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Iron-Sensitive Cardiac Magnetic Resonance Imaging for Prediction of Ventricular Arrhythmia Risk in Patients With Chronic Myocardial Infarction: Early Evidence.

ABSTRACT: BACKGROUND:Recent canines studies have shown that iron deposition within chronic myocardial infarction (CMI) influences the electric behavior of the heart. To date, the link between the iron deposition and malignant ventricular arrhythmias in humans with CMI is unknown. METHODS AND RESULTS:Patients with CMI (n=94) who underwent late-gadolinium-enhanced cardiac magnetic resonance imaging before implantable cardioverter-defibrillator implantation for primary and secondary preventions were retrospectively analyzed. The predictive values of hypointense cores (HIC) in balanced steady-state free precession images and conventional cardiac magnetic resonance imaging and ECG malignant ventricular arrhythmia parameters for the prediction of primary combined outcome (appropriate implantable cardioverter-defibrillator therapy, survived cardiac arrest, or sudden cardiac death) were studied. The use of HIC within CMI on balanced steady-state free precession as a marker of iron deposition was validated in a canine MI model (n=18). Nineteen patients met the study criteria with events occurring at a median of 249 (interquartile range of 540) days after implantable cardioverter-defibrillator placement. Of the 19 patients meeting the primary end point, 18 were classified as HIC+, whereas only 1 was HIC-. Among the cohort in whom the primary end point was not met, there were 28 HIC+ and 47 HIC- patients. Receiver operating characteristic curve analysis demonstrated an additive predictive value of HIC for malignant ventricular arrhythmias with an increased area under the curve of 0.87 when added to left ventricular ejection fraction (left ventricular ejection fraction alone, 0.68). Both cardiac magnetic resonance imaging and histological validation studies performed in canines demonstrated that HIC regions in balanced steady-state free precession images within CMI likely result from iron depositions. CONCLUSIONS:Hypointense cores within CMI on balanced steady-state free precession cardiac magnetic resonance imaging can be used as a marker of iron deposition and yields incremental information toward improved prediction of malignant ventricular arrhythmias.

SUBMITTER: Cokic I

PROVIDER: S-EPMC4779060 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature

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Iron-Sensitive Cardiac Magnetic Resonance Imaging for Prediction of Ventricular Arrhythmia Risk in Patients With Chronic Myocardial Infarction: Early Evidence.

Cokic Ivan I Kali Avinash A Yang Hsin-Jung HJ Yee Raymond R Tang Richard R Tighiouart Mourad M Wang Xunzhang X Jackman Warren S WS Chugh Sumeet S SS White James A JA Dharmakumar Rohan R

Circulation. Cardiovascular imaging 20150801 8

<h4>Background</h4>Recent canines studies have shown that iron deposition within chronic myocardial infarction (CMI) influences the electric behavior of the heart. To date, the link between the iron deposition and malignant ventricular arrhythmias in humans with CMI is unknown.<h4>Methods and results</h4>Patients with CMI (n=94) who underwent late-gadolinium-enhanced cardiac magnetic resonance imaging before implantable cardioverter-defibrillator implantation for primary and secondary prevention ...[more]

PMID: 26259581

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Project description:Background Despite limitations as a stand-alone parameter, left ventricular (LV) ejection fraction is the preferred measure of myocardial function and marker for postinfarction risk stratification. LV myocardial uniformity alterations may provide superior prognostic information after acute myocardial infarction, which was the subject of this study. Methods and Results Consecutive patients with acute myocardial infarction (n=1082; median age: 63 years; 75% male) undergoing cardiac magnetic resonance at a median of 3 days after infarction were included in this multicenter observational study. Circumferential and radial uniformity ratio estimates were derived from cardiac magnetic resonance feature tracking as markers of mechanical uniformity alterations (values between 0 and 1 with 1 reflecting perfect uniformity). The clinical end point was the 12-month rate of major adverse cardiac events, consisting of all-cause death, reinfarction, and new congestive heart failure. Patients with major adverse cardiac events (n=73) had significantly impaired circumferential uniformity ratio estimates (0.76 [interquartile range: 0.67-0.86] versus 0.84 [interquartile range: 0.76-0.89]; P<0.001) and radial uniformity ratio estimates (0.69 [interquartile range: 0.60-0.79] versus 0.76 [interquartile range: 0.67-0.83]; P<0.001) compared with patients without events. Although uniformity estimates did not provide independent prognostic information in the overall cohort, a circumferential uniformity ratio estimate below the median of 0.84 emerged as an independent predictor of outcome in postinfarction patients with LV ejection fraction >35% (n=959), even after adjustment for established risk factors (hazard ratio: 1.99; 95% CI, 1.06-3.74; P=0.033 in multivariable Cox regression analysis). In contrast, LV ejection fraction was not associated with adverse events in this subgroup of patients with acute myocardial infarction. Conclusions Cardiac magnetic resonance-derived estimates of mechanical uniformity alterations are novel markers for risk assessment after acute myocardial infarction, and the circumferential uniformity ratio estimate provides independent prognostic information for patients with preserved or only moderately reduced LV ejection fraction.

Project description:BackgroundAlthough there are many studies on the prognostic factors of left ventricular myocardial noncompaction (LVNC), the determinants are varied and not entirely consistent. This study aimed to build predictive models using radiomics features and machine learning to predict major adverse cardiovascular events (MACEs) in patients with LVNC.MethodsIn total, 96 patients with LVNC were included and randomly divided into training and test cohorts. A total of 105 cine cardiac magnetic resonance (CMR)-derived radiomics features and 35 clinical characteristics were extracted. Five different oversampling algorithms were compared for selection of the optimal imbalanced processing. Feature importance was assessed with extreme gradient boosting (XGBoost). We compared the performance of 5 machine learning classification methods with different sample:feature ratios to determine the optimal hybrid classification strategy. Subsequently, radiomics, clinical, and combined radiomics-clinical models were developed and compared.ResultsThe machine learning pipeline included an adaptive synthetic (ADASYN) algorithm for imbalanced processing, XGBoost feature selection with a sample:feature ratio of 10, and support vector machine (SVM) modeling. The areas under the receiver operating characteristic curves (AUCs) of the radiomics model, clinical model, and combined model in the validation cohort were 0.87 (sensitivity 83.33%, specificity 64.29%), 0.65 (sensitivity 16.67%, specificity 78.57%), and 0.92 (specificity 33.33%, sensitivity 100.00%), respectively. The radiomics model performed similarly to the clinical and combined models (P=0.124 and P=0.621, respectively). The performance of the combined model was significantly better than that of the clinical model (P=0.003).ConclusionsThe machine learning-based cine CMR radiomics model performed well at predicting MACEs in patients with LVNC. Adding radiomics features offered incremental prognostic value over clinical factors alone.

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Project description:AimsMyocardial infarction (MI) alters cardiac fibre organization with unknown consequences on ventricular arrhythmia. We used diffusion tensor imaging (DTI) of three-dimensional (3D) cardiac fibres and scar reconstructions to identify the main parameters associated with ventricular arrhythmia inducibility and ventricular tachycardia (VT) features after MI.Methods and resultsTwelve pigs with established MI and three controls underwent invasive electrophysiological characterization of ventricular arrhythmia inducibility and VT features. Animal-specific 3D scar and myocardial fibre distribution were obtained from ex vivo high-resolution contrast-enhanced T1 mapping and DTI sequences. Diffusion tensor imaging-derived parameters significantly different between healthy and scarring myocardium, scar volumes, and left ventricular ejection fraction (LVEF) were included for arrhythmia risk stratification and correlation analyses with VT features. Ventricular fibrillation (VF) was the only inducible arrhythmia in 4 out of 12 infarcted pigs and all controls. Ventricular tachycardia was also inducible in the remaining eight pigs during programmed ventricular stimulation. A DTI-based 3D fibre disorganization index (FDI) showed higher disorganization within dense scar regions of VF-only inducible pigs compared with VT inducible animals (FDI: 0.36; 0.36-0.37 vs. 0.32; 0.26-0.33, respectively, P = 0.0485). Ventricular fibrillation induction required lower programmed stimulation aggressiveness in VF-only inducible pigs than VT inducible and control animals. Neither LVEF nor scar volumes differentiated between VF and VT inducible animals. Re-entrant VT circuits were localized within areas of highly disorganized fibres. Moreover, the FDI within heterogeneous scar regions was associated with the median VT cycle length per animal (R2 = 0.5320).ConclusionThe amount of scar-related cardiac fibre disorganization in DTI sequences is a promising approach for ventricular arrhythmia stratification after MI.

Dataset Information

Iron-Sensitive Cardiac Magnetic Resonance Imaging for Prediction of Ventricular Arrhythmia Risk in Patients With Chronic Myocardial Infarction: Early Evidence.

Publications

Iron-Sensitive Cardiac Magnetic Resonance Imaging for Prediction of Ventricular Arrhythmia Risk in Patients With Chronic Myocardial Infarction: Early Evidence.

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