Cardiovascular magnetic resonance-determined left ventricular myocardium impairment is associated with C-reactive protein and ST2 in patients with paroxysmal atrial fibrillation.

BACKGROUND: Myocardial strain assessed with cardiovascular magnetic resonance (CMR) feature tracking can detect early left ventricular (LV) myocardial deformation quantitatively in patients with a variety of cardiovascular diseases, but this method has not yet been applied to quantify myocardial strain in patients with atrial fibrillation (AF) and no coexistent cardiovascular disease, i.e., the early stage of AF. This study sought to compare LV myocardial strain and T1 mapping indices in AF patients and healthy subjects, and to investigate the associations of a portfolio of inflammation, cardiac remodeling and fibrosis biomarkers with LV myocardial strain and T1 mapping indices in AF patients with no coexistent cardiovascular disease. METHODS: The study consisted of 80 patients with paroxysmal AF patients and no coexistent cardiovascular disease and 20 age- and sex-matched healthy controls. Left atrial volume (LAV), LV myocardial strain and native T1 were assessed with CMR, and compared between the AF patients and healthy subjects. Biomarkers of C-reactive protein (CRP), transforming growth factor beta-1 (TGF-ß1), collagen III N-terminal propeptide (PIIINP), and soluble suppression of tumorigenicity 2 (sST2) were obtained with blood tests, and compared between the AF patients and healthy controls. Associations of these biomarkers with those CMR-measured parameters were analyzed for the AF patients. RESULTS: For the CMR-measured parameters, the AF patients showed significantly larger LAV and LV end-systolic volume, and higher native T1 than the healthy controls (max P = 0.027). The absolute values of the LV peak systolic circumferential strain and its rate as well as the LV diastolic circumferential strain rate were all significantly reduced in the AF patients (all P < 0.001). For the biomarkers, the AF patients showed significantly larger CRP (an inflammation biomarker) and sST2 (a myocardium stiffness biomarker) than the controls (max P = 0.007). In the AF patients, the five CMR-measured parameters of LAV, three LV strain indices and native T1 were all significantly associated with these two biomarkers of CRP and sST2 (max P = 0.020). CONCLUSIONS: In patients with paroxysmal AF and no coexistent cardiovascular disease, LAV enlargement and LV myocardium abnormalities were detected by CMR, and these abnormalities were associated with biomarkers that reflect inflammation and myocardial stiffness.

Comparison of Left Ventricular Global Strain in Anterior and Non-anterior Wall Myocardial Infarction With CMR Tissue Tracking.

Left ventricular (LV) myocardial dysfunction occurs after myocardial infarction (MI) is associated with the location, infarct size, and transmurality degrees of MI. The myocardial strain is a sensitive index used for the quantification of myocardium dysfunction. This study used Tissue-Tracking to evaluate whether the different location of MI would result in different myocardial dysfunction. One hundred patients diagnosed with MI who underwent cardiovascular magnetic resonance examination were included. The tissue-tracking indices, LV global radial strain (GRS), global circumferential strain (GCS), global longitudinal strain (GLS), and the infarct size (IS,% of LV mass) were quantified. There were 42 cases of anterior wall MI (AWMI) and 58 cases of non-anterior wall MI (NAWMI). The GCS of AWMI was significantly lower than that of NAWMI (P = 0.036). In the same level of infarct size, the myocardial strain of AWMI was not significantly different from NAWMI group (P > 0.05). The GRS and GCS were significantly different between transmurality > 50% group with transmurality ≤ 50% group (P < 0.05). The present study demonstrated that LV MI is associated with reduced myocardial strain, and the infarct size and degrees of transmurality were both related to the decline of myocardial strain in patients with MI.

Effects of Different LVEF Assessed by Echocardiography and CMR on the Diagnosis and Therapeutic Decisions of Cardiovascular Diseases.

AIMS: The impact of different left ventricular ejection fraction (LVEF) assessed by echocardiography (EC) and cardiac magnetic resonance (CMR) on clinical diagnosis and management that could be critical in clinical practice remains unclear. This study investigated this impact for patients who underwent both exams in a real-world clinical practice. METHODS: 500 patients who underwent CMR and two-dimensional EC were retrospectively included in present study. EC-measured LVEF and CMR-measured LVEF were compared. A 50% cut-off of LVEF was chosen to assess the effect of the difference between these two modalities on disease diagnosis, and a 35% cut-off was chosen for disease management, respectively. For those patients who received device therapy or coronary artery bypass grafting (CABG), the study compared the LVEF between EC and CMR with the current guideline for therapy recommendation. RESULTS: EC-LVEF and CMR-LVEF were positively correlated, but EC-LVEF was significantly larger than CMR-LVEF (P < 0.001). Three patient groups were examined: (I) CMR-LVEF ≥ 50%, (II) 35% < CMR-LVEF < 50%, and (III) CMR-LVEF ≤ 35%. Overall, 139 of 500 patients showed inconsistent measures. There were more inconsistent measures between the two modalities in group III than group I (41.6% for group III vs. 4.1% for group I). In patients who received device therapy or CABG, 97.6% of the CMR-measured LVEF were consistent with the guideline, but only 61.0% consistent EC-measured LVEF. CONCLUSION: For patients with lower LVEF and planning to receive device therapy or cardiac surgery, it should be cautious to applying the recommended cut-off values to CMR-measured LVEF because its inconsistency with EC-measured LVEF.

Validation of black blood late gadolinium enhancement (LGE) for evaluation of myocardial infarction in patients with or without pathological Q-wave on electrocardiogram (ECG).

BACKGROUND: The pathological Q-wave (QW) is an important indicator of infarcted myocardial volume indicating a worse prognosis compared to non-Q-wave (NQW) infarctions. Traditional classification divides infarcts into transmural and non-transmural based on QW and NQW. This view has been challenged by the advent of late gadolinium enhancement (LGE) MR imaging. Conventional LGE (Conv-LGE) detection of subendocardial MI is limited by bright blood pool. Dark Blood LGE imaging (DB-LGE) nulls the blood pool improving the conspicuity and accuracy of detection of subendocardial infarcts. We hypothesize that improved detection of subendocardial enhancement with DB-LGE will result in improved correlation of electrocardiogram (ECG) and extent of infarction. METHODS: Sixty-four clinically confirmed infarction patients were enrolled in this prospective study. All the participants underwent cardiac MR imaging including conv-LGE and DB-LGE. Twelve-lead ECG were performed on the same day. The patients were divided into QW and NQW groups by one experienced cardiologist. MI quantitation was by MI% (the ratio of MI volume to whole myocardial volume) and transmural grading, compared using paired t-test and Wilcoxon-test, respectively. The image quality obtained by Conv-LGE and DB-LGE were evaluated according to the signal intensity ratio (SIR) and contrast-to-noise ratio (CNR). RESULTS: Fifty-six subjects were enrolled in the final analysis [23 (41%) QW and 33 (59%) NQW infarcts]. For the QW cohort, both sequences classified infarcts as transmural in 21/23 (91%) subjects and subendocardial in 2/23 (9%). For the NQW cohort, both sequences classified infarcts as transmural in 16/33 (48%) subjects and subendocardial in 17/33 (52%). Using BB-LGE there were significant differences in detecting subendocardial infarcts in QW and NQW cohorts (Z=-5.85, P<0.001). The MI% of QW group was greater than in NQW group (24.2±10.3 vs.15.9±9.8, P=0.003). Compared to Conv-LGE, BB-LGE provided higher CNR and SIR between infarcted myocardium and blood pool (6.3±2.6 vs. 2.1±1.3, P<0.001; 5.4±1.9 vs. 1.3±0.2, P<0.001). BB-LGE detected more subendocardial infarcted segments in the QW group and NQW group (Z=-4.24, P<0.001; Z=-5.57, P<0.001). The larger MI% was displayed in BB-LGE than in Conv-LGE in both QW group and NQW group (24.2±10.3 vs. 22.6±10.3, P<0.001; 15.9±9.8 vs.14.6±9.6, P=0.001). CONCLUSIONS: Compared to conventional LGE, DB-LGE can provide more accurate detection and characterization of infarction in terms of transmurality and subendocardial extent. This is important for evaluating QW and NQW MIs. Due to nulling the high signal of blood pool, DB-LGE can effectively improve the identification of subendocardial MI which may be missed on conventional LGE. Therefore, in both QW and NQW MIs, DB-LGE detects more subendocardial MIs and larger MI% is found. This may facilitate more accurate quantitative MR assessment of both QW and NQW MIs and further empower LGE volume as a predictive biomarker.

Using machine learning to predict one-year cardiovascular events in patients with severe dilated cardiomyopathy.

PURPOSE: Dilated cardiomyopathy (DCM) is a common form of cardiomyopathy and it is associated with poor outcomes. A poor prognosis of DCM patients with low ejection fraction has been noted in the short-term follow-up. Machine learning (ML) could aid clinicians in risk stratification and patient management after considering the correlation between numerous features and the outcomes. The present study aimed to predict the 1-year cardiovascular events in patients with severe DCM using ML, and aid clinicians in risk stratification and patient management. MATERIALS AND METHODS: The dataset used to establish the ML model was obtained from 98 patients with severe DCM (LVEF < 35%) from two centres. Totally 32 features from clinical data were input to the ML algorithm, and the significant features highly relevant to the cardiovascular events were selected by Information gain (IG). A naive Bayes classifier was built, and its predictive performance was evaluated using the area under the curve (AUC) of the receiver operating characteristics by 10-fold cross-validation. RESULTS: During the 1-year follow-up, a total of 22 patients met the criterion of the study end-point. The top features with IG > 0.01 were selected for ML model, including left atrial size (IG = 0.240), QRS duration (IG = 0.200), and systolic blood pressure (IG = 0.151). ML performed well in predicting cardiovascular events in patients with severe DCM (AUC, 0.887 [95% confidence interval, 0.813-0.961]). CONCLUSIONS: ML effectively predicted risk in patients with severe DCM in 1-year follow-up, and this may direct risk stratification and patient management in the future.

Discordant clinical presentation and pathophysiology: insights with cardiac magnetic resonance.

We report serial cardiac magnetic resonance (CMR) imaging finding in a 23-year-old man admitted with wide complex ventricular tachycardia (VT), chest pain and syncope. Serial CMR was performed prior to and following clinical stabilization after treatment for suspected myocarditis. The initial CMR exam showed mildly thickened mid left ventricular septum with mild hyperintensity lesion on T2-weighted image (T2WI). There was enhancement in subepicardial mid inferoseptal wall and right ventricular insertion on late gadolinium enhancement (LGE) image. A subsequent CMR exam after resolution of symptoms and normalization of cardiac markers demonstrated diffuse mid and subepicardial edema of left ventricle on T2WI. Diffuse mid and subepicardial enhancement of left ventricle on LGE. The extent of LGE was increased compared to the initial CMR exam. The potential explanation for the discordance between clinical observations and imaging findings is discussed.

Value of Cardiac Magnetic Resonance Fractal Analysis Combined With Myocardial Strain in Discriminating Isolated Left Ventricular Noncompaction and Dilated Cardiomyopathy.

BACKGROUND: Excessive trabeculation is present in isolated left ventricular noncompaction (LVNC) and dilated cardiomyopathy (DCM), which sometimes makes the differentiation between these two difficult. Fractal dimension (FD) is a unitless measure value of how completely the object fills space, which can assess the extent of myocardial trabeculae quantitatively. PURPOSE: To compare the trabeculae features and myocardial strain derived from cardiac MR between LVNC and DCM. STUDY TYPE: Respective case-control series. POPULATION: In all, 35 LVNC patients and 30 DCM patients were enrolled, and 20 healthy volunteers were selected as a control group. FIELD STRENGTH/SEQUENCE: 5 T with 8-channel phased-array cardiac receiver coil including steady-state free precession cine imaging. ASSESSMENT: The degree of left ventricular trabeculation was evaluated by a semiautomatic tool based on fractal analysis. Myocardial deformation was assessed by feature tracking. STATISTICAL TESTS: Independent samples Student's t-test, Mann-Whitney U-test, receiver operating characteristics (ROC) curves, and Spearman's rank coefficient were conducted. RESULTS: Max apical FD and mean global FD were higher in the LVNC group than in the DCM group (1.433 ± 0.074 vs. 1.341 ± 0.062, P < 0.001; 1.323 ± 0.036 vs. 1.267 ± 0.041, P < 0.001, respectively). For diagnosing LVNC, max apical FD was 1.392 (area under the curve [AUC] = 0.881, 95% confidence interval [CI]: 0.804-0.957), and the cutoff value of mean global FD was 1.283 (AUC = 0.895, 95% CI: 0.828-0.961). The global peak longitudinal strain value of the left ventricle (GPLS) showed significant differences between the LVNC group and DCM group [-6.49 (-11.41, -4.90) vs. -4.61 (-5.87, -3.61), P = 0.006]. The diagnostic accuracy for LVNC is highest when using FDs in coordination with GPLS (AUC = 0.93, 95% CI: 0.86-0.98, P < 0.001). DATA CONCLUSION: Fractal analysis provides a quantitative measurement of myocardial trabeculation. The combination of fractal analysis with myocardial strain provides a novel biomarker in distinguishing LVNC from DCM. LEVEL OF EVIDENCE: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:153-163.