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Cardiovascular magnetic resonance feature tracking strain analysis for discrimination between hypertensive heart disease and hypertrophic cardiomyopathy.

ABSTRACT: BACKGROUND:Hypertensive heart disease (HHD) and hypertrophic cardiomyopathy (HCM) are both associated with an increased left ventricular (LV) wall thickness. Whilst LV ejection fraction is frequently normal in both, LV strain assessment could differentiate between the diseases. We sought to establish if cardiovascular magnetic resonance myocardial feature tracking (CMR-FT), an emerging method allowing accurate assessment of myocardial deformation, differentiates between both diseases. Additionally, CMR assessment of fibrosis and LV hypertrophy allowed association analyses and comparison of diagnostic capacities. METHODS:Two-hundred twenty-four consecutive subjects (53 HHD, 107 HCM, and 64 controls) underwent 1.5T CMR including native myocardial T1 mapping and late gadolinium enhancement (LGE). Global longitudinal strain (GLS) was assessed by CMR-FT (CVi42, Circle Cardiovascular Imaging Inc.). RESULTS:GLS was significantly higher in HCM patients (-14.7±3.8 vs. -16.5±3.3% [HHD], P = 0.004; or vs. -17.2±2.0% [controls], P<0.001). GLS was associated with LV mass index (HHD, R = 0.419, P = 0.002; HCM, R = 0.429, P<0.001), and LV ejection fraction (HHD, R = -0.493, P = 0.002; HCM, R = -0.329, P<0.001). In HCM patients, GLS was also associated with global native T1 (R = 0.282, P = 0.003), and LGE volume (? = 0.380, P<0.001). Discrimination between HHD and HCM by GLS (c = 0.639, 95% confidence interval [CI] 0.550-0.729) was similar to LV mass index (c = 0.643, 95% CI 0.556-0.731), global myocardial native T1 (c = 0.718, 95% CI 0.638-0.799), and LGE volume (c = 0.680, 95% CI 0.585-0.775). CONCLUSION:CMR-FT GLS differentiates between HHD and HCM. In HCM patients GLS is associated with myocardial fibrosis. The discriminatory capacity of CMR-FT GLS is similar to LV hypertrophy and fibrosis imaging markers.

SUBMITTER: Neisius U

PROVIDER: S-EPMC6703851 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Cardiovascular magnetic resonance feature tracking strain analysis for discrimination between hypertensive heart disease and hypertrophic cardiomyopathy.

Neisius Ulf U Myerson Lana L Fahmy Ahmed S AS Nakamori Shiro S El-Rewaidy Hossam H Joshi Gargi G Duan Chong C Manning Warren J WJ Nezafat Reza R

PloS one 20190821 8

<h4>Background</h4>Hypertensive heart disease (HHD) and hypertrophic cardiomyopathy (HCM) are both associated with an increased left ventricular (LV) wall thickness. Whilst LV ejection fraction is frequently normal in both, LV strain assessment could differentiate between the diseases. We sought to establish if cardiovascular magnetic resonance myocardial feature tracking (CMR-FT), an emerging method allowing accurate assessment of myocardial deformation, differentiates between both diseases. Ad ...[more]

PMID: 31433823

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Project description:BackgroundLeft ventricular segmental wall motion analysis is important for clinical decision making in cardiac diseases. Strain analysis with myocardial tissue tagging is the non-invasive gold standard for quantitative assessment, however, it is time-consuming. Cardiovascular magnetic resonance myocardial feature-tracking (CMR-FT) can rapidly perform strain analysis, because it can be employed with standard CMR cine-imaging. The aim is to validate segmental peak systolic circumferential strain (peak SCS) and time to peak systolic circumferential strain (T2P-SCS) analysed by CMR-FT against tissue tagging, and determine its intra and inter-observer variability.MethodsPatients in whom both cine CMR and tissue tagging has been performed were selected. CMR-FT analysis was done using endocardial (CMR-FTendo) and mid-wall contours (CMR-FTmid). The Intra Class Correlation Coefficient (ICC) and Pearson correlation were calculated.Results10 healthy volunteers, 10 left bundle branch block (LBBB) and 10 hypertrophic cardiomyopathy patients were selected. With CMR-FT all 480 segments were analyzable and with tissue tagging 464 segments.Significant differences in mean peak SCS values of the total study group were present between CMR-FTendo and tissue tagging (-23.8 ± 9.9% vs -13.4 ± 3.3%, p<0.001). Differences were smaller between CMR-FTmid and tissue tagging (-16.4 ± 6.1% vs -13.4 ± 3.3%, p=0.001). The ICC of the mean peak SCS of the total study group between CMR-FTendo and tissue tagging was low (0.19 (95%-CI-0.10-0.49), p=0.02). Comparable results were seen between CMR-FTmid and tissue tagging. In LBBB patients, mean T2P-SCS values measured with CMR-FTendo and CMR-FTmid were 418 ± 66 ms, 454 ± 60 ms, which were longer than with tissue tagging, 376 ± 55 ms, both p<0.05. ICC of the mean T2P-SCS between CMR-FTendo and tissue tagging was 0.64 (95%-CI-0.36-0.81), p<0.001, this was better in the healthy volunteers and LBBB group, whereas the ICC between CMR-FTmid and tissue tagging was lower.The intra and inter-observer agreement of segmental peak SCS with CMR-FTmid was lower compared with tissue tagging; similar results were seen for segmental T2P-SCS.ConclusionsThe intra and inter-observer agreement of segmental peak SCS and T2P-SCS is substantially lower with CMR-FTmid compared with tissue tagging. Therefore, current segmental CMR-FTmid techniques are not yet applicable for clinical and research purposes.

Project description:ObjectivesThe purpose of this study was to determine the prognostic value of feature-tracking global longitudinal strain (GLS) measured during vasodilator stress cardiac magnetic resonance (CMR) imaging.BackgroundPrior studies have suggested that blunted myocardial strain during dobutamine stress echocardiography may be associated with adverse prognosis. Recent developments in CMR feature-tracking techniques now allow assessment of strain in clinical practice using standard cine images without specialized pulse sequences or complex post-processing. Whether feature-tracking GLS measured during vasodilator stress provides independent and incremental prognostic data is unclear.MethodsConsecutive patients undergoing stress perfusion CMR were prospectively enrolled (n = 535). Feature-tracking stress GLS was measured immediately after regadenoson perfusion. Patients were followed for major adverse cardiac events (MACE): death, nonfatal myocardial infarction, heart failure hospitalization, sustained ventricular tachycardia, and late revascularization. Cox proportional hazards regression modeling was used to examine the association between stress GLS and MACE. The incremental prognostic value of stress GLS was assessed in nested models.ResultsOver a median follow-up of 1.5 years, 82 patients experienced MACE. By Kaplan-Meier analysis, patients with stress GLS ≥ median (-19%) had significantly reduced event-free survival compared with those with stress GLS < median (log-rank p < 0.001). Stress GLS was significantly associated with risk of MACE after adjustment for clinical and imaging risk factors including ischemia, ejection fraction, and late gadolinium enhancement (hazard ratio: 1.267; p < 0.001). Addition of stress GLS into a model with clinical and imaging predictors resulted in significant increase in the C-index (from 0.80 to 0.85; p = 0.031) and a continuous net reclassification improvement of 0.898 (95% confidence interval: 0.565 to 1.124).ConclusionsFeature-tracking stress GLS measured during vasodilator stress CMR is an independent predictor of MACE in patients with known or suspected coronary artery disease, incremental to common clinical and imaging risk factors. These findings suggest a role for feature-tracking derived stress GLS in identifying patients at highest risk of adverse events following stress CMR.

Project description:Purpose To evaluate myocardial strain and circumferential transmural strain difference (cTSD; the difference between epicardial and endocardial circumferential strain) in a genotyped cohort with hypertrophic cardiomyopathy (HCM) and to explore correlations between cTSD and other anatomic and functional markers of disease status. Left ventricular (LV) dysfunction may indicate early disease in preclinical HCM (sarcomere mutation carriers without LV hypertrophy). Cardiac MRI feature tracking may be used to evaluate myocardial strain in carriers of HCM sarcomere mutation. Materials and Methods Participants with HCM and their family members participated in a prospective, multicenter, observational study (HCMNet). Genetic testing was performed in all participants. Study participants underwent cardiac MRI with temporal resolution at 40 msec or less. LV myocardial strain was analyzed by using feature-tracking software. Circumferential strain was measured at the epicardial and endocardial surfaces; their difference yielded the circumferential transmural strain difference (cTSD). Multivariable analysis to predict HCM status was performed by using multinomial logistic regression adjusting for age, sex, and LV parameters. Results Ninety-nine participants were evaluated (23 control participants, 34 participants with preclinical HCM [positive for sarcomere mutation and negative for LV hypertrophy], and 42 participants with overt HCM [positive for sarcomere mutation and negative for LV hypertrophy]). The average age was 25 years ± 11 and 44 participants (44%) were women. Maximal LV wall thickness was 9.5 mm ± 1.4, 9.8 mm ± 2.2, and 16.1 mm ± 5.3 in control participants, participants with preclinical HCM (P = .496 vs control participants), and participants with overt HCM (P < .001 vs control participants), respectively. cTSD for control participants, preclinical HCM, and overt HCM was 14% ± 4, 17% ± 4, and 22% ± 7, respectively (P < .01 for all comparisons). In multivariable models (controlling for septal thickness and log-transformed N-terminal brain-type natriuretic peptide), cTSD was predictive of preclinical and overt HCM disease status (P < .01). Conclusion Cardiac MRI feature tracking identifies myocardial dysfunction not only in participants with overt hypertrophic cardiomyopathy, but also in carriers of sarcomere mutation without left ventricular hypertrophy, suggesting that contractile abnormalities are present even when left ventricular wall thickness is normal. © RSNA, 2018 Online supplemental material is available for this article.

Project description:Left Ventricular (LV) Non-compaction (LVNC), Hypertrophic Cardiomyopathy (HCM), and Dilated Cardiomyopathy (DCM) share morphological and functional traits that increase the diagnosis complexity. Additional clinical information, besides imaging data such as cardiovascular magnetic resonance (CMR), is usually required to reach a definitive diagnosis, including electrocardiography (ECG), family history, and genetics. Alternatively, indices of hypertrabeculation have been introduced, but they require tedious and time-consuming delineations of the trabeculae on the CMR images. In this paper, we propose a radiomics approach to automatically encode differences in the underlying shape, gray-scale and textural information in the myocardium and its trabeculae, which may enhance the capacity to differentiate between these overlapping conditions. A total of 118 subjects, including 35 patients with LVNC, 25 with HCM, 37 with DCM, as well as 21 healthy volunteers (NOR), underwent CMR imaging. A comprehensive radiomics characterization was applied to LV short-axis images to quantify shape, first-order, co-occurrence matrix, run-length matrix, and local binary patterns. Conventional CMR indices (LV volumes, mass, wall thickness, LV ejection fraction-LVEF-), as well as hypertrabeculation indices by Petersen and Jacquier, were also analyzed. State-of-the-art Machine Learning (ML) models (one-vs.-rest Support Vector Machine-SVM-, Logistic Regression-LR-, and Random Forest Classifier-RF-) were used for one-vs.-rest classification tasks. The use of radiomics models for the automated diagnosis of LVNC, HCM, and DCM resulted in excellent one-vs.-rest ROC-AUC values of 0.95 while generating these results without the need for the delineation of the trabeculae. First-order and texture features resulted to be among the most discriminative features in the obtained radiomics signatures, indicating their added value for quantifying relevant tissue patterns in cardiomyopathy differential diagnosis.

Dataset Information

Cardiovascular magnetic resonance feature tracking strain analysis for discrimination between hypertensive heart disease and hypertrophic cardiomyopathy.

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Cardiovascular magnetic resonance feature tracking strain analysis for discrimination between hypertensive heart disease and hypertrophic cardiomyopathy.

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