Prognostic impact of MRI-derived feature tracking myocardial strain in patients with non-ischaemic dilated cardiomyopathy: a systematic review and meta-analysis

AIM

To evaluate the clinical utility of feature tracking (FT)-derived myocardial strain in patients with non-ischaemic dilated cardiomyopathy (NIDCM).

MATERIALS AND METHODS

Electronic database searches of PubMed, Web of Science Core Collection, Cochrane advanced search, and EMBASE were performed. Studies on NIDCM were divided into categories according to left ventricular ejection fraction (LVEF; <30%, 30-40%, >40%), and correlations between strains and prevalence of late gadolinium enhancement (LGE) were evaluated by weighted correlation coefficients. Global longitudinal strain (GLS) hazard ratios were also integrated for prediction of future adverse events.

RESULTS

The present meta-analysis analysed data from 5,767 patients with NIDCM from 30 eligible studies. GLS and global circumferential strain significantly differed across the three LVEF categories (all p<0.05); however, global radial strain did not. Only GLS showed a strong correlation with the prevalence of LGE (Spearman's correlation coefficient = 0.61). The pooled HR of GLS for predicting adverse events was 1.15 (95% confidence interval [CI]: 1.07-1.23, p<0.001).

CONCLUSION

In this meta-analysis, FT-derived GLS was strongly correlated with myocardial fibrosis and was an important predictor of future adverse events. These results suggest that FT-derived GLS may be useful in the pathological evaluation and risk stratification of NIDCM.

The value of CMR Left ventricular strain analysis in evaluating ICM

The purpose of this article is to investigate the value of cardiac magnetic resonance imaging (CMR) derived left ventricular strain parameters in evaluation of ischemic cardiomyopathy (ICM). Thirty-one ICM patients and nineteen non-cardiomyopathy (non-CM) patients who performed CMR examinations during the same period were selected for this retrospective study. The basic clinical data, CMR left ventricular function parameters, left ventricular strain parameters were compared among the left ventricular ejection fraction (LVEF) preserved ICM group, the LVEF impaired ICM group and the non-CM group. The differences of MyoGCS (-21.9 ± 1.9 vs. -18.9 ± 2.7 P<0.001), MyoGLS (-20.8 ± 2.3 vs. -17.0 ± 2.9 P<0.001) and EndoGLS (-22.2 ± 3.1 vs. -17.6 ± 3.7 P<0.001) between LVEF preserved ICM group and non-CM group were statistically significant, while the differences of left heart function parameters between the two groups were not statistically significant (P > 0.05). The left ventricular strain analysis can be used to assess cardiac functional and morphological alterations in ICM patients prior to changes of left ventricular function parameters, which has high clinical significance.

CMR-derived myocardial strain analysis differentiates ischemic and dilated cardiomyopathy-a propensity score-matched study

Left ventricular (LV) longitudinal, circumferential, and radial motion can be measured using feature tracking of cardiac magnetic resonance (CMR) images. The aim of our study was to detect differences in LV mechanics between patients with dilated cardiomyopathy (DCM) and ischemic cardiomyopathy (ICM) who were matched using a propensity score-based model. Between April 2017 and October 2019, 1224 patients were included in our CMR registry, among them 141 with ICM and 77 with DCM. Propensity score matching was used to pair patients based on their indexed end-diastolic volume (EDVi), ejection fraction (EF), and septal T1 relaxation time (psmatch2 module L Feature tracking provided six parameters for global longitudinal, circumferential, and radial strain with corresponding strain rates in each group. Strain parameters were compared between matched pairs of ICM and DCM patients using paired t tests. Propensity score matching yielded 72 patients in each group (DCM mean age 58.6 ± 11.6 years, 15 females; ICM mean age 62.6 ± 13.2 years, 11 females, p = 0.084 and 0.44 respectively; LV-EF 32.2 ± 13.5% vs. 33.8 ± 12.1%, p = 0.356; EDVi 127.2 ± 30.7 ml/m2 vs. 121.1 ± 41.8 ml/m2, p = 0.251; native T1 values 1165 ± 58 ms vs. 1167 ± 70 ms, p = 0.862). There was no difference in global longitudinal strain between DCM and ICM patients (- 10.9 ± 5.5% vs. - 11.2 ± 4.7%, p = 0.72), whereas in DCM patients there was a significant reduction in global circumferential strain (- 10.0 ± 4.5% vs. - 12.2 ± 4.7%, p = 0.002) and radial strain (17.1 ± 8.51 vs. 21.2 ± 9.7%, p = 0.039). Our data suggest that ICM and DCM patients have inherently different myocardial mechanics, even if phenotypes are similar. Our data show that GCS is significantly more impaired in DCM patients. This feature may help in more thoroughly characterizing cardiomyopathy patients.

Identification of MYH6 as the potential gene for human ischaemic cardiomyopathy

The present study aimed to explore the potential hub genes and pathways of ischaemic cardiomyopathy (ICM) and to investigate the possible associated mechanisms. Two microarray data sets (GSE5406 and GSE57338) were downloaded from the Gene Expression Omnibus (GEO) database. The limma package was used to analyse the differentially expressed genes (DEGs). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, Disease Ontology (DO) and Gene Ontology (GO) annotation analyses were performed. A protein‐protein interaction (PPI) network was set up using Cytoscape software. Significant modules and hub genes were identified by the Molecular Complex Detection (MCODE) app. Then, further functional validation of hub genes in other microarrays and survival analysis were performed to judge the prognosis. A total of 1065 genes were matched, with an adjusted p < 0.05, and 17 were upregulated and 25 were downregulated with|log₂ (fold change)|≥1.2. After removing the lengthy entries, GO identified 12 items, and 8 pathways were enriched at adjusted p < 0.05 (false discovery rate, FDR set at <0.05). Three modules with a score >8 after MCODE analysis and MYH6 were ultimately identified. When validated in GSE23561, MYH6 expression was lower in patients with CAD than in healthy controls (p < 0.05). GSE60993 data suggested that MYH6 expression was also lower in AMI patients (p < 0.05). In the GSE59867 data set, MYH6 expression was lower in CAD patients than in AMI patients and lower in heart failure (HF) patients than in non‐HF patients. However, there was no difference at different periods within half a year, and HF was increased when MYH6 expression was low (p < 0.05–0.01). We performed an integrated analysis and validation and found that MYH6 expression was closely related to ICM and HF. However, whether this marker can be used as a predictor in blood samples needs further experimental verification.

Machine learning phenotyping of scarred myocardium from cine in hypertrophic cardiomyopathy

AIMS

Cardiovascular magnetic resonance (CMR) with late-gadolinium enhancement (LGE) is increasingly being used in hypertrophic cardiomyopathy (HCM) for diagnosis, risk stratification, and monitoring. However, recent data demonstrating brain gadolinium deposits have raised safety concerns. We developed and validated a machine-learning (ML) method that incorporates features extracted from cine to identify HCM patients without fibrosis in whom gadolinium can be avoided.

METHODS AND RESULTS

An XGBoost ML model was developed using regional wall thickness and thickening, and radiomic features of myocardial signal intensity, texture, size, and shape from cine. A CMR dataset containing 1099 HCM patients collected using 1.5T CMR scanners from different vendors and centres was used for model development (n=882) and validation (n=217). Among the 2613 radiomic features, we identified 7 features that provided best discrimination between +LGE and -LGE using 10-fold stratified cross-validation in the development cohort. Subsequently, an XGBoost model was developed using these radiomic features, regional wall thickness and thickening. In the independent validation cohort, the ML model yielded an area under the curve of 0.83 (95% CI: 0.77-0.89), sensitivity of 91%, specificity of 62%, F1-score of 77%, true negatives rate (TNR) of 34%, and negative predictive value (NPV) of 89%. Optimization for sensitivity provided sensitivity of 96%, F2-score of 83%, TNR of 19% and NPV of 91%; false negatives halved from 4% to 2%.

CONCLUSION

An ML model incorporating novel radiomic markers of myocardium from cine can rule-out myocardial fibrosis in one-third of HCM patients referred for CMR reducing unnecessary gadolinium administration.

The year 2018 in the European Heart Journal - Cardiovascular Imaging: Part I

The European Heart Journal - Cardiovascular Imaging has become one of the leading multimodality cardiovascular imaging journal, since it was launched in 2012. The impact factor is an impressive 8.366 and it is now established as one of the top 10 cardiovascular journals. The journal is the most important cardiovascular imaging journal in Europe. The most important studies from 2018 will be highlighted in two reports. Part I of the review will focus on studies about myocardial function and risk prediction, myocardial ischaemia, and emerging techniques in cardiovascular imaging, while Part II will focus on valvular heart disease, heart failure, cardiomyopathies, and congenital heart disease.

The year 2017 in the European Heart Journal-Cardiovascular Imaging: Part II

European Heart Journal - Cardiovascular Imaging was launched in 2012 as a multimodality cardiovascular imaging journal. It has gained an impressive impact factor of 8.366 during its first 5 years and is now established as one of the top 10 cardiovascular journals and has become the most important cardiovascular imaging journal in Europe. The most important studies from 2017 will be highlighted in two reports. Part I of the review will focus on studies about myocardial function and risk prediction, myocardial ischaemia, and emerging techniques in cardiovascular imaging, while Part II will focus on valvular heart disease, heart failure, cardiomyopathies, and congenital heart disease.