Evaluation of Right Ventricular Myocardial Mechanics by 2- and 3-Dimensional Speckle-Tracking Echocardiography in Patients With an Ischemic or Non-ischemic Etiology of End-Stage Heart Failure

Cardiac Magnetic Resonance Imaging in the Evaluation of Functional Impairments in the Right Heart

Cardiac magnetic resonance (cMRI) imaging has recently become essential in cardiology. cMRI is widely recognized as the most reliable imaging technique for assessing the size and performance of the right ventricle. It allows for objective and functional cardiac tissue evaluations. Early in disease progression, cardiac structure and activity decrease subclinically. Late-phase clinically visible signs have been associated with less favourable outcomes. Subclinical alterations ought to be recognized for rapid evaluations and accurate treatment. An increasing amount of evidence supports cMRI deformation parameter quantification. Strain imaging enables cardiologists to assess heart function beyond traditional measurements. Prognostic information for cardiovascular disease patients is obtained through the right ventricle (RV) strain, including information primarily about the left ventricle (LV). Right atrial (RA) function evaluations using RA strain have been promising in recent studies. Therefore, this narrative review aims to present an overview of the data that are currently available for assessing right myocardial strain and biomechanics using cMRI.

Cine-cardiac magnetic resonance to distinguish between ischemic and non-ischemic cardiomyopathies: a machine learning approach

OBJECTIVE

This work aimed to derive a machine learning (ML) model for the differentiation between ischemic cardiomyopathy (ICM) and non-ischemic cardiomyopathy (NICM) on non-contrast cardiovascular magnetic resonance (CMR).

METHODS

This retrospective study evaluated CMR scans of 107 consecutive patients (49 ICM, 58 NICM), including atrial and ventricular strain parameters. We used these data to compare an explainable tree-based gradient boosting additive model with four traditional ML models for the differentiation of ICM and NICM. The models were trained and internally validated with repeated cross-validation according to discrimination and calibration. Furthermore, we examined important variables for distinguishing between ICM and NICM.

RESULTS

A total of 107 patients and 38 variables were available for the analysis. Of those, 49 were ICM (34 males, mean age 60 ± 9 years) and 58 patients were NICM (38 males, mean age 56 ± 19 years). After 10 repetitions of the tenfold cross-validation, the proposed model achieved the highest area under curve (0.82, 95% CI [0.47-1.00]) and lowest Brier score (0.19, 95% CI [0.13-0.27]), showing competitive diagnostic accuracy and calibration. At the Youden's index, sensitivity was 0.72 (95% CI [0.68-0.76]), the highest of all. Analysis of predictions revealed that both atrial and ventricular strain CMR parameters were important for the identification of ICM patients.

CONCLUSION

The current study demonstrated that using a ML model, multi chamber myocardial strain, and function on non-contrast CMR parameters enables the discrimination between ICM and NICM with competitive diagnostic accuracy.

CLINICAL RELEVANCE STATEMENT

A machine learning model based on non-contrast cardiovascular magnetic resonance parameters may discriminate between ischemic and non-ischemic cardiomyopathy enabling wider access to cardiovascular magnetic resonance examinations with lower costs and faster imaging acquisition.

KEY POINTS

• The exponential growth in cardiovascular magnetic resonance examinations may require faster and more cost-effective protocols. • Artificial intelligence models can be utilized to distinguish between ischemic and non-ischemic etiologies. • Machine learning using non-contrast CMR parameters can effectively distinguish between ischemic and non-ischemic cardiomyopathies.

Quantitative Assessment of Right Ventricular Function in Patients With Systemic Lupus Erythematosus Using the Novel Non-invasive Pressure-Strain Loop

OBJECTIVE

Current echocardiography evaluation of right ventricular (RV) function, which heralds the prognosis in patients with systemic lupus erythematosus (SLE), is of limited utility. The non-invasive pressure-strain loop (PSL), an emerging technique, has been found to feasible, sensitive and accurate in the diagnosis of cardiovascular diseases. The aim of this study was to quantitatively evaluate, using the non-invasive PSL, the right ventricular myocardial work (RVMW) in SLE patients.

METHODS

Seventy-five SLE patients were recruited and grouped by pulmonary artery systolic pressure (PASP) into normal (group A, N = 26), mild (group B, N = 22) and moderate to severe (group C, N = 27) groups. Twenty-five healthy volunteers undergoing physical examination were recruited as the control group. Right ventricular global myocardial work index (RVGWI), global constructive work (RVGCW), global wasted work (RVGWW), global work efficiency (RVGWE), global longitudinal strain (RVGLS) and other conventional parameters were measured.

DISCUSSION

There were no differences between group A and the control group with respect to RVLS, RVGLS and all RVMW parameters (all p values > 0.05). RVGWI and RVGCW significantly differed among the other groups (all p values < 0.05). RVGWE was significantly lower and RVGWW was significantly higher in group C than in the control group and groups A and B (all p values < 0.05). Compared with the control group, RVGWW was significantly increased and RVGLS was significantly decreased in group B (all p values < 0.05). All but one RVMW parameter moderately to strongly correlated with SLE disease activity index (SLEDAI) and World Health Organization Functional Class (WHO-FC). RVGWW (area under the receiver operating characteristic curve [AUC] = 0.893) and RVGWE (AUC = 0.877) were sensitive parameters in detecting earlier cardiac dysfunction in SLE patients.

CONCLUSION

RVGWW and RVGWE serve as sensitive and promising parameters in the integrative analysis of early right ventricular dysfunction in SLE patients. To conclude, non-invasive PSL, the novel method, facilitates the quantitative assessment of RVMW in SLE patients.

Pharmacological clearance of senescent cells improves cardiac remodeling and function after myocardial infarction in female aged mice

Cardiovascular diseases (CVD) are predominantly an aging disease. Important sex-specific differences exist and the mechanism(s) by which this sex-by-age interaction influences CVD development and progression remains elusive. Accordingly, it is still unknown whether cell senescence, a main feature of cardiac male aging, is a significant feature also of the female aged mouse heart and whether senolytics, senescence-clearing compounds, promote myocardial repair and regeneration after myocardial infarction (MI) in aged female mice. To this aim, the combination of two senolytics, dasatinib and quercetin (D+Q) or just their vehicle was administered to 22-24 months old C57BL/6 female mice after MI. D+Q improved global left ventricle function and myocardial performance after MI whereby female cardiac aging is characterized by accumulation of cardiac senescent cells that are further increased by MI. Despite their terminal differentiation nature, also cardiomyocytes acquire a senescent phenotype with age in females. D+Q removed senescent cardiac non-myocyte and myocyte cells ameliorating cardiac remodeling and regeneration. Senolytics removed aged dysfunctional cardiac stem/progenitor cells (CSCs), relieving healthy CSCs with normal proliferative and cardiomyogenic differentiation potential. In conclusions, cardiac senescent cells accumulate in the aged female hearts. Removing senescent cells is a key therapeutic target for efficient repair of the aged female heart.