d6-5650-4909-a4a0-25d65ec5d203","added_by":"auto","created_at":"2025-10-17 12:28:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":148872,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curves for VAs (left) and AAs (right) stratified by EAT entropy cut-off values (6.936 for VAs, 6.779 for AAs) analyses. Incidence of arrhythmia events according to high and low EAT entropy classified according to Youden Index (both p＜0.001).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7531155/v1/a6a54b94e74e32c5222b045d.png"},{"id":93776275,"identity":"6751b167-9115-499e-b5c7-9043ac035b64","added_by":"auto","created_at":"2025-10-17 12:36:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":125773,"visible":true,"origin":"","legend":"\u003cp\u003eTime-dependent ROC curves with associated AUCs were generated for the regression models selected by stepwise analysis. Left for VAs, right for AAs.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7531155/v1/4d6fa7f4e2f963f39ad9a747.png"},{"id":102785288,"identity":"e5c9fb79-691e-4680-afc1-8cb2e815f0d4","added_by":"auto","created_at":"2026-02-16 16:04:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1930973,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7531155/v1/0d935c92-0ef1-4823-bc77-56020033da0a.pdf"},{"id":93776273,"identity":"f7477df0-4d64-4567-93be-57971549558a","added_by":"auto","created_at":"2025-10-17 12:36:09","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":25701,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarytables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7531155/v1/fc5a50ebcbf4aad9ceaec650.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"CMR—Quantified Epicardial Adipose Tissue Heterogeneity and Its Predictive Value for Ventricular and Atrial Arrhythmias After Myocardial Infarction","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eFollowing myocardial infarction (MI), the progressive and extensive loss of myocardial tissue leads to the activation of cardiac fibroblasts, which subsequently differentiate into cardiac myofibroblasts, initiating fibrotic repair within the injured myocardium. The replacement of normal myocardial tissue by fibrotic scar disrupts physiological electrical conduction pathways. Patients are at increased risk of developing arrhythmias. However, current clinical strategies for risk stratification of arrhythmias remain limited\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Arrhythmogenesis is fundamentally linked to pathological alterations in myocardial structure, electrophysiological properties, and functional performance. These inter-related changes create the substrate for cardiac rhythm disturbances\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Therefore, current research should focus on leveraging noninvasive imaging techniques to achieve a more comprehensive assessment of cardiac remodeling, with the goal of establishing more accurate prognostic evaluation models.\u003c/p\u003e\u003cp\u003eEpicardial adipose tissue (EAT) is visceral adipose tissue, which located between the myocardium and the visceral layer of the pericardium\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The absence of a separating membrane between EAT and the myocardium, coupled with shared microcirculation, enables direct interaction that mediates both vascular secretion and paracrine signaling\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. This anatomic proximity permits EAT to secrete diverse bioactive molecules modulating energy metabolism and vascular inflammation. Under physiological conditions, EAT regulates positive effects, maintaining a balance between anti-inflammatory and pro-inflammatory responses, thus preserving the normal structure and function of the myocardium and coronary arteries\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eEmerging evidence implicates EAT as an active contributor to arrhythmogenesis, mediating both electrophysiological disturbances and structural remodeling within substrates\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. EAT can directly infiltrate the myocardium, leading to cardiomyocyte dysfunction, promoting myocardial fibrosis, causing structural changes and functional impairment of the cardiomyocytes\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Additionally, studies have suggested that pathological changes in EAT may be associated with abnormal increases in autonomic nervous tension and the occurrence of arrhythmias\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Leptin produced by adipocytes can activate sympathetic neurons and increase the release of neuropeptide Y, which then interacts with Y1 receptors to trigger arrhythmias in cardiomyocytes\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eImaging techniques are primary means for the quantitative assessment of EAT, including echocardiography, computed tomography (CT), and cardiac magnetic resonance (CMR). Echocardiography is less accurate and less reproducible compared to CT and CMR. Although CT remains the conventional gold standard for EAT quantification, CMR is increasingly preferred due to superior tissue contrast, precise functional analysis, and absence of ionizing radiation\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Traditional imaging studies of EAT have prioritized quantitative assessment while largely neglecting heterogeneity analysis. Entropy, an established parameter for evaluating tissue heterogeneity in CMR imaging, noninvasively characterizes tissue heterogeneity through pixel signal distribution variations\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Previous studies have demonstrated a strong association between myocardial scar entropy and post-MI arrhythmias\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. This study quantified EAT entropy using CMR imaging to investigate its association with myocardial fibrosis in post-MI patients and to evaluate its clinical utility for risk stratification.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy population\u003c/h2\u003e\n \u003cp\u003eParticipants were recruited between June 2017 and February 2022 from The Second Affiliated Hospital of Kunming Medical University. Enrollment criteria: a. previous MI with coronary angiography\u0026thinsp;\u0026ge;\u0026thinsp;70% stenosis in \u0026ge;\u0026thinsp;1 coronary artery or \u0026ge;\u0026thinsp;50% stenosis of the left main stem, b. CMR scanning with myocardial scars identified as ischemic distributions characterized by subendocardial or transmural hyperintensity within the coronary supply territory in the CMR images, c. New York Heart Association (NYHA) class\u0026thinsp;\u0026le;\u0026thinsp;III. Exclusion criteria: a. myocardial scar on CMR images due to another original and secondary cardiomyopathy, b. history of sustained ventricular arrhythmias (VAs) or atrial tachyarrhythmias (AAs), c. presence of pericardial effusion interference EAT measurement, d. poor CMR image quality for post-processing. The investigation conforms with the principles outlined in the Declaration of Helsinki. The institutional review boards at the hospital approved the study and all participants provided written informed consent.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eClinical assessment and follow-up\u003c/h3\u003e\n\u003cp\u003eClinical history, laboratory data, and medications were accessed from electronic medical records by investigator (C.MT). Followed-up information of patients were collected on electronic medical records by two researchers (SW.C., P.L.) blinded to patients\u0026rsquo; baseline data. VAs are defined as a composite of ventricular tachycardia (VT), ventricular fibrillation (VF), and ventricular flutter. VT is characterized by more than three consecutive ventricular ectopic beats at a rate exceeding 120 beats per minute. VF and ventricular flutter are defined by a ventricular rate exceeding 180 beats per minute, accompanied by indistinguishable QRS and T waves. AAs are defined as a composite of atrial fibrillation (AF), atrial flutter, and atrial tachycardia (AT). AT is characterized by more than three consecutive atrial ectopic beats at a P wave rate between 150 and 250 beats per minute. AF and atrial flutter are defined by an atrial rate exceeding 250 beats per minute, accompanied by indistinguishable P waves. All patients underwent 24-hour Holter monitoring at baseline and follow-up. Arrhythmias were confirmed via simultaneous 12-lead ECG during documented events. The follow-up period is determined by the date of the last contact during follow-up (if no events occurred) or the date of endpoint events.\u003c/p\u003e\n\u003ch3\u003eCMR protocol and postprocessing\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eCMR protocol\u003c/h2\u003e\n \u003cp\u003eImages were acquired on a 3.0T Philips MR scanner (Achieva, Philips Medical Systems, the Netherlands) with electrocardiographic gating techniques. The cine imaging was conducted following a standardized protocol in short-axis, two-, three-, and four-chamber long-axis views, utilizing a steady-state free precession sequence. The scan parameters are as follows: repetition time (TR)\u0026thinsp;=\u0026thinsp;3.1 ms, echo time (TE)\u0026thinsp;=\u0026thinsp;1.54 ms, flip angle (FA)\u0026thinsp;=\u0026thinsp;45\u0026deg;, slice thickness\u0026thinsp;=\u0026thinsp;8mm, field of view (FOV)\u0026thinsp;=\u0026thinsp;350mm\u0026times;350mm, and voxel size\u0026thinsp;=\u0026thinsp;1.8mm\u0026times;1.4mm\u0026times;8.0mm. Late gadolinium enhancement (LGE) images were acquired 10\u0026ndash;15 minutes after the administration of the contrast agent Gd-HP-DO3A (ProHance, BRACCO S.P.A., Italy) using an inversion recovery gradient-echo imaging sequence. The imaging parameters were: TR\u0026thinsp;=\u0026thinsp;5.0 ms, TE\u0026thinsp;=\u0026thinsp;2.4 ms, FA\u0026thinsp;=\u0026thinsp;25\u0026deg;, slice thickness\u0026thinsp;=\u0026thinsp;6 mm, FOV\u0026thinsp;=\u0026thinsp;350mm\u0026times;350mm, and voxel size\u0026thinsp;=\u0026thinsp;1.8mm\u0026times;1.4mm\u0026times;8.0mm.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eCardiac function and strain analysis\u003c/h3\u003e\n\u003cp\u003eTwo radiologists (XY.Z., YJ.S.) independently analyzed the CMR images offline using the CVI 42 software (Circle Cardiovascular Imaging Inc\u0026reg;, v5.1.4, Canada). The software can automatically track the endocardial and epicardial borders of the left ventricle (LV) on the short-axis cine stack, as well as the borders of the left atrium (LA) on two- and four-chamber long-axis images. The observers evaluated the accuracy of these automatic tracings and manually adjusted the borders when necessary. LV functional parameters: LV ejection fraction (LVEF), LV end-diastolic volume (LVEDV), LV end-systolic volume (LVESV), cardiac output (CO), LV mass (LVM) and LA volume: LA end-diastolic volume (LAEDV), LA end-systolic volume (LAESV) were automatically acquired. Left atrio-ventricular coupling index (LACI) was calculated as the ratio of LAEDV/LVEDV\u0026times;100%\u003csup\u003e14\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eLV strain analyses were performed using the \u0026lsquo;Tissue Tracking\u0026rsquo; module. This process involved delineating the endocardial and epicardial borders of the LV on short-axis, two-, three-, and four-chamber cine images. Peak LV global radial strain (GRS), global circumferential strain (GCS), and global longitudinal strain (GLS) were then automatically calculated as the averages of all wall segments.The endocardium and epicardium of the LA were automatically delineated on the two- and four-chamber cine sequences. The time-strain rate curve for LA longitudinal strain were automatically generated. From the LA time-strain curve, the following parameters were derived: LA storage period strain (Es, corresponding to total LA strain), LA conduit period strain (Ee, corresponding to passive LA strain), and LA contraction period strain (Ea, corresponding to active LA strain), with Ee calculated as Es - Ea.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eEAT volume, thickness, and entropy\u003c/h2\u003e\n \u003cp\u003eEAT was defined as the adipose tissue with high intensity located between the myocardium and the visceral pericardium on standard short-axis cine images. All measurements were performed during the endo-diastolic phase. The epicardial borders and parietal pericardium of LV and right ventricle (RV) were manually delineated on the short-axis stacks. Special attention was given to accurately identify the pericardium and to avoid the inclusion of paracardial fat in the tracing. EAT were meticulously traced on consecutive images, starting from the mitral valve plane and continuing to the cardiac apex, covering 6 to 8 slices depending on the heart size (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). EAT volumes were calculated using the modified Simpson\u0026rsquo;s method, where the areas traced from all slices were summed and then multiplied by the slice thickness plus the slice gap (each 6 mm)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. EAT thickness was measured perpendicularly from the epicardium to the pericardium at the Rindfleisch fold level on three-chamber long-axis views, traversing coronary artery centers within EAT\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. EAT entropy analysis was performed using a the Shannon\u0026rsquo;s formula written in Python (MathWorks 3.8, MA, version)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Signal intensity (SI) for each myocardial voxel was automatically quantified across contoured short-axis images. Entropy values were scaled from 0 to 10, where 0 denoted maximum SI homogeneity and 10 represented maximum SI heterogeneity.\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n \u003cp\u003eP(x) represents the probability distribution of SI, where x denotes the SI normalized according to a predefined range of 0 to 1024. Simultaneously, histogram features describing the distribution of SI across the volume of interest were obtained\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eLGE extent\u003c/h3\u003e\n\u003cp\u003eThe grayscale thresholding algorithm was employed to semi-quantitatively segment the myocardium into three distinct tissue zones: infarct core (IC), border zone (BZ), and remote myocardium. IBZ is quantitatively defined as the sum of IC and BZ. The region outside the infarct was manually delineated as precisely as possible, after which the mean and standard deviation (SD) of the voxel intensities within this region were automatically calculated. The grayscale value corresponding to mean\u0026thinsp;+\u0026thinsp;2 SDs was designated as the upper limit for normal tissue, while mean\u0026thinsp;+\u0026thinsp;3 SDs was established as the lower limit for IC. BZ was defined as the area between mean\u0026thinsp;+\u0026thinsp;2 SDs and mean\u0026thinsp;+\u0026thinsp;3 SDs. The extent of LGE was expressed as a proportion of the total LVM (LGE%).\u003c/p\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eVariables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, numbers (percentage), or median (interquartile ranges), depending on distribution characteristics. Differences in continuous variables between groups were compared using either the independent samples t-test or the Wilcoxon rank-sum test, as appropriate. Categorical variables were analyzed using the Chi-squared test or Fisher\u0026rsquo;s exact test. Spearman rank correlation was used to analyze correlations between EAT parameters and myocardial fibrosis. The receiver operator characteristic (ROC) analysis was employed to evaluate the prognostic value of the parameters. To analyze occurrence of VAs and AAs, the Kaplan\u0026ndash;Meier method was applied and log-rank test was used for assessing differences between groups divided by cut-off values. Univariable Cox proportional regression models were constructed. Variables achieving statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were included in the multivariable analysis. A stepwise selection approach (retention threshold: p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; elimination threshold: p\u0026thinsp;\u0026ge;\u0026thinsp;0.1) was applied during regression modeling. Time-dependent ROC curves were generated to assess the prognostic performance of the models. Intra- and inter-observer variabilities for IC, total EAT volume, thickness, and entropy were assessed in a random subset of 40 patients, with intraclass correlation coefficients (ICCs) calculated.Statistical analyses used GraphPad Prism 9, with significance defined as two-tailed P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003ePatient characteristics\u003c/h2\u003e\n \u003cp\u003eThis study initially recruited 319 post-MI patients, of whom 241 were ultimately included in the analysis. During a mean follow-up of 31 months, 49 patients developed VAs, and 30 patients developed AAs. The baseline characteristics are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Patients with VAs were significantly older than VAs (-) (p\u0026thinsp;=\u0026thinsp;0.040) and had higher NYHA class (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, AAs (+) and AAs (-) did not differ significantly in NYHA class (p\u0026thinsp;=\u0026thinsp;0.070). In addition, no significant differences were observed between the groups in terms of gender, underlying comorbidities, types of MI, culprit arteries, or medication (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eClinical characteristics of the patient population \u003csup\u003e*\u003c/sup\u003e: p\u0026lt;0.05. BMI: body mass index, NYHA: New York Heart Association, MI: myocardial infarction, STEMI: ST-segment elevation myocardial infarction, NSTEMI: non ST-segment elevation myocardial infarction, LAD: left anterior descending branch, LCX: left circumflex artery, RCA: right coronary artery.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAll patients\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;241)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eVentricular arrhythmias\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eAtrial arrhythmias\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;49)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;192)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;211)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u0026thinsp;\u0026plusmn;\u0026thinsp;13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59\u0026thinsp;\u0026plusmn;\u0026thinsp;14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u0026thinsp;\u0026plusmn;\u0026thinsp;13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.040\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62\u0026thinsp;\u0026plusmn;\u0026thinsp;14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54\u0026thinsp;\u0026plusmn;\u0026thinsp;13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e176(73.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39(79.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e137(71.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19(63.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e157(74.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.38\u0026thinsp;\u0026plusmn;\u0026thinsp;3.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.55\u0026thinsp;\u0026plusmn;\u0026thinsp;3.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.73\u0026thinsp;\u0026plusmn;\u0026thinsp;2.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.51\u0026thinsp;\u0026plusmn;\u0026thinsp;3.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.49\u0026thinsp;\u0026plusmn;\u0026thinsp;3.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e127(52.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23(46.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104(54.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19(63.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108(51.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.212\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypercholesterolemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e188(78.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39(79.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e149(77.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23(76.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e165(78.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.850\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62(25.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16(32.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46(23.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10(33.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52(24.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.308\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115(47.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25(51.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90(46.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(43.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102(48.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.607\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNYHA class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103(42.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14(28.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89(46.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(23.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96(45.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110(45.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20(40.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90(46.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18(60.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92(43.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28(11.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15(30.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(6.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(16.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23(10.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMI type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.527\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSTEMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e148(61.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32(65.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116(60.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20(66.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e128(60.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNSTEMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93(38.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17(34.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76(39.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10(33.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83(39.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCulprit arteries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.839\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108(44.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20(40.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88(45.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(43.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95(45.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLCX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62(25.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(26.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49(25.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9(30.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53(25.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71(29.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16(32.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55(28.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(26.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63(29.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta-blockers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e213(88.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45(91.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168(87.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27(90.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e186(88.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.768\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRAAS-inhibitors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e175(72.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34(69.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e141(73.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24(80.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e151(71.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.332\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStatins\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e221(91.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48(97.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e173(90.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28(93.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e193(91.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.729\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLoop diuretics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88(36.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25(51.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63(32.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.018\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(36.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77(36.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.985\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAspirin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e205(85.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46(93.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e159(82.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28(93.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e177(83.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClopidogrel/Prasugrel/Ticagrelor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150(62.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32(65.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118(61.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20(66.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130(61.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.593\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003csup\u003e*\u003c/sup\u003e: p＜0.05. BMI: body mass index, NYHA: New York Heart Association, MI: myocardial infarction, STEMI: ST-segment elevation myocardial infarction, NSTEMI: non ST-segment elevation myocardial infarction, LAD: left anterior descending branch, LCX: left circumflex artery, RCA: right coronary artery.\u003c/p\u003e\n \u003cp\u003eThe CMR characteristics are presented in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Compared to the VAs (-) group, VAs (+) patients exhibited significantly impaired cardiac pumping function and a greater extent of myocardial fibrosis (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, no statistically differences were observed between the AAs (+) and AAs (-) groups (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Patients with VAs demonstrated significantly greater impairment of global left ventricular strain in all three directions (radial, circumferential, longitudinal) compared to those without VAs (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Conversely, no significant differences in strain were observed between patients with atrial arrhythmias (AAs+) and those without (AAs\u0026minus;) across any direction (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The VAs (+) group also exhibited higher rates of ventricular aneurysm (p\u0026thinsp;=\u0026thinsp;0.018). Additionally, patients with elevated LA strain, LACI, total EAT volume, RV EAT volume, and EAT entropy were more likely to develop both VAs and AAs (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, the degree of transmurality, mitral regurgitation, aortic regurgitation, and microvascular obstruction (MVO) showed no significant differences among patients (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCMR characteristics of the patient population\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAll patients\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;241)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eVentricular arrhythmias\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eAatrial arrhythmias\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCMR parameters\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;49)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;192)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;211)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCI, L/min\u0026middot;m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.64\u0026thinsp;\u0026plusmn;\u0026thinsp;11.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLVMI, g/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.92(19.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e58.16(18.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.46(19.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e49.04(16.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.20(17.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLVEDVI, mL/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.19(49.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e93.06(65.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.81(44.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.00(43.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e81.44(48.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLVESVI, mL/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.90(40.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e57.12(76.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.31(64.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.66(59.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e41.48(66.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLVEF, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.83(30.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e35.94(8.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.51(12.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.2314.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e46.68(24.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEs, % (reservoir)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.04(9.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e22.04(6.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.14(9.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.51(7.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e15.14(7.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEe, % (conduit)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.12(7.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e12.98(3.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.21(14.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.13(11.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e16.08(13.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEa, % (contraction)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.23(6.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e9.93(6.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.07(7.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.07(5.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e12.89(7.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLACI, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.79(11.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e27.12(4.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.46(11.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.84(12.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e21.33(11.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDegree of transmural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103(42.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e23(46.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80(41.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(43.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e90(42.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26%\u0026minus;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39(16.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e9(18.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30(15.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(23.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e32(15.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51%\u0026minus;75%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54(22.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4(8.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50(26.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(20.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e48(22.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;76%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45(18.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e13(26.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32(16.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(13.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e41(19.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIC, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.35(13.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e19.00(3.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.84(7.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.15(6.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e14.40(7.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBZ, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.64(7.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e9.26(6.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.69(5.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.70(6.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6.63(6.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIBZ, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.23(20.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e29.32(15.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.26(17.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.88(18.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e22.49(17.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRS, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.79\u0026thinsp;\u0026plusmn;\u0026thinsp;10.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e18.28\u0026thinsp;\u0026plusmn;\u0026thinsp;9.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.94\u0026thinsp;\u0026plusmn;\u0026thinsp;10.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.37\u0026thinsp;\u0026plusmn;\u0026thinsp;12.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e22.43\u0026thinsp;\u0026plusmn;\u0026thinsp;10.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGCS, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-15.23(8.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-11.12(7.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-15.87(7.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-16.89(6.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-15.03(8.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGLS, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-10.49\u0026thinsp;\u0026plusmn;\u0026thinsp;4.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-8.77\u0026thinsp;\u0026plusmn;\u0026thinsp;3.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-11.79\u0026thinsp;\u0026plusmn;\u0026thinsp;4.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-12.13\u0026thinsp;\u0026plusmn;\u0026thinsp;3.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-11.04\u0026thinsp;\u0026plusmn;\u0026thinsp;4.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal EAT volume, cm\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.72\u0026thinsp;\u0026plusmn;\u0026thinsp;20.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e72.82\u0026thinsp;\u0026plusmn;\u0026thinsp;20.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.02\u0026thinsp;\u0026plusmn;\u0026thinsp;19.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.85\u0026thinsp;\u0026plusmn;\u0026thinsp;18.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e64.87\u0026thinsp;\u0026plusmn;\u0026thinsp;19.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEAT thickness, mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.10(2.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6.20(3.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.10(2.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.00(2.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5.10(2.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLV EAT volume, cm\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.57\u0026thinsp;\u0026plusmn;\u0026thinsp;9.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e22.91\u0026thinsp;\u0026plusmn;\u0026thinsp;9.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.60\u0026thinsp;\u0026plusmn;\u0026thinsp;9.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.13\u0026thinsp;\u0026plusmn;\u0026thinsp;7.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e21.12\u0026thinsp;\u0026plusmn;\u0026thinsp;9.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRV EAT volume, cm\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.15\u0026thinsp;\u0026plusmn;\u0026thinsp;14.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e49.91\u0026thinsp;\u0026plusmn;\u0026thinsp;15.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.42\u0026thinsp;\u0026plusmn;\u0026thinsp;13.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.72\u0026thinsp;\u0026plusmn;\u0026thinsp;14.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e43.75\u0026thinsp;\u0026plusmn;\u0026thinsp;14.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEAT entropy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.73(0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e7.19(0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.54(0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.09(0.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6.66(0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emitral regurgitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76(31.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e17(34.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59(30.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(43.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e63(29.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eaortic regurgitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38(15.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e11(22.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27(14.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(16.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e33(15.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMVO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(5.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4(8.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9(18.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e13(100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eventricular aneurysm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30(12.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(22.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e19(38.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(13.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e26(12.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\"\u003e\u003csup\u003e*\u003c/sup\u003e: p\u0026lt;0.05. CMR: cardiac magnetic resonance, CI: cardiac index, LV: left ventricle, LVMI: LV mass index, LVEDVI: LV end-diastolic volume index, LVESVI: LV end-systolic volume index, LVEF:LV functional parameters, Es: LA storage period strain, Ee: LA conduit period strain, Ea: LA contraction period strain, LACI: left atrio-ventricular coupling index, IC: infarct core, BZ: border zone, IBZ: IC\u0026thinsp;+\u0026thinsp;BZ, GRS: global radial strain, GCS: global circumferential strain, GLS: global longitudinal strain, EAT: epicardial adipose tissue, RV: right ventricle, MVO: microvascular obstruction.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eEAT and myocardial fibrosis\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the correlations between EAT entropy and myocardial fibrosis. Notably, IC%, BZ%, and IBZ% exhibited modest correlations with EAT entropy (r\u0026thinsp;=\u0026thinsp;0.23, 0.27, and 0.26, respectively, all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Conversely, other EAT parameters showed no significant correlations with myocardial fibrosis (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eArrhythmias prediction\u003c/h2\u003e\n \u003cp\u003eROC curve analysis (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) identified multiple predictors of VAs: LVEF, LA and LV strain, LACI, LGE extent, total and RV EAT volume, EAT thickness, and EAT entropy (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). For AAs, significant predictors included LA strain, LACI, total LV and RV EAT volumes, EAT thickness, and EAT entropy(all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Kaplan-Meier analysis using optimal EAT entropy thresholds determined by Youden Index significantly improved prognostic risk stratification in high-risk patients with both VAs and AAs (both p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Figure. 3).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eROC analysis results of arrhythmias prediction\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eVentricular arrhythmias\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAatrial arrhythmias\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLVEF, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.252\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEs, % (reservoir)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEe, % (conduit)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEa, % (contraction)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLACI, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIC, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.569\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBZ, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.599\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIBZ, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.798\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRS, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.243\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGCS, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGLS, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal EAT volume, cm\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEAT thickness, mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLV EAT volume, cm\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRV EAT volume, cm\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEAT entropy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\u003csup\u003e*\u003c/sup\u003e: p\u0026lt;0.05. LV: left ventricle, LVEF:LV functional parameters, Es: LA storage period strain, Ee: LA conduit period strain, Ea: LA contraction period strain, LACI: left atrio-ventricular coupling index, IC: infarct core, BZ: border zone, IBZ: IC\u0026thinsp;+\u0026thinsp;BZ, GRS: global radial strain, GCS: global circumferential strain, GLS: global longitudinal strain, EAT: epicardial adipose tissue, RV: right ventricle.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eIn the univariable Cox proportional regression analysis, LVEF, LA and LV strain, LGE extent, total and RV EAT volume, EAT thickness, and EAT entropy emerged as significant predictors of VAs (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Similarly, LA strain, LACI, total LV and RV EAT volume, EAT thickness, and EAT entropy were identified as univariable predictors of AAs (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In the multivariable stepwise regression analysis, EAT entropy, Es, IC%, and GLS were selected as independent predictors for VAs (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) after excluding other parameters (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). EAT entropy, Es, and EAT thickness were identified as independent predictors for AAs (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eUnivariable and multivariable regression to identify variables associated with arrhythmias\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eVentricular arrhythmias\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eAatrial arrhythmias\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnadjusted Hazard Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdjusted Hazard Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnadjusted Hazard Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdjusted Hazard Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLVEF, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.969(0.949,0.990)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.015(0.989,1.041)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEs, % (reservoir)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.912(0.872,0.955)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.928(0.881,0.977)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.883(0.833,0.937)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.872(0.809,0.939)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEe, % (conduit)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.896(0.842,0.953)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.855(0.789,0.926)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEa, % (contraction)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.893(0.832,0.957)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.846(0.772,0.926)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLACI, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.017(0.996,1.039)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.030(1.003,1.058)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.032\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIC, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.087(1.049,1.126)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.049(1.008,1.093)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.020\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.987(0.947,1.029)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBZ, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.151(1.076,1.230)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.014(0.936,1.098)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIBZ, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.065(1.038,1.093)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.994(0.966,1.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRS, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.944(0.911,0.978)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.024(0.990,1.058)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGCS, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.097(1.033,1.164)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.941(0.873,1.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGLS, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.209(1.110,1.316)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.134(1.016,1.265)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.025\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.937(0.852,1.030)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal EAT volume, cm\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.020(1.003,1.036)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.017\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.037(1.016,1.059)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEAT thickness, mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.265(1.074,1.490)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.565(1.270,1.929)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.455(1.140,1.856)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLV EAT volume, cm\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.015(0.981,1.050)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.073(1.028,1.121)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRV EAT volume, cm\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.032(1.009,1.056)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.040(1.012,1.068)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEAT entropy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.033(1.021,1.0440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.033(1.020,1.046)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.028(1.015,1.041)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.026(1.011,1.041)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003e\u003csup\u003e*\u003c/sup\u003e: p\u0026lt;0.05. LV: left ventricle, LVEF:LV functional parameters, Es: LA storage period strain, Ee: LA conduit period strain, Ea: LA contraction period strain, LACI: left atrio-ventricular coupling index, IC: infarct core, BZ: border zone, IBZ: IC\u0026thinsp;+\u0026thinsp;BZ, GRS: global radial strain, GCS: global circumferential strain, GLS: global longitudinal strain, EAT: epicardial adipose tissue, RV: right ventricle.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eFurthermore, we fitted the regression models and constructed time-dependent ROC curves (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) to evaluate the predictive efficiency of the models more comprehensively. Excellent predictive performance was demonstrated in both regression models for VAs (AUC: 0.881, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and AAs (AUC: 0.879, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). For VAs, the predictive performance improved progressively with the extension of follow-up time. In contrast, the predictive performance of the regression model for AAs was better for follow-up periods less than 30 months compared to longer durations.\u003c/p\u003e\n \u003cp\u003ePatients were stratified into low-/high-entropy EAT groups (median cutoff) to evaluate arrhythmia susceptibility. High-entropy patients exhibited significantly increased scar burden (larger IC, BZ, and IBZ; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), greater MVO prevalence, and higher STEMI proportion (Tables S1,S2), indicating more severe pathology. They also demonstrated impaired left atrial function and atrioventricular mechanical discoordination (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Collectively, these findings suggest EAT entropy elevation expands the proarrhythmic substrate through structural and functional derangements.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eReproducibility\u003c/h2\u003e\n \u003cp\u003eAn inter-observer reliability analysis was conducted on 40 randomly selected patients by two independent observers, along with an intra-observer reliability analysis. The reproducibility of IC%, total EAT volume, EAT thickness, and EAT entropy was confirmed at both the inter-observer and intra-observer levels. The inter-observer ICC was 0.857 for IC%, 0.883 for total EAT volume, 0.903 for EAT thickness, and 0.926 for EAT entropy (all p\u0026lt;0.05). For the intra-observer ICC, the first observer achieved 0.870 for IC%, 0.858 for total EAT volume, 0.892 for EAT thickness, and 0.924 for EAT entropy, while the second observer recorded 0.834 for IC%, 0.873 for total EAT volume, 0.903 for EAT thickness, and 0.931 for EAT entropy (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOur study presents novel predictive models incorporating EAT entropy for arrhythmia risk stratification in post-MI patients. Key findings include: (1). EAT entropy significantly enhanced predictive value for VAs and AAs through stepwise regression analysis. (2).The optimal VA prediction model combined EAT entropy, Es, IC%, and GLS. The optimal AA prediction model integrated EAT entropy, EAT thickness, and Es. (3). EAT entropy showed significant linear correlation with quantified myocardial fibrosis.\u003c/p\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eEAT characteristic parameters in VAs and AAs cohorts\u003c/h2\u003e\u003cp\u003eEAT infiltration into the myocardium can impede cardiac excitation, while the adipokines it secretes regulate myocardial remodeling, making it a potential target for preventing cardiac remodeling and arrhythmias\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. While previous research has primarily focused on EAT volume or thickness, our study is innovative in quantitatively assessing EAT entropy on CMR images. A 2021 study confirmed fibrotic remodeling within EAT under pathological conditions and its association with myocardial fibrosis, revealed the presence of heterogeneity within the EAT\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. As entropy effectively reflects tissue heterogeneity and has been used to assess myocardial scar remodeling post-MI, this study quantified EAT entropy to evaluate its heterogeneity\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Statistical results showed that patients who developed VAs or AAs after MI had significantly higher EAT volume, thickness, and heterogeneity compared to those without arrhythmias. A 2011 study revealed that total EAT volume and thickness were significantly increased in patients with AAs\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. A recent study also showed that EAT thickness was significantly greater in patients with AAs compared to the control group\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Meta-analysis confirmed that EAT volume was greater in patients with AAs compared to the control group\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. These findings are consistent with ours, suggesting that EAT may be involved in the development of AAs.\u003c/p\u003e\u003cp\u003eLimited research focused on the relationship between EAT and VAs. Although extensive data suggest that EAT may play a role in the initiation and propagation of VAs, there is no direct evidence\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. A 2016 study found a strong correlation between EAT and the frequency of ventricular premature beats, suggesting that EAT has the potential to contribute to VAs\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. The presence of EAT and pericardial fat affects the adipocytes within the myocardium, which in turn influences the conduction velocity in the subepicardial myocardium. This lead to the loss of the ventricular action potential conduction gradient, potentially explaining the mechanism by which EAT contributes to VAs\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Our study suggests that there is a statistically significant difference in RV EAT volume between patients with and without VAs, whereas LV EAT volume does not show such a difference. Evidence indicates that patients with ventricular premature beats originating from the LV have increased LV EAT volume, while those with VAs originating from the RV have larger RV EAT volumes. However, since we were unable to accurately determine the origin of VAs, we cannot confirm whether this result is due to the predominance of RV-originating VAs in our study cohort\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eEAT entropy and myocardial fibrosis\u003c/h2\u003e\u003cp\u003eLGE can using for predicting VAs in post-MI patients by identifying myocardial fibrosis. Based on the degree of ischemic damage and histological characteristics, fibrosis scar can be further categorized into IC and BZ. Previous studies have regarded the BZ as a key region for the development of VAs\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. EAT can directly infiltrate the myocardium, causing dysfunction of myocardial cells, promoting myocardial fibrosis, and leading to structural changes and functional disturbances in the myocardial cells\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. EAT volume is independently associated with increased myocardial fibrosis\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. A 2020 study conducted histological analysis of the right atrial appendage in post-MI patients to characterize EAT and atrial fibrosis. The results showed that a larger EAT volume was clinically associated with slower myocardial conduction and increased fibrosis, which demonstrated that excessive EAT exacerbates atrial myocardial fibrosis and disrupts conduction between myocardial cells\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Abnormal deposition of EAT, through excessive secretion of inflammatory cytokines, vascular endothelial growth factors, and matrix metalloproteinases, excessively regulates extracellular matrix activity, promotes collagen deposition, and ultimately leads to atrial myocardial fibrosis\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. A 2021 study proved that EAT volume was higher in MI patients compared to healthy controls, and EAT volume was correlated with the extent of ventricular myocardial fibrosis\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Hao et al. observed a significant increase in EAT in a MI rat model and found a positive correlation between EAT mass and the extent of ventricular myocardial fibrosis measured at 2 and 4 weeks after MI\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.While EAT's relationship with myocardial fibrosis after MI remains unquantified by CMR, our study is the first to establish this correlation using entropy-based tissue heterogeneity assessment. This method objectively quantifies pixel signal distribution on MR images and has been demonstrated to complement CMR diagnosis in ischemic and non-ischemic cardiomyopathy\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eEAT entropy shows a linear correlation with IC%, BZ%, and IBZ%, whereas other EAT parameters do not demonstrate significant correlations with fibrosis. This indicates that, compared to volume and thickness, heterogeneity may have a deeper intrinsic connection with fibrosis. Given the clear association between scar burden and the incidence of VAs, it is possible that both factors jointly contribute to the development of VAs. However, due to the limited number of cases in this study, further research is needed to draw more conclusive and guiding conclusions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eRisk stratification models for VAs and AAs in post-MI patients\u003c/h2\u003e\u003cp\u003e Current guidelines for predicting VAs in post-MI patients rely on reduced LVEF. However, the accuracy and specificity of this criterion are not ideal\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Research focusing on AAs prediction post-MI remains limited, with underlying pathological mechanisms incompletely defined. As VA and AA risk stratification involves complex multifactorial interactions, we integrated comprehensive clinical and CMR-derived parameters\u0026mdash;including scar heterogeneity, functional indices, and EAT entropy\u0026mdash;to develop predictive models using stepwise regression analysis. The final model for predicting VAs included EAT entropy, Es, IC%, and GLS, while the model for predicting AAs included EAT entropy, EAT thickness, and Es. Both models demonstrated high predictive efficacy, confirming that CMR-quantified EAT entropy has significant additional predictive value for both AAs and VAs in post-MI patients beyond conventional parameters. When EAT accumulates abnormally, it releases a large number of exosomes, which act on coronary arteries and myocardial cells through paracrine and vascular secretion mechanisms\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Changes in the composition of adipose tissue and stroma within EAT may indicate abnormal activation processes. On CMR images, EAT typically appears as a nearly homogeneous high-signal tissue on visual evaluation. The value of entropy quantifies the degree of variation in pixel signals, enabling the detection of signal heterogeneity. In contrast to static anatomical metrics, EAT entropy captures dynamic inflammatory-adipogenic activity. This novel biomarker may resolve clinical dilemmas where current parameters yield ambiguous risk profiles - particularly arrhythmogenic remodeling post-MI. The results of this study demonstrate the superior prognostic value of EAT entropy in post-MI patients. For ICD decision-making, we propose EAT entropy could refine guidelines in two domains: a. for preserved LVEF (\u0026gt;\u0026thinsp;35% )\u0026thinsp;+\u0026thinsp;low EAT entropy, ICD implantation is not considered; b. for decreased LVEF\u0026thinsp;+\u0026thinsp;high EAT entropy, warranting prophylactic ICD per shared decision-making. However, such decisions must be grounded in a comprehensive individualized evaluation of the patient before finalization.\u003c/p\u003e\u003cp\u003eConsistent with our study, Muhib et al. showed CMR-measured EAT volume increase predicts AAs in hypertrophic cardiomyopathy, independent of conventional risk factors\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. A meta-analysis further confirmed the association between increased EAT thickness and volume with AAs\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Maryam et al. demonstrated that an increase in EAT volume is associated with a higher risk of VAs in patients with non-ischemic cardiomyopathy\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. In a study of patients with idiopathic VT and no structural heart disease, EAT volume was identified as an independent predictor of VT recurrence\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. These studies confirm that EAT plays a role in the occurrence of both atrial and ventricular arrhythmias.\u003c/p\u003e\u003cp\u003eNotably, our VAs prediction model gained accuracy with prolonged follow-up, whereas the AAs model peaked before 30 months\u0026mdash;potentially reflecting EAT-driven fibrofatty replacement inducing delayed activation and pro-arrhythmic electrophysiological remodeling during inflammatory progression\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. EAT primarily distributes along the atrioventricular groove and the interventricular septum, extending along the branches of the coronary arteries, including the circumflex and left anterior descending arteries, and surrounding the atrium\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Given that the mechanisms of EAT primarily involve local infiltration, EAT may affect atrial myocardial fibrosis earlier. Although EAT can also impact ventricular myocardial fibrosis, this process likely requires a longer duration of influence. It may help explain the results of our study, but further evidence is needed to support this hypothesis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eStudy limitations\u003c/h2\u003e\u003cp\u003eAs a single-center cohort study without external validation, generalizability is limited. While arrhythmia outcomes were predefined in the original observational registry, current CMR technology precluded left atrial fibrosis quantification\u0026mdash;a key substrate for atrial arrhythmogenesis. Future studies employing 3D-LGE imaging could further explore the relationship between left atrial fibrosis and EAT. Further analysis could focus separately on persistent arrhythmias.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe present study is the first to quantify EAT heterogeneity using CMR images and to explore its association with VAs and AAs after MI, and we developed prediction models and evaluated their performance over different follow-up periods. Additionally, we proved the relationship between CMR-derived EAT heterogeneity and myocardial fibrosis in post-MI patients. Our data suggest that EAT heterogeneity can offer valuable prognostic and predictive information for post-MI patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eBasic Research Program Supported by Yunnan Fundamental Research Kunming Medical University Joint Projects \u0026nbsp; \u0026nbsp; 202501AY070001-103\u003c/li\u003e\n \u003cli\u003eYunnan Science and Technology Platform and Talent Project (Academician Expert Workstation) \u0026nbsp; \u0026nbsp; 202305AF150033\u003c/li\u003e\n \u003cli\u003eInnovation Fund for Doctoral Education of Kunming Medical University in 2025 \u0026nbsp; \u0026nbsp; 2025B027\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eYunnan medical and health personnel special \u0026lsquo;Xing Dian talent\u0026rsquo; plan \u0026nbsp; XDYC-YLWS-2023-0022\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eIRB Information\u003c/p\u003e\n\u003cp\u003eThe investigation of present study conforms with the principles outlined in the Declaration of Helsinki and was approved by Ethics Committee of the Second Affiliated Hospital of Kunming Medical University (reference number: 审-PJ-科-2023-30).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eData Availability Statement\u003c/p\u003e\n\u003cp\u003eThe deidentified participant data will be shared on a request basis. Please directly contact the corresponding author to request data sharing.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHall Trygve S, von Lueder, Thomas G, Rossignol P, Duarte K, Chouihed T, et al. Relationship between left ventricular ejection fraction and mortality after myocardial infarction complicated by heart failure or left ventricular dysfunction[J]. \u003cem\u003eINT J CARDIOL\u003c/em\u003e 2018; 272(11):260-266. \u003c/li\u003e\n\u003cli\u003eWang Y, Li Q, Tao B, Angelini M, Ramadoss S, Sun B, et al. Fibroblasts in heart scar tissue directly regulate cardiac excitability and arrhythmogenesis[J]. \u003cem\u003eSCIENCE\u003c/em\u003e 2023; 381(6665):1480-1487.\u003c/li\u003e\n\u003cli\u003eChahine Y, Chamoun N, Kassar A, Lee B, Fima M, Nazem A. 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Reactivation of the epicardium at the origin of myocardial fibro-fatty infiltration during the atrial cardiomyopathy. \u003cem\u003eCirc Res\u003c/em\u003e 2020;126:1330\u0026ndash;1342.\u003c/li\u003e\n\u003cli\u003eMarchington JM, Mattacks CA, Pond CM. Adipose tissue in the mammalian heart and pericar-dium: structure, foetal development and biochemical properties. \u003cem\u003eComp Biochem Physiol B\u003c/em\u003e 1989;94:225\u0026ndash;232.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Epicardial adipose tissue, Arrhythmias, Myocardial infarction, Myocardial fibrosis, Risk stratification","lastPublishedDoi":"10.21203/rs.3.rs-7531155/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7531155/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eEpicardial adipose tissue (EAT) mediate both electrophysiological disturbances and structural remodeling within substrates. Fibrotic remodeling within EAT under pathological conditions revealed the presence of heterogeneity.\u003c/p\u003e\u003ch2\u003eMethods and Results\u003c/h2\u003e\u003cp\u003eThe cohort study included 241 consecutive post-myocardial infarction (MI) patients, 49 experienced ventricular arrhythmias (VAs) VAs and 30 experienced atrial tachyarrhythmias (AAs) during the follow-up period. EAT volume, myocardial scar, functional and strain parameters were obtained using CVI42 workstation. EAT heterogeneity was calculated using the entropy formula in Python. Patients in the VAs(+) group showed impaired cardiac pumping function, reduced left ventricular (LV) strain, and a greater extent of myocardial fibrosis. Similarly, patients with elevated left atrial (LA) strain, left atrioventricular coupling index (LACI), total EAT volume, right ventricular (RV) EAT volume, and EAT entropy were more likely to develop AAs. Myocardial fibrosis exhibited modest correlations with EAT entropy. Multivariable stepwise regression models identified EAT entropy, LA storage period strain (Es), infarct core (IC) percentage, and global longitudinal strain (GLS) as independent predictors of VAs. EAT entropy, Es, and EAT thickness were predictors of AAs. Time-dependent receiver operating characteristic (ROC) curves demonstrated that the predictive performance for VAs improved progressively with longer follow-up durations.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eCMR-quantified EAT entropy is a significant indicator for predicting VAs and AAs after MI and shows a linear correlation with myocardial fibrosis.\u003c/p\u003e","manuscriptTitle":"CMR—Quantified Epicardial Adipose Tissue Heterogeneity and Its Predictive Value for Ventricular and Atrial Arrhythmias After Myocardial Infarction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-17 12:28:04","doi":"10.21203/rs.3.rs-7531155/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-05T15:14:54+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-19T21:39:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-15T10:15:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"315526727853475789460044488845870246527","date":"2025-10-15T08:18:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"330383973783947901226883588106789070912","date":"2025-10-08T21:41:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-06T11:29:22+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-08T14:36:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-05T10:05:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-05T10:05:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Imaging","date":"2025-09-04T02:07:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"49aa6b02-ff59-4992-9cc6-04a8f382cde6","owner":[],"postedDate":"October 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-16T16:01:24+00:00","versionOfRecord":{"articleIdentity":"rs-7531155","link":"https://doi.org/10.1186/s12880-026-02228-z","journal":{"identity":"bmc-medical-imaging","isVorOnly":false,"title":"BMC Medical Imaging"},"publishedOn":"2026-02-14 15:58:18","publishedOnDateReadable":"February 14th, 2026"},"versionCreatedAt":"2025-10-17 12:28:04","video":"","vorDoi":"10.1186/s12880-026-02228-z","vorDoiUrl":"https://doi.org/10.1186/s12880-026-02228-z","workflowStages":[]},"version":"v1","identity":"rs-7531155","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7531155","identity":"rs-7531155","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}