Global and Regional Longitudinal Strain Assessment in Hypertrophic Cardiomyopathy

Complete spatiotemporal quantification of cardiac motion in mice through enhanced acquisition and super-resolution reconstruction

The quantification of cardiac motion using cardiac magnetic resonance imaging (CMR) has shown promise as an early-stage marker for cardiovascular diseases. Despite the growing popularity of CMR-based myocardial strain calculations, measures of complete spatiotemporal strains (i.e., three-dimensional strains over the cardiac cycle) remain elusive. Complete spatiotemporal strain calculations are primarily hampered by poor spatial resolution, with the rapid motion of the cardiac wall also challenging the reproducibility of such strains. We hypothesize that a super-resolution reconstruction (SRR) framework that leverages combined image acquisitions at multiple orientations will enhance the reproducibility of complete spatiotemporal strain estimation. Two sets of CMR acquisitions were obtained for five wild-type mice, combining short-axis scans with radial and orthogonal long-axis scans. Super-resolution reconstruction, integrated with tissue classification, was performed to generate full four-dimensional (4D) images. The resulting enhanced and full 4D images enabled complete quantification of the motion in terms of 4D myocardial strains. Additionally, the effects of SRR in improving accurate strain measurements were evaluated using an in-silico heart phantom. The SRR framework revealed near isotropic spatial resolution, high structural similarity, and minimal loss of contrast, which led to overall improvements in strain accuracy. In essence, a comprehensive methodology was generated to quantify complete and reproducible myocardial deformation, aiding in the much-needed standardization of complete spatiotemporal strain calculations.

Incremental value of myocardial global longitudinal strain in predicting major adverse cardiac events among patients with hypertrophic cardiomyopathy

OBJECTIVES

Endocardial global longitudinal strain (endo-GLS) measured with echocardiography (echo) has been demonstrated to be associated with myocardial fibrosis (MF) and is a prognostic predictor in patients with hypertrophic cardiomyopathy (HCM). Late gadolinium enhancement cardiac magnetic resonance (LGE-CMR) imaging showed that MF is primarily located in the myocardial layer of the extremely hypertrophic septal or ventricular wall. We hypothesized that GLS of the myocardial layer (myo-GLS) is more strongly correlated with the extent of LGE (%LGE) and is a more powerful prognostic factor than endo-GLS.

METHODS

A total of 177 inpatients (54.0 [IQR: 43.0, 64.0] years, female 37.3%) with HCM were retrospectively included from May 2019 to April 2021. Among them, 162 patients underwent echocardiographic examination and contrast-enhanced CMR within 7 days. Myo-GLS and %LGE were blindly assessed in a core laboratory. All the patients were followed after they were discharged.

RESULTS

During a mean follow-up of 33.77 [IQR 30.05, 35.40] months, 14 participants (7.91%) experienced major adverse cardiac events (MACE). The MACE (+) group showed lower absolute endo-GLS and myo-GLS than the MACE (-) group. Myo-GLS was more associated with %LGE (r = -.68, P < .001) than endo-GLS (r = -.64, P < .001). Cox multivariable analysis indicated that absolute myo-GLS was independently associated with MACE (adjusted hazard ratio = .75, P < .05). Myo-GLS was better than endo-GLS at detecting MACE (+) patients (-8.64%, AUC .939 vs. - 16.375%, AUC .898, P < .05).

CONCLUSIONS

Myo-GLS is a stronger predictor of MACE than endo-GLS in patients with HCM and is highly correlated with %LGE.

Hereditary Transthyretin Amyloidosis: How to Differentiate Carriers and Patients Using Speckle-Tracking Echocardiography

BACKGROUND

Hereditary transthyretin amyloidosis is a rare disease caused by transthyretin (TTR) gene mutations. The aim of our study was to identify early signs of cardiac involvement in patients with a TTR gene mutation in order to differentiate carriers from patients with neurological or cardiac disease.

METHODS

A case-control study was carried out on 31 subjects with the TTR mutation. Patients were divided into three groups: 23% with cardiac amyloidosis and polyneuropathy (group A), 42% with only polyneuropathy (group B) and 35% carriers (group C). Speckle-tracking echocardiography (left-ventricular global longitudinal strain-GLS, atrial stiffness) was performed in all patients. The apical/basal longitudinal strain ratio (SAB) and relative apical sparing (RAS) were assessed in all subjects.

RESULTS

Analyzing groups C and B, we only found a significant difference in the SAB (p-value 0.001) and RAS (p-value 0.039). These parameters were significantly more impaired in group A compared to group B (SAB p-value 0.008; RAS p-value 0.002). Also, atrial stiffness was significantly impaired in groups A and B compared to group C.

CONCLUSIONS

Our study suggests the diagnostic role of the SAB and RAS in cardiac amyloidosis. The SAB and RAS showed a gradual increase from carriers to patients with neurological and cardiac diseases. Thus, these parameters, in addition to atrial stiffness, could be used to monitor carriers. More extensive data are needed.

Right ventricular global strain in patients with hypertrophic cardiomyopathy with and without right ventricular hypertrophy

PURPOSE

Regardless of whether there are morphological abnormalities of right ventricle in hypertrophic cardiomyopathy (HCM) patients, the exact contribution of right ventricular (RV) global strains remains unresolved. We aimed to study the prognostic value of RV global strains in HCM patients with and without RV hypertrophy (RVH).

METHOD

A total of 358 HCM patients who underwent the CMR examination and carried out the follow-up were finally included in this retrospective study. The endpoint was a composite of all-cause mortality, aborted SCD, and heart failure readmission. RV hypertrophy (RVH) was defined as maximal RVWT ≥ 5 mm at end-diastole. RV global strains (RV global longitudinal strain (GLS) and RV global circumferential strain (GCS) were measured in HCM patients by cardiac MRI feature tracking technique. The intraobserver and interobserver reproducibility were evaluated. Receiver-operating characteristic curves and Kaplan-Meier curves, cox proportional hazards regression, Likelihood ratio test and Integrated Discrimination Improvement (IDI) analysis were performed. P-value were corrected for multiple testing when using many covariables by a false discovery rate adjustment.

RESULTS

Over a median follow-up of 25 (range 3-54) months, 49 patients reached the composite endpoints. HCM patients were divided into the RVH group and non-RVH groups. In the multivariate cox proportional hazards regression, after adjusting multiple clinical and imaging variables, RV GLS and RV GCS were independently associated with the composite endpoints in the RVH group (HR: 1.123; 95 % CI: 1.048-1.205; P = 0.002) and non-RVH group (HR: 1.174; 95 % CI: 1.031-1.337; P = 0.015), respectively. And The IDI index of models improved when adding RV GLS (IDI = 0.030, p < 0.001) and RV GLS (IDI = 0.056, p = 0.020), respectively.

CONCLUSIONS

RV GLS and RV GCS are independent predictors of HCM with RVH and without RVH, respectively. RV GLS in the RVH group and RV GCS in the non-RVH group provide additional values for predicting the risk of adverse events.

Reference Values for Inward Displacement in the Normal Left Ventricle: A Novel Method of Regional Left Ventricular Function Assessment

BACKGROUND

Regional functional left ventricular (LV) assessment using current imaging techniques remains limited. Inward displacement (InD) has been developed as a novel technique to assess regional LV function via measurement of the regional displacement of the LV endocardial border across each of the 17 LV segments. Currently, normal ranges for InD are not available for clinical use. The aim of this study was to validate the normal reference limits of InD in healthy adults across all LV segments.

METHODS

InD was analyzed in 120 healthy subjects with a normal LV ejection fraction, using the three standard long-axis views obtained during cardiac MRI that quantified the degree of inward endocardial wall motion towards the true LV center of contraction. For all LV segments, InD was measured in mm and expressed as a percentage of the theoretical degree of maximal segment contraction towards the true LV centerline. The arithmetic average InD was obtained for each of the 17 segments. The LV was divided into three regions, obtaining average InD at the LV base (segments 1-6), mid-cavity (segments 7-12) and apex (segments 13-17).

RESULTS

Average InD was 33.4 ± 4.3%. InD was higher in basal and mid-cavity LV segments (32.8 ± 4.1% and 38.1 ± 5.8%) compared to apical LV segments (28.6 ± 7.7%). Interobserver variability correlations for InD were strong (R = 0.80, p < 0.0001).

CONCLUSIONS

We provide clinically meaningful reference ranges for InD in subjects with normal LV function, which will emerge as an important screening and assessment imaging tool for a range of HFrEF therapies.

Left Atrial and Ventricular Strain Differentiates Cardiac Amyloidosis and Hypertensive Heart Disease: A Cardiac MR Feature Tracking Study

RATIONALE AND OBJECTIVES

Strain measured by feature tracking technique represents the degree of deformation and reflects the systolic and diastolic function of the heart. Our purpose was to evaluate the differential diagnostic value and correlations of left atrial (LA) strain (LAS) and left ventricular (LV) strain (LVS) in cardiac amyloidosis (CA) and hypertensive heart disease (HHD) patients.

MATERIALS AND METHODS

We recruited 25 CA patients, 30 sex- and age-matched HHD patients and 20 healthy subjects totally. LAS and LVS were analyzed by CVI42 post-processing software. The efficiency of LAS and LVS in differentiating CA from HHD was compared by receiver operating characteristic curves analysis. Pearson or Spearman's analysis were used to assess the correlation between LAS and LV parameters.

RESULTS

Both HHD and CA patients had impaired LVS, the gradient of increasing absolute values of longitudinal strain (LS) and radial strain (RS) from the basal to the apical myocardium was most pronounced in the CA group, its relative apical sparing of LS (RASLS) ratio reached 0.91 ± 0.02, significantly higher than other two groups (HHD: 0.72 ± 0.02; controls: 0.56 ± 0.01, all p <0.001). Additionally, except for the booster strain in the HHD group was preserved, all other LAS were reduced in patients' groups. The RASLS had the best differential diagnostic efficacy with an area under the curve (AUC) of 0.930 (p <0.001); The AUCs of LAS all greater than 0.850, above global LS (GLS) (AUC = 0.770, p = 0.001). LAS was notably correlated with LV ejection fraction (LVEF) and GLS, with reservoir strain having the greatest correlation with GLS (r = -0.828, p <0.001).

CONCLUSION

The RASLS has high efficiency in guiding the differential diagnosis of CA and HHD with similar degree and presentation of LVH. Moreover, LAS values can also provide some useful information and they are closely linked with LV function, CMR feature tracking may provide assistance in the evaluation of LA-LV coupling.

Machine Learning Approaches in Diagnosis, Prognosis and Treatment Selection of Cardiac Amyloidosis

Cardiac amyloidosis is an uncommon restrictive cardiomyopathy featuring an unregulated amyloid protein deposition that impairs organic function. Early cardiac amyloidosis diagnosis is generally delayed by indistinguishable clinical findings of more frequent hypertrophic diseases. Furthermore, amyloidosis is divided into various groups, according to a generally accepted taxonomy, based on the proteins that make up the amyloid deposits; a careful differentiation between the various forms of amyloidosis is necessary to undertake an adequate therapeutic treatment. Thus, cardiac amyloidosis is thought to be underdiagnosed, which delays necessary therapeutic procedures, diminishing quality of life and impairing clinical prognosis. The diagnostic work-up for cardiac amyloidosis begins with the identification of clinical features, electrocardiographic and imaging findings suggestive or compatible with cardiac amyloidosis, and often requires the histological demonstration of amyloid deposition. One approach to overcome the difficulty of an early diagnosis is the use of automated diagnostic algorithms. Machine learning enables the automatic extraction of salient information from “raw data” without the need for pre-processing methods based on the a priori knowledge of the human operator. This review attempts to assess the various diagnostic approaches and artificial intelligence computational techniques in the detection of cardiac amyloidosis.

Reference centiles for left ventricular longitudinal global and regional systolic strain by automated functional imaging in healthy Egyptian children

BACKGROUND

Two-dimensional speckle tracking echocardiography-derived left ventricular longitudinal systolic strain is an important myocardial deformation parameter for assessing the systolic function of the left ventricle. Strain values differ according to the vendor machine and software. This study aimed to provide normal reference values for global and regional left ventricular longitudinal systolic strain in Egyptian children using automated functional imaging software integrated into the General Electric healthcare machine and to study the correlation between the global longitudinal left ventricular systolic strain and age, body size, vital data, and some echocardiographic parameters.

METHODS

Healthy children (250) aged from 1 to 16 years were included. Conventional echocardiography was done to measure the left ventricular dimensions and function. Automated functional imaging was performed to measure the global and regional peak longitudinal systolic strain.

RESULTS

The global longitudinal strain was -21.224 ± 1.862%. The regional strain was -20.68 ± 2.11%, -21.06 ± 1.84%, and -21.86 ± 2.71% at the basal, mid, and apical segments, respectively. The mean values of the systolic longitudinal strain become significantly more negative from base to apex. Age differences were found as regard to global and regional longitudinal strain parameters but no gender differences. The global peak longitudinal systolic strain correlated positively with age. No correlations were found with either the anthropometric parameters or the vital data.

CONCLUSIONS

Age-specific normal values for two-dimensional speckle tracking-derived left ventricular longitudinal regional and global systolic strain are established using automated functional imaging.

Neural-Network-Based Diagnosis Using 3-Dimensional Myocardial Architecture and Deformation: Demonstration for the Differentiation of Hypertrophic Cardiomyopathy

The diagnosis of cardiomyopathy states may benefit from machine-learning (ML) based approaches, particularly to distinguish those states with similar phenotypic characteristics. Three-dimensional myocardial deformation analysis (3D-MDA) has been validated to provide standardized descriptors of myocardial architecture and deformation, and may therefore offer appropriate features for the training of ML-based diagnostic tools. We aimed to assess the feasibility of automated disease diagnosis using a neural network trained using 3D-MDA to discriminate hypertrophic cardiomyopathy (HCM) from its mimic states: cardiac amyloidosis (CA), Anderson–Fabry disease (AFD), and hypertensive cardiomyopathy (HTNcm). 3D-MDA data from 163 patients (mean age 53.1 ± 14.8 years; 68 females) with left ventricular hypertrophy (LVH) of known etiology was provided. Source imaging data was from cardiac magnetic resonance (CMR). Clinical diagnoses were as follows: 85 HCM, 30 HTNcm, 30 AFD, and 18 CA. A fully-connected-layer feed-forward neural was trained to distinguish HCM vs. other mimic states. Diagnostic performance was compared to threshold-based assessments of volumetric and strain-based CMR markers, in addition to baseline clinical patient characteristics. Threshold-based measures provided modest performance, the greatest area under the curve (AUC) being 0.70. Global strain parameters exhibited reduced performance, with AUC under 0.64. A neural network trained exclusively from 3D-MDA data achieved an AUC of 0.94 (sensitivity 0.92, specificity 0.90) when performing the same task. This study demonstrates that ML-based diagnosis of cardiomyopathy states performed exclusively from 3D-MDA is feasible and can distinguish HCM from mimic disease states. These findings suggest strong potential for computer-assisted diagnosis in clinical practice.