Position Statement on the Use of Myocardial Strain in Cardiology Routines by the Brazilian Society of Cardiology's Department Of Cardiovascular Imaging - 2023. / Posicionamento do Departamento de Imagem Cardiovascular da Sociedade Brasileira de Cardiologia sobre o Uso do Strain Miocárdico na Rotina do Cardiologista ­ 2023.

Central Illustration : Position Statement on the Use of Myocardial Strain in Cardiology Routines by the Brazilian Society of Cardiology's Department Of Cardiovascular Imaging - 2023 Proposal for including strain in the integrated diastolic function assessment algorithm, adapted from Nagueh et al.67 Am: mitral A-wave duration; Ap: reverse pulmonary A-wave duration; DD: diastolic dysfunction; LA: left atrium; LASr: LA strain reserve; LVGLS: left ventricular global longitudinal strain; TI: tricuspid insufficiency. Confirm concentric remodeling with LVGLS. In LVEF, mitral E wave deceleration time < 160 ms and pulmonary S-wave < D-wave are also parameters of increased filling pressure. This algorithm does not apply to patients with atrial fibrillation (AF), mitral annulus calcification, > mild mitral valve disease, left bundle branch block, paced rhythm, prosthetic valves, or severe primary pulmonary hypertension.

Figura Central : Posicionamento do Departamento de Imagem Cardiovascular da Sociedade Brasileira de Cardiologia sobre o Uso do Strain Miocárdico na Rotina do Cardiologista ­ 2023 Proposta de inclusão do strain no algoritmo integrado de avaliação da função diastólica, adaptado e traduzido de Nagueh et al. 67 AE: átrio esquerdo; Ap: duração da onda A reversa pulmonar; Am: duração da onda A mitral; DD: disfunção diastólica; FEVEr: fração de ejeção do ventrículo esquerdo reduzida; IT: insuficiência tricúspide; SAEr: strain do AE de reservatório; SLGVE: strain longitudinal global do ventrículo esquerdo. Se remodelamento concêntrico, confirmar com SLGVE. Na presença de FEVEr, tempo de desaceleração da onda E mitral (TDE) < 160 ms e onda S < D pulmonar também são parâmetros de pressão de enchimento aumentada. Esse algoritmo não se aplica a pacientes com fibrilação atrial (FA), calcificação do anel mitral ou valvopatia mitral maior que discreta, bloqueio de ramo esquerdo (BRE), ritmo de marca-passo, próteses valvares ou hipertensão pulmonar (HP) primária grave.

A machine learning framework for the evaluation of myocardial rotation in patients with noncompaction cardiomyopathy.

AIMS: Noncompaction cardiomyopathy (NCC) is considered a genetic cardiomyopathy with unknown pathophysiological mechanisms. We propose to evaluate echocardiographic predictors for rigid body rotation (RBR) in NCC using a machine learning (ML) based model. METHODS AND RESULTS: Forty-nine outpatients with NCC diagnosis by echocardiography and magnetic resonance imaging (21 men, 42.8±14.8 years) were included. A comprehensive echocardiogram was performed. The layer-specific strain was analyzed from the apical two-, three, four-chamber views, short axis, and focused right ventricle views using 2D echocardiography (2DE) software. RBR was present in 44.9% of patients, and this group presented increased LV mass indexed (118±43.4 vs. 94.1±27.1g/m2, P = 0.034), LV end-diastolic and end-systolic volumes (P< 0.001), E/e' (12.2±8.68 vs. 7.69±3.13, P = 0.034), and decreased LV ejection fraction (40.7±8.71 vs. 58.9±8.76%, P < 0.001) when compared to patients without RBR. Also, patients with RBR presented a significant decrease of global longitudinal, radial, and circumferential strain. When ML model based on a random forest algorithm and a neural network model was applied, it found that twist, NC/C, torsion, LV ejection fraction, and diastolic dysfunction are the strongest predictors to RBR with accuracy, sensitivity, specificity, area under the curve of 0.93, 0.99, 0.80, and 0.88, respectively. CONCLUSION: In this study, a random forest algorithm was capable of selecting the best echocardiographic predictors to RBR pattern in NCC patients, which was consistent with worse systolic, diastolic, and myocardium deformation indices. Prospective studies are warranted to evaluate the role of this tool for NCC risk stratification.

Smoking accelerates renal cystic disease and worsens cardiac phenotype in Pkd1-deficient mice.

Smoking has been associated with renal disease progression in ADPKD but the underlying deleterious mechanisms and whether it specifically worsens the cardiac phenotype remain unknown. To investigate these matters, Pkd1-deficient cystic mice and noncystic littermates were exposed to smoking from conception to 18 weeks of age and, along with nonexposed controls, were analyzed at 13-18 weeks. Renal cystic index and cyst-lining cell proliferation were higher in cystic mice exposed to smoking than nonexposed cystic animals. Smoking increased serum urea nitrogen in cystic and noncystic mice and independently enhanced tubular cell proliferation and apoptosis. Smoking also increased renal fibrosis, however this effect was much higher in cystic than in noncystic animals. Pkd1 deficiency and smoking showed independent and additive effects on reducing renal levels of glutathione. Systolic function and several cardiac structural parameters were also negatively affected by smoking and the Pkd1-deficient status, following independent and additive patterns. Smoking did not increase, however, cardiac apoptosis or fibrosis in cystic and noncystic mice. Notably, smoking promoted a much higher reduction in body weight in Pkd1-deficient than in noncystic animals. Our findings show that smoking aggravated the renal and cardiac phenotypes of Pkd1-deficient cystic mice, suggesting that similar effects may occur in human ADPKD.

Early changes in myocyte contractility and cardiac function in streptozotocin-induced type 1 diabetes in rats.

Diabetes can elicit direct deleterious effects on the myocardium, independent of coronary artery disease or hypertension. These cardiac disturbances are termed diabetic cardiomyopathy showing increased risk of heart failure with or without reduced ejection fraction. Presently, there is no specific treatment for this type of cardiomyopathy and in the case of type I diabetes, it may start in early childhood independent of glycemic control. We hypothesized that alterations in isolated myocyte contractility and cardiac function are present in the early stages of experimental diabetes in rats before overt changes in myocardium structure occur. Diabetes was induced by single-dose injection of streptozotocin (STZ) in rats with data collected from control and diabetic animals 3 weeks after injection. Left ventricle myocyte contractility was measured by single-cell length variation under electrical stimulation. Cardiac function and morphology were studied by high-resolution echocardiography with pulsed-wave tissue Doppler imaging (TDI) measurements and three-lead surface electrocardiogram. Triglycerides, cholesterol and liver enzyme levels were measured from plasma samples obtained from both groups. Myocardial collagen content and perivascular fibrosis of atria and ventricle were studied by histological analysis after picrosirius red staining. Diabetes resulted in altered contractility of isolated cardiac myocytes with increased contraction and relaxation time intervals. Echocardiography showed left atrium dilation, increased end-diastolic LV and posterior wall thickness, with reduced longitudinal systolic peak velocity (S') of the septum mitral annulus at the apical four-chamber view obtained by TDI. Triglycerides, aspartate aminotransferase and alkaline phosphatase were elevated in diabetic animals. Intertitial collagen content was higher in atria of both groups and did not differ among control and diabetic animals. Perivascular intramyocardial arterioles collagen did not differ between groups. These results suggest that alterations in cardiac function are present in the early phase in this model of diabetes type 1 and occur before overt changes in myocardium structure appear as evaluated by intersticial collagen deposition and perivascular fibrosis of intramyocardial arterioles.

Biventricular imaging markers to predict outcomes in non-compaction cardiomyopathy: a machine learning study.

AIMS: Left ventricular non-compaction cardiomyopathy (LVNC) is a genetic heart disease, with heart failure, arrhythmias, and embolic events as main clinical manifestations. The goal of this study was to analyse a large set of echocardiographic (echo) and cardiac magnetic resonance imaging (CMRI) parameters using machine learning (ML) techniques to find imaging predictors of clinical outcomes in a long-term follow-up of LVNC patients. METHODS AND RESULTS: Patients with echo and/or CMRI criteria of LVNC, followed from January 2011 to December 2017 in the heart failure section of a tertiary referral cardiologic hospital, were enrolled in a retrospective study. Two-dimensional colour Doppler echocardiography and subsequent CMRI were carried out. Twenty-four hour Holter monitoring was also performed in all patients. Death, cardiac transplantation, heart failure hospitalization, aborted sudden cardiac death, complex ventricular arrhythmias (sustained and non-sustained ventricular tachycardia), and embolisms (i.e. stroke, pulmonary thromboembolism and/or peripheral arterial embolism) were registered and were referred to as major adverse cardiovascular events (MACEs) in this study. Recruited for the study were 108 LVNC patients, aged 38.3 ± 15.5 years, 48.1% men, diagnosed by echo and CMRI criteria. They were followed for 5.8 ± 3.9 years, and MACEs were registered. CMRI and echo parameters were analysed via a supervised ML methodology. Forty-seven (43.5%) patients had at least one MACE. The best performance of imaging variables was achieved by combining four parameters: left ventricular (LV) ejection fraction (by CMRI), right ventricular (RV) end-systolic volume (by CMRI), RV systolic dysfunction (by echo), and RV lower diameter (by CMRI) with accuracy, sensitivity, and specificity rates of 75.5%, 77%, 75%, respectively. CONCLUSIONS: Our findings show the importance of biventricular assessment to detect the severity of this cardiomyopathy and to plan for early clinical intervention. In addition, this study shows that even patients with normal LV function and negative late gadolinium enhancement had MACE. ML is a promising tool for analysing a large set of parameters to stratify and predict prognosis in LVNC patients.