Heart failure with preserved ejection fraction, atrial fibrillation, and increased NT‑proBNP levels : An emergent clinical dilemma

Characteristics and prognostic value of cardiac magnetic resonance strain analysis in patients with different phenotypes of heart failure

Background

Strain analysis of cardiac magnetic resonance imaging (CMR) is important for the prognosis of heart failure (HF). Herein, we aimed to identify the characteristics and prognostic value of strain analysis revealed by CMR in different HF phenotypes.

Methods

Participants with HF, including HF with reduced ejection fraction, HF with mildly reduced ejection fraction, and HF with preserved ejection fraction, and controls were enrolled. The baseline information and clinical parameters of participants were collected, and echocardiography and CMR examination were performed. Three-dimensional strain analysis was performed in the left ventricle, right ventricle, left atrium, and right atrium using CMR. A multifactor Cox risk proportional model was established to assess the influencing factors of cardiovascular adverse events in patients with HF.

Results

During a median follow-up of 999 days (range: 616-1334), 20.6% of participants (73/354) experienced adverse events (HF readmission and/or cardiovascular death). Univariable Cox regression revealed that a 1% increase in left atrial global longitudinal strain (LAGLS) was associated with a hazard ratio (HR) of 1.21 [95% confidence interval (CI):1.15-1.28; P < 0.001]. Left ventricular global circumferential strain (LVGCS) (HR, 1.18; 95% CI: 1.12-1.24; P < 0.001), and left ventricular global longitudinal strain (LVGLS) (HR, 1.27; 95% CI: 1.20-1.36; P < 0.001) were also associated with HF hospitalizations and cardiovascular deaths. Among clinical variables, hypertension (HR, 2.11; 95% CI: 1.33-13.36; P = 0.002), cardiomyopathy (HR, 2.26; 95% CI: 1.42-3.60; P < 0.001) were associated with outcomes in univariable analysis. Multivariable analyses revealed that LAGLS (95% CI: 1.08-1.29; P < 0.001), LVGLS (95% CI:1.08-1.29; P < 0.001) and LVGCS (95% CI: 1.19-1.51; P < 0.001) were significantly associated with outcomes. Among clinical variables, hypertension (95% CI: 1.09-3.73; P < 0.025) remained a risk factor.

Conclusion

CMR plays an obvious role in phenotyping HF. Strain analysis, particularly left atrial and left ventricular strain analysis (LAGLS, LVGLS, and LVGCS) has good value in predicting adverse outcome events.

Prognosticators of All-Cause Mortality in Patients With Heart Failure With Preserved Ejection Fraction

Heart failure with preserved ejection fraction (HFpEF) represents ∼50% of all cases of congestive heart failure (CHF) with prevalence expected to increase with aging of the population. We performed an observational study of all patients admitted to 3 hospitals in the ExcelaHealth care system, Greensburg, PA, with a primary diagnosis of HFpEF heart failure exacerbation between January 2014 and January 2017. Demographic data, laboratory results, and echocardiograms performed closest to index hospitalization were collected. A total of 487 patients were admitted with a primary diagnosis of CHF exacerbation and HFpEF, with a mean age of 80.5 years (±10.9), 62% women and predominantly Caucasian (98.8%). Over a median follow-up of 21.7 months, 246 patients died with an all-cause mortality rate of 51.3%. Receiver operator curves were generated for multiple continuous variables to identify optimal cut-off values Kaplan-Meir survival curves were then generated. Clinical factors were tested by univariate Cox regression modeling, with significant factors entered into a step-wise multivariate model. Our modeling identified age>80 years, serum albumin level<3.2 g/dl, N-terminal pro-brain natriuretic peptide (NT-proBNP) >5,000 pg/mL and medial E/e'≥20 as significant, independent predictors of all-cause mortality (p-value <0.0001). Surprisingly, lack of a diagnosis of hypertension was associated with significantly increased mortality risk. In a community-based sample of HFpEF patients, we identified multiple factors that were strong, independent predictors of all-cause mortality that can be easily applied in a clinical setting.