Ventricular longitudinal function by cardiovascular magnetic resonance predicts cardiovascular morbidity in HFrEF patients

Noninvasive Pressure-Volume Loops Predict Major Adverse Cardiac Events in Heart Failure With Reduced Ejection Fraction

Background

Heart failure with reduced ejection fraction (HFrEF) is characterized by ventricular remodeling and impaired myocardial energetics. Left ventricular pressure-volume (PV) loop analysis can be performed noninvasively using cardiovascular magnetic resonance (CMR) imaging to assess cardiac thermodynamic efficiency.

Objectives

The aim of the study was to investigate whether noninvasive PV loop parameters, derived from CMR, could predict major adverse cardiac events (MACE) in HFrEF patients.

Methods

PV loop parameters (stroke work, ventricular efficiency, external power, contractility, and energy per ejected volume) were computed from CMR cine images and brachial blood pressure. The primary end point was MACE (cardiovascular death, heart failure (HF) hospitalization, myocardial infarction, revascularization, ventricular tachycardia/fibrillation, heart transplantation, or left ventricular assist device implantation within 5 years). Associations between PV loop parameters and MACE were evaluated using multivariable Cox regression.

Results

One hundred and sixty-four HFrEF patients (left ventricular ejection fraction ≤40%, age 63 [IQR: 55-70] years, 79% male) who underwent clinical CMR examination between 2004 and 2014 were included. Eighty-eight patients (54%) experienced at least one MACE after an average of 2.8 years. Unadjusted models demonstrated a significant association between MACE and all PV loop parameters (P < 0.05 for all), HF etiology (P < 0.001), left ventricular ejection fraction (P = 0.003), global longitudinal strain (P < 0.001), and N-terminal prohormone of brain natriuretic peptide level (P = 0.001). In the multivariable Cox regression analysis adjusted for age, sex, hypertension, diabetes, and HF etiology, ventricular efficiency was associated with MACE (HR: 1.04 (95% CI: 1.01-1.08) per-% decrease, P = 0.01).

Conclusions

Ventricular efficiency, derived from noninvasive PV loop analysis from standard CMR scans, is associated with MACE in patients with HFrEF.

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.

Automatic assessment of left ventricular function for hemodynamic monitoring using artificial intelligence and transesophageal echocardiography

We have developed a method to automatically assess LV function by measuring mitral annular plane systolic excursion (MAPSE) using artificial intelligence and transesophageal echocardiography (autoMAPSE). Our aim was to evaluate autoMAPSE as an automatic tool for rapid and quantitative assessment of LV function in critical care patients. In this retrospective study, we studied 40 critical care patients immediately after cardiac surgery. First, we recorded a set of echocardiographic data, consisting of three consecutive beats of midesophageal two- and four-chamber views. We then altered the patient's hemodynamics by positioning them in anti-Trendelenburg and repeated the recordings. We measured MAPSE manually and used autoMAPSE in all available heartbeats and in four LV walls. To assess the agreement with manual measurements, we used a modified Bland-Altman analysis. To assess the precision of each method, we calculated the least significant change (LSC). Finally, to assess trending ability, we calculated the concordance rates using a four-quadrant plot. We found that autoMAPSE measured MAPSE in almost every set of two- and four-chamber views (feasibility 95%). It took less than a second to measure and average MAPSE over three heartbeats. AutoMAPSE had a low bias (0.4 mm) and acceptable limits of agreement (- 3.7 to 4.5 mm). AutoMAPSE was more precise than manual measurements if it averaged more heartbeats. AutoMAPSE had acceptable trending ability (concordance rate 81%) during hemodynamic alterations. In conclusion, autoMAPSE is feasible as an automatic tool for rapid and quantitative assessment of LV function, indicating its potential for hemodynamic monitoring.