Evaluation of Extracellular Volume by Computed Tomography is Useful for Prediction of Prognosis in Dilated Cardiomyopathy

Evaluation of Extracellular Volume by Computed Tomography is Useful for Prediction of Prognosis in Dilated Cardiomyopathy | 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 Evaluation of Extracellular Volume by Computed Tomography is Useful for Prediction of Prognosis in Dilated Cardiomyopathy Satomi Yashima, Hiroyuki Takaoka, Manami Takahashi, Makiko Kinoshita, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1149072/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose: Dilated cardiomyopathy (DCM) is commonly encountered in daily clinical practice, and screening for coronary artery disease and other cardiomyopathies is necessary for its diagnosis. Cardiac CT is useful for the screening of coronary artery stenosis, and extracellular volume fraction (ECV) analysis by CT has become available using new specific software. Here, we evaluated the utility of ECV analysis using cardiac CT to predict patient prognosis in cases with DCM. Methods: We analyzed 70 cases with DCM and coronary computed tomography (CT) with available late-phase images. We evaluated the ECV of the left ventricular myocardium (LVM) using commercially available software (Ziostation 2, Ziosoft Inc, Japan). Results: ECV on LVM was 34.5±4.9%. Major adverse cardiac events (MACE) occurred in 20 cases (29%). ECV of the LVM on CT and the presence of significant valvular disease were significantly higher in cases with MACE than in those without (37.6±5.9 vs 33.2±3.9% and 55% vs 24%, P=0.0057 and P=0.013). LVEF was significantly lower in cases with MACE than in those without (22.3±7.6 vs 30.8±11.8%, P=0.0008). The best cut-off value of ECV on LVM for prediction of MACE was 32.7% based on receiver operating characteristics analysis. Cases with ECV ≥32.7% had significantly higher MACE based on Kaplan-Meier analysis (P=0.012). Only ECV on LVM was an independent predictor of MACE based on a Cox proportional hazards model (P=0.028). Conclusion: Evaluation of ECV on LVM by CT is useful for predicting MACE in patients with DCM. Cardiac & Cardiovascular Systems Nuclear Medicine & Medical Imaging dilated cardiomyopathy extracellular volume echocardiography computed tomography Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Dilated cardiomyopathy (DCM) is commonly encountered in daily clinical practice. It is the third-most common reason for heart failure and has an estimated prevalence of one in 2500 in the average population ( 1 ). Diagnosis is dependent on screening for coronary artery disease, other cardiomyopathies and other conditions causing abnormal loading, including valvular heart disease and hypertension ( 1 ) ( 2 ) ( 3 ). The gold standard for evaluation of myocardial damage is late enhancement analysis using cardiac magnetic resonance imaging (MRI). The pattern of late gadolinium enhancement (LGE) is useful in the differential diagnosis of several types of cardiomyopathies 4). Linear mid-layer LGE is the typical pattern in patients with DCM and the presence of the LGE has been regarded as a sign of worse prognosis in these patients ( 5 ) ( 6 ). Of interest, extracellular volume fraction (ECV) analysis using T1 mapping images on MRI is also useful for quantitative analysis of myocardial damage. This analysis requires gadolinium contrast ( 7 ). ECV provides significant prognostic information in DCM, and patients with adverse events have significantly higher ECV than those without 8). Cardiac MRI is sometimes contraindicated in patients with cardiovascular disease, because of the presence of cardiac mechanical devices, claustrophobia or severe renal dysfunction, and obtaining clear LGE images in cases with arrhythmia is often difficult ( 9 ). Cardiac computed tomography (CT) is useful for screening coronary artery stenosis in patients suspected of having myocardial disease ( 10 ), and obtaining clear cardiac images is easy even in cases with arrhythmia ( 11 ). Additional late phase scan is also helpful for detecting myocardial damage as late enhancement ( 12 ). New software has also now made ECV analysis on CT available, and ECV values on CT are similar to those on MRI ( 13 ). The purpose of this study was to evaluate the utility of ECV analysis on CT in predicting major adverse cardiac events (MACE) in patients with DCM. Methods Ninety-three patients diagnosed with DCM underwent coronary computed tomography (CT), including late-phase acquisition in our institution from Dec 2008 to Feb 2021. However, ECV analysis was impossible because of the significant metallic artifacts of pacemaker leads in 4 patients, significant gaps of the cardiac phases between the non-contrast and late phase cardiac images in 2 patients, and the different tube voltages between the non-contrast and late phase cardiac images in 17 patients. The study was conducted under a retrospective design in the remaining 70 consecutive patients with DCM. Written informed consents were obtained from all patients. All patients had a lower left ventricular (LV) ejection fraction (LVEF) less than 45%, and they were finally diagnosed with DCM based on the screening for coronary artery disease, other cardiomyopathies, and other conditions causing abnormal loading, including primary valvular heart disease and hypertension ( 2 ). Major adverse cardiac events (MACE) were defined as a composite of cardiovascular death; fatal arrhythmic events, including ventricular tachycardia or fibrillation; stroke; and hospitalization due to heart failure. Patient background, including risk factors for coronary artery disease and medical treatment, were obtained from medical records (Table 1 ). Table 1 Details of patient background N=70 Age, years 58 ± 14 Male, n (%) 52 (74) Hypertension, n (%) 30 (43) Dyslipidemia, n (%) 22 (31) Diabetes Mellitus, n (%) 15 (22) Atrial fibrillation, n (%) 14 (20) Administration of β-blocker, n (%) 44 (63) Administration of ARB, n (%) 24 (34) Administration of ACE-I, n (%) 23 (33) Administration of MRB (%) 27 (39) Administration of statin, n (%) 20 (29) Administration of SGLT-2, n (%) 0 (0) ACE, angiotensin-converting enzyme; ARB, angiotensin receptor II blocker; SGLT-2, sodium-dependent glucose cotransporter 1; MRB, mineralocorticoid receptor antagonists Protocol for computed tomography CT was performed using a 320-slice CT (Aquilion One or Aquilion One/ViSion Edition, Canon Medical Systems, Otawara, Japan) or 256-slice CT (Revolution CT, GE Healthcare, GE Healthcare, Milwaukee, Wis), with patients lying supine on the scanner table. A scout scan and a non-contrast ECG-gated cardiac scan were performed using a prospective ECG-gated technique before contrast scan. Slice thickness and tube voltage was 0.5 mm and 80-120 kV for 320-slice CT, and 0.625 mm and 70 kV for 256-slice CT, respectively (Table 1 ) For retrospective ECG gating, performed using the dose modulation technique to decrease radiation dose during the systolic phases where possible, conventional enhanced CT was performed with a slice thickness and tube voltage of 0.5 mm and 80-120 kV for 320-slice CT and 0.625 mm and 120 kV for 256-slice CT, respectively (Table 1 ) ( 14 ). Tube current at scanning was determined based on the auto exposure control system with slight manual modification. All patients with a heart rate ≥65 beats per minute received 10 mg of propranolol or 12.5 mg landiolol prior to scanning, except for those in whom β-blockers were contraindicated. Just prior to the scanning procedure, subjects were administered two doses of isosorbide dinitrate sublingually. For contrast material injection, we employed a routine triphasic protocol. Right or left antecubital intravenous access using a 20- or 22-gauge needle was attained, and the system was connected to a dual-syringe injector with a dual-flow option (Dual Shot, Nemoto, Tokyo, Japan). During the first phase, we injected 50–70 ml of undiluted iodinate contrast agent (350-370mg/mL) at 3-4 ml/s, followed by 40-50 ml of a 50%/50% saline-to-contrast material mixture at 3-4 ml/s and 20 ml of pure saline at 4 ml/s. A late phase scan was added 6 minutes after the injection of iodine contrast media using the prospective ECG-gating technique, slightly modified from similar previous research ( 15 ) (Figure 1 ). CT was performed with a slice thickness and tube voltage of 0.5 mm and 80-120 kV for 320-slice CT and 0.625 mm and 70 kV for 256-slice CT, respectively (Figure 1 ) (same tube voltage as for the non-contrast scan). Analysis of ECV on CT Myocardial ECV of the left ventricular myocardium (LVM) was measured using commercially available software (Ziostation 2, Ziosoft Inc, Japan) with the following equation: ECV= (ΔHUm/ΔHUb)/(1 − Hct), where ΔHUm is change in myocardial CT attenuation in Hounsfield units (HU), ΔHUb is change in CT attenuation of the blood, and Hct is hematocrit ( 15 ) (Figure 2 ). This software performs automatic three-dimensional non-rigid registration of the myocardium between non-contrast and late phase CT images to generate subtraction images ( 16 ). The change in CT attenuation (ΔHU) is then obtained on the subtraction image. The software produces a polar map showing both 16 American Heart Association myocardial segments with the mean ECV value for each segment and the mean ECV value of all LVM. ECV of LVM was measured by two cardiologists (SA and YN). The effective dose for scanning of coronary arteries was calculated from the dose-length product in a dose report (conversion factor 0.014) ( 17 ). Statistical analysis Continuous variables are expressed as the mean ± SD or as median (interquartile range) if not normally distributed. Categorical variables are reported as counts and percentages. All tests were 2-sided, and p values <0.05 were considered to indicate statistical significance. Analysis of variance or chi-square tests were used to compare baseline characteristics. Interobserver agreement over the presence of significant coronary artery stenosis was assessed using correlation coefficients and compared using chi-square tests. All statistical analyses were performed using the JMP software program, version 15.0.0 (SAS Institute Inc, Cary, NC, USA). Results Patients were followed for 53 ± 44 months after the performance of cardiac CT. Among the 20 patients (28.6%) who experienced MACE during the follow-up period, the number of patients taking mineralocorticoid receptor antagonists (MRB) was significantly higher than the number not taking these agents (60% vs 30%, P=0.020). (Table 2 ). ECV of the LVM on CT was significantly higher in cases with MACE than in those without MACE (37.6±5.9 vs 33.2±3.9%, P=0.0057). The presence of significant valvular disease (≥moderate) was significantly higher in cases with MACE than in those without MACE (55% vs 24%, P=0.013). LVEF was significantly lower in cases with MACE than in those without MACE (22.3±7.6 vs 30.8±11.8%, P=0.0008). Table 2 Comparison of patient background between patients with and without major adverse cardiac events MACE (+) (N=20) MACE (-) (N=50) P-value Age, years 57 ± 12 58 ± 14 0.95 Male, n (%) 16 (80) 36 (72) 0.49 Hypertension, n (%) 6 (30) 24 (48) 0.17 Dyslipidemia, n (%) 5 (25) 17 (34) 0.46 Diabetes Mellitus, n (%) 2 (10) 13 (27) 0.13 Atrial fibrillation, n (%) 4 (20) 10 (20) 1.0 Administration of β-blocker, n (%) 13 (65) 31 (62) 0.81 Administration of statin, n (%) 6 (30) 14 (28) 0.87 Administration of ACE-i or ARB, n (%) 13 (65) 33 (66) 0.94 Administration of MRB, n (%) 12 (60) 15 (30) 0.020* Follow-up period (months) 46 ± 34 55 ± 47 0.34 ACE, angiotensin converting enzyme; ARB, angiotensin receptor II blocker; MRB, mineralocorticoid receptor antagonists Table 3 Comparison of parameters on TTE and CT between patients with and without major adverse cardiac events MACE (+) (N=20) MACE (-) (N=50) P-value LVEF on TTE (%) 22 ± 8 31 ± 12 0.0008* Significant valvular disease (≥moderate) on TTE, n (%) 11 (55%) 12 (24%) 0.013* ECV of LVM on CT 38 ± 5.9 33 ± 3.9 0.0057* LVEF, left ventricular ejection fraction; LVM, left ventricular myocardium; TTE, transthoracic echocardiography; ECV, extracellular volume fraction The best cut-off value of ECV on LVM for prediction of MACE was 32.7% based on receiver operating characteristics (ROC) analysis. The area under the curve (AUC) of the ROC curve was 0.751 (P=0.0034) (Figure 3 A). Sensitivity and specificity for prediction of future MACE were 90% and 54%, respectively (Figure 3 A). The best cut-off value for left ventricular ejection fraction for prediction of MACE was 24% based on ROC analysis. The area under the curve of the receiver operating characteristics curve was 0.718 (P=0.0026). Sensitivity and specificity for prediction of future MACE were 75% and 68%, respectively. (Figure 3 B) Cases with ECV ≥32.7% had significantly higher MACE than those with <32.7% during the follow-up period based on Kaplan-Meier analysis (Figure 4 ). The consistency of ECV of LVM on CT between the two observers was 0.85. A univariate Cox proportional hazards model showed that significant valvular disease (≥moderate) on TTE, LVEF on TTE ≤ 24%, administration of MRB and ECV of LVM on CT ≥ 32.7% were significant risk factors for MACE (P=0.025, 0.0089, 0.018 and 0.0044) (Table 4 ). A multivariate Cox proportional hazard model was also performed, and ECV of LVM on CT ≥ 32.7% was the only independent significant predictor of MACE during the follow-up period (P=0.78, 0.15, 0.49 and 0.028) (Table 5 ). The effective radiation dose for additional late phase scan was 3.7±0.4mSv (radiation dose for the first 8 cases was not recorded, and these were excluded from this analysis). Table 4 Hazard ratio 95% Confidence interval P value Significant valvular disease (≥moderate) on TTE, n (%) 2.76 1.13 – 6.73 0.025* LVEF on TTE ≤ 24% (%) 3.88 1.41 – 10.7 0.0089* Administration of MRB, n (%) 2.99 1.21 – 7.38 0.018* ECV on LVM ≥ 32.7% (%) 8.42 1.94 – 36.5 0.0044* TTE, transthoracic echocardiography; LVEF, left ventricular ejection fraction; MRB, mineralocorticoid receptor antagonists; ECV, extra-cellular volume fraction; LVM, left ventricular myocardium Table 5 Hazard ratio 95% Confidence interval P value Significant valvular disease (≥moderate) on TTE, n (%) 1.15 0.43 – 3.1 0.78 LVEF on TTE ≤24% (%) 2.29 0.74 – 7.1 0.15 Administration of MRB, n (%) 1.41 0.53 – 3.8 0.49 ECV on LVM ≥32.7% (%) 5.62 1.20 – 26.3 0.028* TTE, transthoracic echocardiography; LVEF, left ventricular ejection fraction; MRB, mineralocorticoid receptor antagonists; ECV, extra-cellular volume fraction; LVM, left ventricular myocardium Discussion The results of this study suggest that ECV of LVM on CT might be a predictor of future MACE in patients with DCM. CT is useful for the detection of coronary artery stenosis, and ECV analysis is also feasible if an additional late phase scan is performed. From these findings, CT appears to be a useful modality for whole cardiac screening in patients with DCM. ( 6 ) ECV analysis in cases on DCM The presence of LGE is a marker of higher risk of future cardiac events in patients with DCM, because myocardial damage is a marker of low cardiac function and fatal arrhythmia ( 5 ) ( 6 ). However, almost two-thirds of patients with DCM do not have LGE, because the evaluation of LGE is a qualitative analysis of focal fibrosis, and diffuse myocardial fibrosis is difficult to detect as LGE. Recently, ECV measurement using T1 mapping on MRI has become available for the prediction of future MACE ( 7 ). MRI is sometimes contraindicated in cases with DCM because of the presence of mechanical devices or claustrophobia, and gadolinium contrast is also contraindicated in cases with renal dysfunction ( 9 ). Increased ECV on LVM is almost equal to the higher amount of biopsy-proven myocardial fibrosis ( 16 ); accordingly, higher ECV means severe degeneration of LVM, which leads to lower LV function or ventricular arrhythmic events ( 8 ). The amount of myocardial fibrosis is a significant risk factor of future cardiac events in several myocardial diseases ( 5 ) ( 6 ) ( 8 ). Additional radiation dose for late enhancement analysis on CT Additional radiation dose is necessary for ECV analysis on CT but is a tradeoff for the important clinical information obtained. The effective dose for the late-phase scan in this study was 3.7±0.4 mSv, which is smaller than the effective dose for chest CT for evaluation of the lung (almost 5mSv) ( 17 ). The radiation dose for late phase cardiac images has recently decreased following the introduction of new iterative reconstruction techniques and wide coverage multi-detector CTs, and the image quality of late-phase cardiac images has improved ( 11 ). These changes have ameliorated the disadvantages of additional late phase cardiac imaging, and late phase cardiac scan should be recommended in cases with DCM. Improvement in image quality of late enhancement Contrast resolution of late phase cardiac images on CT is inferior to MRI, and MRI remains the gold standard modality for evaluation of myocardial damage on late phase images. Although CT attenuation value increases when a CT scan is performed using a lower tube voltage, image noise increases owing to the limited radiation dose ( 18 ). Recently, however, newer iterative reconstruction techniques have appeared, and the maximum tube current of CT scanners has increased. They help decrease image noise in images acquired using a lower tube voltage. We previously reported that the combination of new-generation CT and the iterative reconstruction technique is useful for improving the image quality of late enhancement on CT and higher diagnostic accuracy in the detection of myocardial fibrosis ( 12 ). Of note, the improvement in image quality in late enhancement of LVM has resulted in the approval of ECV analysis using CT as a substitute for MRI in the latest guidelines for cardiac CT and cardiac amyloidosis from the Japanese Cardiovascular Society ( 19 ) ( 20 ). Limitations Several limitations of our study warrant mention. First, the study was conducted under a retrospective design at a single center. Second, ECV analysis was performed on single-energy images, and subtraction of late phase and non-contrast images was therefore necessary. Gaps between images in these 2 phases might cause under- or overestimation of ECV on single-energy images compared with analysis of dual-energy images using the latest CT scanners (without gaps). Finally, the study was conducted using different CT scanners, and difference between them may have affected the results. Conclusion Evaluation of ECV by CT is useful for the prediction of MACE in patients with DCM. Declarations Acknowledgements : None Authors’ contributions: SY and HT (Conception of hypothesis, data analysis, writing of manuscript), MT and YN (data analysis, critical revision), MK (critical revision), HS (critical revision), N-SE (critical revision), JO (critical revision), HG (critical revision), YK (critical revision) Funding: This work was partially supported by the TSUCHIYA MEMORIAL MEDICAL FOUNDATION (Grant no. J17KF00167). Data availability : Because of the sensitive nature of the data collected for this study, the data will not be made publicly available Disclosure: All authors have no conflict of interest related to this article. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1149072","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":68948297,"identity":"587c427f-30e3-4966-94b3-85ea299b0c40","order_by":0,"name":"Satomi Yashima","email":"","orcid":"","institution":"Chiba University Graduate School of Medicine School of Medicine: Chiba Daigaku Daigakuin Igaku Kenkyuin 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Igakubu","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Hiroyuki","middleName":"","lastName":"Takaoka","suffix":""},{"id":68948299,"identity":"3f174f48-7526-4f32-af3f-96d93e96340e","order_by":2,"name":"Manami Takahashi","email":"","orcid":"","institution":"Chiba University Graduate School of Medicine School of Medicine: Chiba Daigaku Daigakuin Igaku Kenkyuin Igakubu","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Manami","middleName":"","lastName":"Takahashi","suffix":""},{"id":68948300,"identity":"77e906e9-6570-4a99-b365-fb122d46bf76","order_by":3,"name":"Makiko Kinoshita","email":"","orcid":"","institution":"Chiba University Graduate School of Medicine School of Medicine: Chiba Daigaku Daigakuin Igaku Kenkyuin Igakubu","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Makiko","middleName":"","lastName":"Kinoshita","suffix":""},{"id":68948301,"identity":"6dc29d5a-d8c5-4f73-9adf-e3e79d358a0a","order_by":4,"name":"Haruka Sasaki","email":"","orcid":"","institution":"Chiba University Graduate School of Medicine School of Medicine: Chiba Daigaku Daigakuin Igaku Kenkyuin Igakubu","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Haruka","middleName":"","lastName":"Sasaki","suffix":""},{"id":68948302,"identity":"21fecb28-dedb-450f-a67d-71413f4fa1aa","order_by":5,"name":"Noriko Suzuki-Eguchi","email":"","orcid":"","institution":"Chiba University Graduate School of Medicine School of Medicine: Chiba Daigaku Daigakuin Igaku Kenkyuin Igakubu","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Noriko","middleName":"","lastName":"Suzuki-Eguchi","suffix":""},{"id":68948303,"identity":"8bd689e4-3daf-4882-85b0-d50213696096","order_by":6,"name":"Joji Ota","email":"","orcid":"","institution":"Chiba University Hospital: Chiba Daigaku Igakubu Fuzoku Byoin","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Joji","middleName":"","lastName":"Ota","suffix":""},{"id":68948304,"identity":"b14663aa-ca0e-4ab1-94ee-ad19738c69de","order_by":7,"name":"Yusei Nishikawa","email":"","orcid":"","institution":"Chiba University: Chiba Daigaku","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yusei","middleName":"","lastName":"Nishikawa","suffix":""},{"id":68948305,"identity":"50484484-3cf6-48b5-89d0-85c6b8e22d5a","order_by":8,"name":"Hiroki Goto","email":"","orcid":"","institution":"Chiba University Graduate School of Medicine School of Medicine: Chiba Daigaku Daigakuin Igaku Kenkyuin Igakubu","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hiroki","middleName":"","lastName":"Goto","suffix":""},{"id":68948306,"identity":"a3974844-1f1f-4768-ae3b-6a715a66d8c0","order_by":9,"name":"Yoshio Kobayashi","email":"","orcid":"","institution":"Chiba University Graduate School of Medicine School of Medicine: Chiba Daigaku Daigakuin Igaku Kenkyuin Igakubu","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yoshio","middleName":"","lastName":"Kobayashi","suffix":""}],"badges":[],"createdAt":"2021-12-07 13:07:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1149072/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1149072/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":16350023,"identity":"f86bd5a3-2c9a-44c2-9c30-cc29aca9e1ff","added_by":"auto","created_at":"2021-12-10 15:23:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1748101,"visible":true,"origin":"","legend":"Details of the CT scan protocol","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-1149072/v1/4dc09b56130530e86463c7f6.png"},{"id":16350026,"identity":"a24e0dd9-9ebe-4899-ad87-6573d283e274","added_by":"auto","created_at":"2021-12-10 15:23:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":651258,"visible":true,"origin":"","legend":"Details of ECV analysis on CT","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-1149072/v1/b4c3540016a3dac2dd3ec41b.png"},{"id":16350024,"identity":"3e90d5d4-1deb-4b2e-b982-b88c8aaf0e51","added_by":"auto","created_at":"2021-12-10 15:23:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":254294,"visible":true,"origin":"","legend":"Receiver operating characteristics analysis for prediction of major adverse cardiac events\nThe best cut-off value for extracellular volume fraction (ECV) on the left ventricular myocardium for prediction of major adverse cardiac events (MACE) was 32.7% based on receiver operating characteristics (ROC) analysis (A). The area under the curve of the receiver operating characteristics curve was 0.751 (P=0.0034) (A). The best cut-off value for left ventricular ejection fraction for prediction of MACE was 24% based on ROC analysis (B). The area under the curve of the receiver operating characteristics curve was 0.718 (P=0.0026) (B).\n","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-1149072/v1/ba82b105d62e853a87517338.png"},{"id":16350095,"identity":"17d6fcc5-41f6-4696-865a-840dadf949de","added_by":"auto","created_at":"2021-12-10 15:26:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":135704,"visible":true,"origin":"","legend":"Incidence of major adverse cardiac events during follow-up by Kaplan-Meier analysis of cases with extracellular volume fraction ≥32.7%\nCases with an extracellular volume fraction ≥32.7% had significantly higher major adverse cardiac events during the follow-up period based on the Kaplan-Meier analysis (P=0.0011). \n","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-1149072/v1/46ba00ae7d5a84c9a4880d0f.png"},{"id":17609296,"identity":"bfb3b732-5412-4eb2-850a-75d4102fe6b2","added_by":"auto","created_at":"2022-01-24 20:27:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1582714,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1149072/v1/c800a3bb-752a-40da-b036-65c97cca3550.pdf"}],"financialInterests":"","formattedTitle":"Evaluation of Extracellular Volume by Computed Tomography is Useful for Prediction of Prognosis in Dilated Cardiomyopathy","fulltext":[{"header":"Background","content":"\u003cp\u003eDilated cardiomyopathy (DCM) is commonly encountered in daily clinical practice. It is the third-most common reason for heart failure and has an estimated prevalence of one in 2500 in the average population (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Diagnosis is dependent on screening for coronary artery disease, other cardiomyopathies and other conditions causing abnormal loading, including valvular heart disease and hypertension (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe gold standard for evaluation of myocardial damage is late enhancement analysis using cardiac magnetic resonance imaging (MRI). The pattern of late gadolinium enhancement (LGE) is useful in the differential diagnosis of several types of cardiomyopathies 4). Linear mid-layer LGE is the typical pattern in patients with DCM and the presence of the LGE has been regarded as a sign of worse prognosis in these patients (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOf interest, extracellular volume fraction (ECV) analysis using T1 mapping images on MRI is also useful for quantitative analysis of myocardial damage. This analysis requires gadolinium contrast (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). ECV provides significant prognostic information in DCM, and patients with adverse events have significantly higher ECV than those without 8).\u003c/p\u003e \u003cp\u003eCardiac MRI is sometimes contraindicated in patients with cardiovascular disease, because of the presence of cardiac mechanical devices, claustrophobia or severe renal dysfunction, and obtaining clear LGE images in cases with arrhythmia is often difficult (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Cardiac computed tomography (CT) is useful for screening coronary artery stenosis in patients suspected of having myocardial disease (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), and obtaining clear cardiac images is easy even in cases with arrhythmia (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Additional late phase scan is also helpful for detecting myocardial damage as late enhancement (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). New software has also now made ECV analysis on CT available, and ECV values on CT are similar to those on MRI (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe purpose of this study was to evaluate the utility of ECV analysis on CT in predicting major adverse cardiac events (MACE) in patients with DCM.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eNinety-three patients diagnosed with DCM underwent coronary computed tomography (CT), including late-phase acquisition in our institution from Dec 2008 to Feb 2021. However, ECV analysis was impossible because of the significant metallic artifacts of pacemaker leads in 4 patients, significant gaps of the cardiac phases between the non-contrast and late phase cardiac images in 2 patients, and the different tube voltages between the non-contrast and late phase cardiac images in 17 patients. The study was conducted under a retrospective design in the remaining 70 consecutive patients with DCM. Written informed consents were obtained from all patients. All patients had a lower left ventricular (LV) ejection fraction (LVEF) less than 45%, and they were finally diagnosed with DCM based on the screening for coronary artery disease, other cardiomyopathies, and other conditions causing abnormal loading, including primary valvular heart disease and hypertension (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Major adverse cardiac events (MACE) were defined as a composite of cardiovascular death; fatal arrhythmic events, including ventricular tachycardia or fibrillation; stroke; and hospitalization due to heart failure. Patient background, including risk factors for coronary artery disease and medical treatment, were obtained from medical records (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetails of patient background\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN=70\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58 \u0026plusmn; 14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52 (74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidemia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (31)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes Mellitus, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrial fibrillation, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdministration of β-blocker, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdministration of ARB, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (34)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdministration of ACE-I, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdministration of MRB (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdministration of statin, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (29)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdministration of SGLT-2, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eACE, angiotensin-converting enzyme; ARB, angiotensin receptor II blocker; SGLT-2, sodium-dependent glucose cotransporter 1; MRB, mineralocorticoid receptor antagonists\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eProtocol for computed tomography\u003c/h2\u003e \u003cp\u003eCT was performed using a 320-slice CT (Aquilion One or Aquilion One/ViSion Edition, Canon Medical Systems, Otawara, Japan) or 256-slice CT (Revolution CT, GE Healthcare, GE Healthcare, Milwaukee, Wis), with patients lying supine on the scanner table. A scout scan and a non-contrast ECG-gated cardiac scan were performed using a prospective ECG-gated technique before contrast scan. Slice thickness and tube voltage was 0.5 mm and 80-120 kV for 320-slice CT, and 0.625 mm and 70 kV for 256-slice CT, respectively (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eFor retrospective ECG gating, performed using the dose modulation technique to decrease radiation dose during the systolic phases where possible, conventional enhanced CT was performed with a slice thickness and tube voltage of 0.5 mm and 80-120 kV for 320-slice CT and 0.625 mm and 120 kV for 256-slice CT, respectively (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Tube current at scanning was determined based on the auto exposure control system with slight manual modification. All patients with a heart rate \u0026ge;65 beats per minute received 10 mg of propranolol or 12.5 mg landiolol prior to scanning, except for those in whom β-blockers were contraindicated. Just prior to the scanning procedure, subjects were administered two doses of isosorbide dinitrate sublingually.\u003c/p\u003e \u003cp\u003eFor contrast material injection, we employed a routine triphasic protocol. Right or left antecubital intravenous access using a 20- or 22-gauge needle was attained, and the system was connected to a dual-syringe injector with a dual-flow option (Dual Shot, Nemoto, Tokyo, Japan). During the first phase, we injected 50\u0026ndash;70 ml of undiluted iodinate contrast agent (350-370mg/mL) at 3-4 ml/s, followed by 40-50 ml of a 50%/50% saline-to-contrast material mixture at 3-4 ml/s and 20 ml of pure saline at 4 ml/s.\u003c/p\u003e \u003cp\u003eA late phase scan was added 6 minutes after the injection of iodine contrast media using the prospective ECG-gating technique, slightly modified from similar previous research (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) (Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). CT was performed with a slice thickness and tube voltage of 0.5 mm and 80-120 kV for 320-slice CT and 0.625 mm and 70 kV for 256-slice CT, respectively (Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) (same tube voltage as for the non-contrast scan).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of ECV on CT\u003c/h2\u003e \u003cp\u003eMyocardial ECV of the left ventricular myocardium (LVM) was measured using commercially available software (Ziostation 2, Ziosoft Inc, Japan) with the following equation: ECV= (ΔHUm/ΔHUb)/(1 \u0026minus; Hct), where ΔHUm is change in myocardial CT attenuation in Hounsfield units (HU), ΔHUb is change in CT attenuation of the blood, and Hct is hematocrit (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) (Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This software performs automatic three-dimensional non-rigid registration of the myocardium between non-contrast and late phase CT images to generate subtraction images (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). The change in CT attenuation (ΔHU) is then obtained on the subtraction image. The software produces a polar map showing both 16 American Heart Association myocardial segments with the mean ECV value for each segment and the mean ECV value of all LVM. ECV of LVM was measured by two cardiologists (SA and YN). The effective dose for scanning of coronary arteries was calculated from the dose-length product in a dose report (conversion factor 0.014) (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables are expressed as the mean \u0026plusmn; SD or as median (interquartile range) if not normally distributed. Categorical variables are reported as counts and percentages. All tests were 2-sided, and p values \u0026lt;0.05 were considered to indicate statistical significance. Analysis of variance or chi-square tests were used to compare baseline characteristics. Interobserver agreement over the presence of significant coronary artery stenosis was assessed using correlation coefficients and compared using chi-square tests. All statistical analyses were performed using the JMP software program, version 15.0.0 (SAS Institute Inc, Cary, NC, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003ePatients were followed for 53 \u0026plusmn; 44 months after the performance of cardiac CT. Among the 20 patients (28.6%) who experienced MACE during the follow-up period, the number of patients taking mineralocorticoid receptor antagonists (MRB) was significantly higher than the number not taking these agents (60% vs 30%, P=0.020). (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). ECV of the LVM on CT was significantly higher in cases with MACE than in those without MACE (37.6\u0026plusmn;5.9 vs 33.2\u0026plusmn;3.9%, P=0.0057). The presence of significant valvular disease (\u0026ge;moderate) was significantly higher in cases with MACE than in those without MACE (55% vs 24%, P=0.013). LVEF was significantly lower in cases with MACE than in those without MACE (22.3\u0026plusmn;7.6 vs 30.8\u0026plusmn;11.8%, P=0.0008).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of patient background between patients with and without major adverse cardiac events\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMACE (+) (N=20)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMACE (-) (N=50)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57 \u0026plusmn; 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58 \u0026plusmn; 14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidemia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes Mellitus, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrial fibrillation, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdministration of β-blocker, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdministration of statin, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdministration of ACE-i or ARB, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdministration of MRB, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.020*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFollow-up period (months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 \u0026plusmn; 34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 \u0026plusmn; 47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eACE, angiotensin converting enzyme; ARB, angiotensin receptor II blocker; MRB, mineralocorticoid receptor antagonists\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of parameters on TTE and CT between patients with and without major adverse cardiac events\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMACE (+) (N=20)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMACE (-) (N=50)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEF on TTE (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 \u0026plusmn; 8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 \u0026plusmn; 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0008*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSignificant valvular disease\u003c/p\u003e \u003cp\u003e(\u0026ge;moderate) on TTE, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.013*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECV of LVM on CT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 \u0026plusmn; 5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 \u0026plusmn; 3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0057*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eLVEF, left ventricular ejection fraction; LVM, left ventricular myocardium; TTE, transthoracic echocardiography; ECV, extracellular volume fraction\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe best cut-off value of ECV on LVM for prediction of MACE was 32.7% based on receiver operating characteristics (ROC) analysis. The area under the curve (AUC) of the ROC curve was 0.751 (P=0.0034) (Figure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Sensitivity and specificity for prediction of future MACE were 90% and 54%, respectively (Figure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The best cut-off value for left ventricular ejection fraction for prediction of MACE was 24% based on ROC analysis. The area under the curve of the receiver operating characteristics curve was 0.718 (P=0.0026). Sensitivity and specificity for prediction of future MACE were 75% and 68%, respectively. (Figure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCases with ECV \u0026ge;32.7% had significantly higher MACE than those with \u0026lt;32.7% during the follow-up period based on Kaplan-Meier analysis (Figure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The consistency of ECV of LVM on CT between the two observers was 0.85. A univariate Cox proportional hazards model showed that significant valvular disease (\u0026ge;moderate) on TTE, LVEF on TTE \u0026le; 24%, administration of MRB and ECV of LVM on CT \u0026ge; 32.7% were significant risk factors for MACE (P=0.025, 0.0089, 0.018 and 0.0044) (Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). A multivariate Cox proportional hazard model was also performed, and ECV of LVM on CT \u0026ge; 32.7% was the only independent significant predictor of MACE during the follow-up period (P=0.78, 0.15, 0.49 and 0.028) (Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The effective radiation dose for additional late phase scan was 3.7\u0026plusmn;0.4mSv (radiation dose for the first 8 cases was not recorded, and these were excluded from this analysis).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e\u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHazard ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% Confidence interval\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSignificant valvular disease\u003c/p\u003e \u003cp\u003e(\u0026ge;moderate) on TTE, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.13 \u0026ndash; 6.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.025*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEF on TTE \u0026le; 24% (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.41 \u0026ndash; 10.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0089*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdministration of MRB, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.21 \u0026ndash; 7.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.018*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECV on LVM \u0026ge; 32.7% (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.94 \u0026ndash; 36.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0044*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eTTE, transthoracic echocardiography; LVEF, left ventricular ejection fraction; MRB, mineralocorticoid receptor antagonists; ECV, extra-cellular volume fraction; LVM, left ventricular myocardium\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e\u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHazard ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% Confidence interval\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSignificant valvular disease\u003c/p\u003e \u003cp\u003e(\u0026ge;moderate) on TTE, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.43 \u0026ndash; 3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEF on TTE \u0026le;24% (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.74 \u0026ndash; 7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdministration of MRB, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.53 \u0026ndash; 3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECV on LVM \u0026ge;32.7% (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.20 \u0026ndash; 26.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.028*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eTTE, transthoracic echocardiography; LVEF, left ventricular ejection fraction; MRB, mineralocorticoid receptor antagonists; ECV, extra-cellular volume fraction; LVM, left ventricular myocardium\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe results of this study suggest that ECV of LVM on CT might be a predictor of future MACE in patients with DCM. CT is useful for the detection of coronary artery stenosis, and ECV analysis is also feasible if an additional late phase scan is performed. From these findings, CT appears to be a useful modality for whole cardiac screening in patients with DCM. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eECV analysis in cases on DCM\u003c/h2\u003e \u003cp\u003eThe presence of LGE is a marker of higher risk of future cardiac events in patients with DCM, because myocardial damage is a marker of low cardiac function and fatal arrhythmia (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). However, almost two-thirds of patients with DCM do not have LGE, because the evaluation of LGE is a qualitative analysis of focal fibrosis, and diffuse myocardial fibrosis is difficult to detect as LGE. Recently, ECV measurement using T1 mapping on MRI has become available for the prediction of future MACE (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). MRI is sometimes contraindicated in cases with DCM because of the presence of mechanical devices or claustrophobia, and gadolinium contrast is also contraindicated in cases with renal dysfunction (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIncreased ECV on LVM is almost equal to the higher amount of biopsy-proven myocardial fibrosis (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e); accordingly, higher ECV means severe degeneration of LVM, which leads to lower LV function or ventricular arrhythmic events (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The amount of myocardial fibrosis is a significant risk factor of future cardiac events in several myocardial diseases (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eAdditional radiation dose for late enhancement analysis on CT\u003c/h2\u003e \u003cp\u003eAdditional radiation dose is necessary for ECV analysis on CT but is a tradeoff for the important clinical information obtained. The effective dose for the late-phase scan in this study was 3.7\u0026plusmn;0.4 mSv, which is smaller than the effective dose for chest CT for evaluation of the lung (almost 5mSv) (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The radiation dose for late phase cardiac images has recently decreased following the introduction of new iterative reconstruction techniques and wide coverage multi-detector CTs, and the image quality of late-phase cardiac images has improved (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). These changes have ameliorated the disadvantages of additional late phase cardiac imaging, and late phase cardiac scan should be recommended in cases with DCM.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eImprovement in image quality of late enhancement\u003c/h2\u003e \u003cp\u003eContrast resolution of late phase cardiac images on CT is inferior to MRI, and MRI remains the gold standard modality for evaluation of myocardial damage on late phase images. Although CT attenuation value increases when a CT scan is performed using a lower tube voltage, image noise increases owing to the limited radiation dose (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Recently, however, newer iterative reconstruction techniques have appeared, and the maximum tube current of CT scanners has increased. They help decrease image noise in images acquired using a lower tube voltage. We previously reported that the combination of new-generation CT and the iterative reconstruction technique is useful for improving the image quality of late enhancement on CT and higher diagnostic accuracy in the detection of myocardial fibrosis (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Of note, the improvement in image quality in late enhancement of LVM has resulted in the approval of ECV analysis using CT as a substitute for MRI in the latest guidelines for cardiac CT and cardiac amyloidosis from the Japanese Cardiovascular Society (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eSeveral limitations of our study warrant mention. First, the study was conducted under a retrospective design at a single center. Second, ECV analysis was performed on single-energy images, and subtraction of late phase and non-contrast images was therefore necessary. Gaps between images in these 2 phases might cause under- or overestimation of ECV on single-energy images compared with analysis of dual-energy images using the latest CT scanners (without gaps). Finally, the study was conducted using different CT scanners, and difference between them may have affected the results.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eEvaluation of ECV by CT is useful for the prediction of MACE in patients with DCM.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e: None\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions:\u003c/strong\u003e SY and HT (Conception of hypothesis, data analysis, writing of manuscript), MT and YN (data analysis, critical revision), MK (critical revision), HS (critical revision), N-SE (critical revision), JO (critical revision), HG (critical revision), YK (critical revision)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was partially supported by the TSUCHIYA MEMORIAL MEDICAL FOUNDATION (Grant no. J17KF00167).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e: Because of the sensitive nature of the data collected for this study, the data will not be made publicly available\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure:\u0026nbsp;\u003c/strong\u003eAll authors have no conflict of interest related to this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e: The study was approved by the local Ethics Committee and Institutional Review Board and therefore conforms to all principles outlined in the 2nd Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e: All authors have provided consent to publish the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eMaron BJ, Towbin JA, Thiene G, Antzelevitch C, Corrado D, Arnett D, et al. (2006) Contemporary definitions and classification of the cardiomyopathies: an American Heart Association Scientific Statement from the Council on Clinical Cardiology, Heart Failure and Transplantation Committee, Quality of Care and Outcomes Research and Functional Genomics and Translational Biology Interdisciplinary Working Groups, and Council on Epidemiology and Prevention. Circulation,113(14):1807-16\u0026nbsp;https://doi.org/\u0026nbsp;10.1161/CIRCULATIONAHA.106.174287\u003c/li\u003e\n \u003cli\u003eSiepen FAD, Buss SJ, Messroghli D, Andre F, Lossnitzer D, Seitz S, et al. (2015) T1 mapping in dilated cardiomyopathy with cardiac magnetic resonance: quantification of diffuse myocardial fibrosis and comparison with endomyocardial biopsy.\u0026nbsp;Eur Heart J\u0026nbsp;Cardiovascular Imaging,16 (2):210-216.\u0026nbsp;https://doi.org/\u0026nbsp;10.1093/ehjci/jeu183\u003c/li\u003e\n \u003cli\u003eMcNally EM, Mestroni L. (2017) Dilated Cardiomyopathy Genetic Determinants and Mechanisms. Circ Res,121(7):731-748.\u0026nbsp; \u0026nbsp;https://doi.org/ 10.1161/CIRCRESAHA.116.309396\u003c/li\u003e\n \u003cli\u003eCummings KW, Bhalla S, Javidan-Nejad C, Bierhals AJ, Gutierrez FR, Woodard PK. (2009) A pattern-based approach to the assessment of delayed enhancement in nonischemic cardiomyopathy at MR imaging. RadioGraphics, 29 (1):89-103.\u0026nbsp;https://doi.org/ 10.1148/rg.291085052\u003c/li\u003e\n \u003cli\u003eAssomull RG, Prasad SK, Lyne J, Smith G, Burman ED, Khan M, et al. (2006) Cardiovascular magnetic resonance, fibrosis, and prognosis in dilated cardiomyopathy. J Am Coll Cardiol,48 (10):1977-85.\u0026nbsp;https://doi.org/ 10.1016/j.jacc.2006.07.049\u003c/li\u003e\n \u003cli\u003eBecker MAJ, Cornel JH, van de Ven PM, van Rossum AC, Allaart CP, Germans T. (2018) The Prognostic Value of Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging in Nonischemic Dilated Cardiomyopathy: A Review and Meta-Analysis. JACC Cardiovasc Imaging,11 (9):1274-1284.\u0026nbsp;https://doi.org/ 10.1016/j.jcmg.2018.03.006\u003c/li\u003e\n \u003cli\u003eHaaf P, Garg P, Messroghli DR, Broadbent DA, Greenwood JP, Plein S. (2016) Cardiac T1 Mapping and Extracellular Volume (ECV) in clinical practice: a comprehensive review. J Cardiovasc Magn Reson,18 (1):89.\u0026nbsp;https://doi.org/10.1186/s12968-016-0308-4.\u003c/li\u003e\n \u003cli\u003eKiaos A, Antonakaki D, Bazmpani MA, Karvounis C, Rimoldi O, Karamitsos TD. (2020) Prognostic value of cardiovascular magnetic resonance T1 mapping techniques in nonischemic dilated cardiomyopathy: A systematic review and meta-analysis. Int J Cardiol,312:110-116.\u0026nbsp;https://doi.org/ 10.1016/j.ijcard.2020.04.052\u003c/li\u003e\n \u003cli\u003eGrobner T, Prischl FC. (2007) Gadolinium and nephrogenic systemic fibrosis. Kidney International,72 (3):260\u0026ndash;264. https://doi.org/ 10.1038/sj.ki.5002338\u003c/li\u003e\n \u003cli\u003eTaylor AJ, Cerqueira M, Hodgson JM, Mark D, Min J, O\u0026apos;Gara P, et al. (2010) ACCF/SCCT/ACR/AHA/ASE/ASNC/NASCI/SCAI/SCMR 2010 appropriate use criteria for cardiac computed tomography. A report of the American College of Cardiology Foundation Appropriate Use Criteria Task Force, the Society of Cardiovascular Computed Tomography, the American College of Radiology, the American Heart Association, the American Society of Echocardiography, the American Society of Nuclear Cardiology, the North American Society for Cardiovascular Imaging, the Society for Cardiovascular Angiography and Interventions, and the Society for Cardiovascular Magnetic Resonance. J Am Coll Cardiol,56 (22):1864-1894. https://doi.org/ 10.1016/j.jacc.2010.07.005\u003c/li\u003e\n \u003cli\u003eUehara M, Takaoka H, Kobayashi Y, Funabashi N. (2013) Diagnostic accuracy of 320-slice computed-tomography for detection of significant coronary artery stenosis in patients with various heart rates and heart rhythms compared with conventional coronary-angiography. Int J Cardiol,167 (3):809-15.\u0026nbsp;https://doi.org/ 10.1016/j.ijcard.2012.02.017\u003c/li\u003e\n \u003cli\u003eTakaoka H, Uehara M, Saito Y, Ota J, Iida Y, Takahashi M, et al. (2020) Improved Diagnostic Performance of New-generation 320-slice Computed Tomography with Forward-projected Model-based Iterative Reconstruction SoluTion for the Assessment of Late Enhancement in Left Ventricular Myocardium. Intern Med,59 (17):2095-2103.\u0026nbsp;https://doi.org/ 10.2169/internalmedicine.4561-20\u003c/li\u003e\n \u003cli\u003eNacif MS, Kawel N, Lee JJ, Chen X, Yao J, Zavodni A, et al. (2012)\u0026nbsp;Interstitial myocardial fibrosis assessed as extracellular volume fraction with low-radiation-dose cardiac CT.\u0026nbsp;Radiology,264 (3):876-83.\u0026nbsp;https://doi.org/ 10.1148/radiol.12112458\u003c/li\u003e\n \u003cli\u003eTakaoka H, Funabashi N, Uehara M, Fujimoto Y, Kobayashi Y. (2013) Diagnostic accuracy of coronary 320 slice CT angiography using retrospective electrocardiogram gated acquisition compared with virtual prospective electrocardiogram gated acquisition with and without padding. Int J Cardiol,168 (3):2811-5.\u0026nbsp;https://doi.org/ 10.1016/j.ijcard.2013.03.066\u003c/li\u003e\n \u003cli\u003eHamdy A, Kitagawa K, Goto Y, Yamada A, Nakamura S, Takafuji M, et al. (2019) Comparison of the different imaging time points in delayed phase cardiac CT for myocardial scar assessment and extracellular volume fraction estimation in patients with old myocardial infarction. Int J Cardiovasc Imaging,35 (5):917\u0026ndash;926.\u0026nbsp;https://doi.org/ 10.1007/s10554-018-1513-z\u003c/li\u003e\n \u003cli\u003eNakamori S, Dohi K, Ishida M, Goto Y, Imanaka-Yoshida K, Omori T, et al. (2018) Native T1 Mapping and Extracellular Volume Mapping for the Assessment of Diffuse Myocardial Fibrosis in Dilated Cardiomyopathy. JACC Cardiovasc Imaging,11 (1):48-59.\u0026nbsp;https://doi.org/ 10.1016/j.jcmg.2017.04.006\u003c/li\u003e\n \u003cli\u003eMcCollough CH, Schueler BA. (2000) Calculation of effective dose. Med Phys,27 (5):828-837.\u0026nbsp;https://doi.org/ 10.1118/1.598948\u003c/li\u003e\n \u003cli\u003eYu L, Bruesewitz MR, Thomas KB, Fletcher JG, Kofler JM, McCollough CH. (2011) Optimal tube potential for radiation dose reduction in pediatric CT: principles, clinical implementations, and pitfalls. Radiographics,31 (3):835-48. https://doi.org/ 10.1148/rg.313105079\u003c/li\u003e\n \u003cli\u003eNarula J, Chandrashekhar Y, Ahmadi A, Abbara S, Berman DS, Blankstein R, et al. (2021) SCCT 2021 Expert Consensus Document on Coronary Computed Tomographic Angiography: A Report of the Society of Cardiovascular Computed Tomography. J Cardiovasc Comput Tomogr,15 (3):192-217.\u0026nbsp;https://doi.org/ 10.1016/j.jcct.2020.11.001\u003c/li\u003e\n \u003cli\u003eKitaoka H, Izumi C, Izumiya Y, Inomata T, Ueda M, Kubo T, et al. (2020) JCS 2020 Guideline on Diagnosis and Treatment of Cardiac Amyloidosis. Circ J,84 (9): 1610\u0026ndash;1671. https://doi.org/ 10.1253/circj.CJ-20-0110\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"dilated cardiomyopathy, extracellular volume, echocardiography, computed tomography","lastPublishedDoi":"10.21203/rs.3.rs-1149072/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1149072/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose:\u003c/h2\u003e \u003cp\u003eDilated cardiomyopathy (DCM) is commonly encountered in daily clinical practice, and screening for coronary artery disease and other cardiomyopathies is necessary for its diagnosis. Cardiac CT is useful for the screening of coronary artery stenosis, and extracellular volume fraction (ECV) analysis by CT has become available using new specific software. Here, we evaluated the utility of ECV analysis using cardiac CT to predict patient prognosis in cases with DCM.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eWe analyzed 70 cases with DCM and coronary computed tomography (CT) with available late-phase images. We evaluated the ECV of the left ventricular myocardium (LVM) using commercially available software (Ziostation 2, Ziosoft Inc, Japan).\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eECV on LVM was 34.5\u0026plusmn;4.9%. Major adverse cardiac events (MACE) occurred in 20 cases (29%). ECV of the LVM on CT and the presence of significant valvular disease were significantly higher in cases with MACE than in those without (37.6\u0026plusmn;5.9 vs 33.2\u0026plusmn;3.9% and 55% vs 24%, P=0.0057 and P=0.013). LVEF was significantly lower in cases with MACE than in those without (22.3\u0026plusmn;7.6 vs 30.8\u0026plusmn;11.8%, P=0.0008). The best cut-off value of ECV on LVM for prediction of MACE was 32.7% based on receiver operating characteristics analysis. Cases with ECV \u0026ge;32.7% had significantly higher MACE based on Kaplan-Meier analysis (P=0.012). Only ECV on LVM was an independent predictor of MACE based on a Cox proportional hazards model (P=0.028).\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eEvaluation of ECV on LVM by CT is useful for predicting MACE in patients with DCM.\u003c/p\u003e","manuscriptTitle":"Evaluation of Extracellular Volume by Computed Tomography is Useful for Prediction of Prognosis in Dilated Cardiomyopathy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-12-10 15:23:42","doi":"10.21203/rs.3.rs-1149072/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8640f6bf-1c25-4afa-991f-3f20086c4270","owner":[],"postedDate":"December 10th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":9072278,"name":"Cardiac \u0026 Cardiovascular Systems"},{"id":9072279,"name":"Nuclear Medicine \u0026 Medical Imaging"}],"tags":[],"updatedAt":"2022-01-24T20:27:01+00:00","versionOfRecord":[],"versionCreatedAt":"2021-12-10 15:23:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1149072","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1149072","identity":"rs-1149072","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}