Left ventricular and atrial myocardial strain in heart failure with preserved ejection fraction: the evidence so far and prospects for phenotyping strategy.

BACKGROUND: Heart failure with preserved ejection fraction (HFpEF) represents a significant proportion of heart failure cases. Accurate diagnosis is challenging due to the heterogeneous nature of the disease and limitations in traditional echocardiographic parameters. MAIN BODY: This review appraises the application of Global Longitudinal Strain (GLS) and Left Atrial Strain (LAS) as echocardiographic biomarkers in the diagnosis and phenotyping of HFpEF. Strain imaging, particularly Speckle Tracking Echocardiography, offers a superior assessment of myocardial deformation, providing a more detailed insight into left heart function than traditional metrics. Normal ranges for GLS and LAS are considered, acknowledging the impact of demographic and technical factors on these values. Clinical studies have demonstrated the prognostic value of GLS and LAS in HFpEF, especially in predicting cardiovascular outcomes and distinguishing HFpEF from other causes of dyspnea. Nevertheless, the variability of strain measurements and the potential for false-negative results underline the need for careful clinical interpretation. The HFA-PEFF scoring system's integration of these biomarkers, although systematic, reveals gaps in addressing the full spectrum of HFpEF pathology. The combined use of GLS and LAS has been suggested to define HFpEF phenogroups, which could lead to more personalized treatment plans. CONCLUSION: GLS and LAS have emerged as pivotal tools in the non-invasive diagnosis and stratification of HFpEF, offering a promise for tailored therapeutic strategies. Despite their potential, a structured approach to incorporating these biomarkers into standard diagnostic workflows is essential. Future clinical guidelines should include clear directives for the combined utilization of GLS and LAS, accentuating their role in the multidimensional assessment of HFpEF.

The association between reconstructed phase space and Artificial Neural Networks for vectorcardiographic recognition of myocardial infarction.

Myocardial infarction is one of the leading causes of death worldwide. As it is life threatening, it requires an immediate and precise treatment. Due to this, a growing number of research and innovations in the field of biomedical signal processing is in high demand. This paper proposes the association of Reconstructed Phase Space and Artificial Neural Networks for Vectorcardiography Myocardial Infarction Recognition. The algorithm promotes better results for the box size 10 × 10 and the combination of four parameters: box counting (Vx), box counting (Vz), self-similarity method (Vx) and self-similarity method (Vy) with sensitivity = 92%, specificity = 96% and accuracy = 94%. The topographic diagnosis presented different performances for different types of infarctions with better results for anterior wall infarctions and less accurate results for inferior infarctions.

Afterload increase challenge unmasks systolic abnormalities in heart failure with preserved ejection fraction.

BACKGROUND: Provocative maneuvers have the potential to overcome the low sensitivity of resting echocardiography and biomarkers in the detection of heart failure with preserved ejection fraction (HFpEF). We investigate the mechanical response of the left ventricle to an afterload challenge in patients with preclinical and early-stage HFpEF (es-HFpEF). METHODS: Three groups of patients (non-HFpEF - n = 42, pre-HFpEF - n = 43, and es-HFpEF - n = 39) underwent echocardiography at rest and during an afterload challenge induced by handgrip maneuver combined with pneumatic constriction of limbs. RESULTS: Patients in the non-HF group displayed a median ΔLPSS = -4% (IQR: -10%, +2%), LPSS rest<16% in 3/42(7%) and LPSS stress<16% in 6/43(14%). Subjects in the pre-HFpEF group displayed median ΔLPSS = -3% (IQR: -10%, +5%) LPSS rest<16% in 13/43(30%) and LPSS stress<16% in 19/43 (44%). 11/43 (25%) subjects in this group increased at least one absolute point in LPSS during stress. Patients in es-HFpEF group displayed a median ΔLPSS = -10% (IQR: -18%, -1%), LPSS rest<16% in 15/39(38%) and LPSS stress<16% in 25/39(64%). Changes in LPSS (ΔLPSS) were significantly greater in es-HFpEF than pre-HFpEF (p = 0.022). In multivariate analysis, this group effect was maintained after adjustment of the LPSS for systolic blood pressure, use of ß-blockers, LV mass, RWT, age, and sex. CONCLUSION: Our data suggest that patients with HFpEF have a marked decrease in peak strain during acute pressure overload. Longitudinal studies are needed to test and compare the clinical impact of each pattern in early and long-term follow-ups.

Mechanical dispersion is a superior echocardiographic feature to predict exercise capacity in preclinical and overt heart failure with preserved ejection fraction.

BACKGROUND: Heart Failure with Preserved Ejection Fraction (HFpEF) is a syndrome characterized by different degrees of exercise intolerance, which leads to poor quality of life and prognosis. Recently, the European score (HFA-PEFF) was proposed to standardize the diagnosis of HFpEF. Even though Global Longitudinal Strain (GLS) is a component of HFA-PEFF, the role of other strain parameters, such as Mechanical Dispersion (MD), has yet to be studied. In this study, we aimed to compare MD and other features from the HFA-PEFF according to their association with exercise capacity in an outpatient population of subjects at risk or suspected HFpEF. METHODS: This is a single-center cross-sectional study performed in an outpatient population of 144 subjects with a median age of 57 years, 58% females, referred to the Echocardiography and Cardiopulmonary Exercise Test to investigate HFpEF. RESULTS: MD had a higher correlation to Peak VO2 (r=-0.43) when compared to GLS (r=-0.26), MD presented a significant correlation to Ventilatory Anaerobic Threshold (VAT) (r=-0.20; p = 0.04), while GLS showed no correlation (r=-0.14; p = 0.15). Neither MD nor GLS showed a correlation with the time to recover VO2 after exercise (T1/2). In Receiver Operator Characteristic (ROC) analysis, MD presented superior performance to GLS to predict Peak VO2 (AUC: 0.77 vs. 0.62), VAT (AUC: 0.61 vs. 0.57), and T1/2 (AUC: 0.64 vs. 0.57). Adding MD to HFA-PEFF improved the model performance (AUC from 0.77 to 0.81). CONCLUSION: MD presented a higher association with Peak VO2 when compared to GLS and most features from the HFA-PEFF. Adding MD to the HFA-PEFF improved the model performance.

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.