Aims

The relationship between resting stroke volume (SV) and prognostic markers in heart failure with preserved ejection fraction (HFpEF) is not well established. We evaluated the association of SV index (SVI) at rest with exercise capacity and N-terminal pro-B-type natriuretic peptide (NT-proBNP) in stable patients with HFpEF.

Methods and results

Participants enrolled in the Phosphodiesterase-5 Inhibition to Improve Clinical Status and Exercise Capacity in Diastolic Heart Failure (RELAX) trial with available data on SVI by the Doppler method were included in this analysis (n = 185). A low-flow state defined by resting SVI < 35 mL/m² was present in 37% of study participants. Multivariable adjusted linear regression analysis suggested that higher resting heart rate, higher body weight, prevalent atrial fibrillation, and smaller left ventricular (LV) end-diastolic dimension were each independently associated with lower SVI. Patients with low-flow HFpEF had lower systolic blood pressure and smaller LV end-diastolic dimension. In multivariable adjusted linear regression models, lower SVI was significantly associated with lower peak oxygen consumption (peak VO₂ ) and higher NT-proBNP levels at baseline, and greater decline in peak VO₂ at 6 month follow-up independent of other confounders. Resting LV ejection fraction was not associated with peak VO₂ and NT-proBNP levels.

Conclusions

There is heterogeneity in the resting SVI distribution among patients with stable HFpEF, with more than one-third of patients identified with the low-flow HFpEF phenotype (SVI < 35 mL/m² ). Lower SVI was independently associated with lower peak VO₂ , higher NT-proBNP levels, and greater decline in peak VO₂ . These findings highlight the potential prognostic utility of SVI assessment in the management of patients with HFpEF.

Objective

To evaluate the associations between long-term change and variability in glycemia with risk of heart failure (HF) among patients with type 2 diabetes mellitus (T2DM).

Research design and methods

Among participants with T2DM enrolled in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial, variability in HbA_1c was assessed from stabilization of HbA_1c following enrollment (8 months) to 3 years of follow-up as follows: average successive variability (ASV) (average absolute difference between successive values), coefficient of variation (SD/mean), and SD. Participants with HF at baseline or within 3 years of enrollment were excluded. Adjusted Cox models were used to evaluate the association of percent change (from baseline to 3 years of follow-up) and variability in HbA_1c over the first 3 years of enrollment and subsequent risk of HF.

Results

The study included 8,576 patients. Over a median follow-up of 6.4 years from the end of variability measurements at year 3, 388 patients had an incident HF hospitalization. Substantial changes in HbA_1c were significantly associated with higher risk of HF (hazard ratio [HR] for ≥10% decrease 1.32 [95% CI 1.08-1.75] and for ≥10% increase 1.55 [1.19-2.04]; reference <10% change in HbA_1c). Greater long-term variability in HbA_1c was significantly associated with higher risk of HF (HR per 1 SD of ASV 1.34 [95% CI 1.17-1.54]) independent of baseline risk factors and interval changes in cardiometabolic parameters. Consistent patterns of association were observed with use of alternative measures of glycemic variability.

Conclusions

Substantial long-term changes and variability in HbA_1c were independently associated with risk of HF among patients with T2DM.

BACKGROUND: Duchenne muscular dystrophy (DMD) is frequently complicated by development of a cardiomyopathy. Despite significant medical advances provided to DMD patients over the past 2 decades, there remains a group of DMD patients who die prematurely. The current study sought to identify a set of prognostic factors that portend a worse outcome among adult DMD patients. METHODS AND RESULTS: A retrospective cohort of 43 consecutive patients was followed in the adult UT Southwestern Neuromuscular Cardiomyopathy Clinic. Clinical data were abstracted from the electronic medical record to generate baseline characteristics. The population was stratified by survival to time of analysis and compared with characteristics associated with death. The DMD population was in the early 20s, with median follow-up times over 2 years. All the patients had developed a cardiomyopathy, with the majority of the patients on angiotensin-converting enzyme inhibitors (86%) and steroids (56%), but few other guideline-directed heart failure medications. Comparison between the nonsurviving and surviving cohorts found several poor prognostic factors, including lower body mass index (17.3 [14.8-19.3] versus 25.8 [20.8-29.1] kg/m2, P<0.01), alanine aminotransferase levels (26 [18-42] versus 53 [37-81] units/L, P=0.001), maximum inspiratory pressures (13 [0-30] versus 33 [25-40] cmH2O, P=0.03), and elevated cardiac biomarkers (N-terminal pro-brain natriuretic peptide: 288 [72-1632] versus 35 [21-135] pg/mL, P=0.03]. CONCLUSIONS: The findings demonstrate a DMD population with a high burden of cardiomyopathy. The nonsurviving cohort was comparatively underweight, and had worse respiratory profiles and elevated cardiac biomarkers. Collectively, these factors highlight a high-risk cardiovascular population with a worse prognosis.

Bloodstream infections (BSIs) are common in patients with continuous-flow left ventricular assist devices (CF-LVADs). Whether CF-LVADs modulate the febrile response to BSIs is unknown. We retrospectively compared the febrile response to BSIs in patients with heart failure (HF) with CF-LVADs versus a control population of patients with HF receiving inotropic infusions. BSIs were adjudicated using the Centers for Disease Control and Prevention and the National Healthcare Safety Network criteria. Febrile status (temperature ≥38°C, 100.4 °F), temperature at presentation with BSI, and the highest temperature within 72 hours (Tmax) were collected. We observed 59 BSIs in LVAD patients and 45 BSIs in controls. LVAD patients were more likely to be afebrile and to have a lower temperature at presentation than control (88% vs 58%, p=0.002, and 37°C ±0.7 vs 37.7°C ±1.0, p=0.0009, respectively). By 72 hours, the difference in afebrile status diminished (53% vs 44%, p=0.42), and the Tmax was similar between the LVAD and control groups (37.9°C±0.9 vs 38.2°C±0.8, respectively, p=0.10). In conclusion, at presentation with a BSI, the vast majority of CF-LVAD patients were afebrile, an event which occurred at a higher frequency when compared with patients with advanced HF on chronic inotropes via an indwelling venous catheter. These data alert clinicians to have a very low threshold to obtain blood cultures in CF-LVAD patients even in the absence of fever. Further study is needed to determine whether a delayed or diminished febrile response represents another pathophysiological consequence of CF-LVADs.

BACKGROUND: In ≈25% of patients with heart failure and reduced left-ventricular ejection fraction, right-ventricular (RV), and left-ventricular (LV) filling pressures are discordant (ie, one is elevated while the other is not). Whether clinical assessment allows detection of this discordance is unknown. We sought to determine the agreement of clinically versus invasively determined patterns of ventricular congestion. METHODS: In 156 heart failure and reduced LV ejection fraction subjects undergoing invasive hemodynamic assessment, we categorized patterns of ventricular congestion (no congestion, RV only, LV only, or both) based on clinical findings of RV (jugular venous distention) or LV (hepatojugular reflux, orthopnea, or bendopnea) congestion. Agreement between clinically and invasively determined (RV congestion if right atrial pressure [RAP] ≥10 mm Hg and LV congestion if pulmonary capillary wedge pressure [PCWP] ≥22 mm Hg) categorizations was the primary end point. RESULTS: The frequency of clinical patterns of congestion was: 51% no congestion, 24% both RV and LV, 21% LV only, and 4% RV only. Jugular venous distention had excellent discrimination for elevated RAP (C=0.88). However, agreement between clinical and invasive congestion patterns was poor, к=0.44 (95% CI, 0.34-0.55). While those with no clinical congestion usually had low RAP and PCWP (67/79, 85%), over one-half (24/38, 64%) with isolated LV clinical congestion had PCWP <22 mm Hg, most (5/7, 71%) with isolated RV clinical congestion had PCWP ≥22 mm Hg, and ≈one-third (10/32, 31%) with both RV and LV clinical congestion had elevated RAP but PCWP <22 mm Hg. CONCLUSIONS: While clinical examination allows accurate detection of elevated RAP, it does not allow accurate detection of discordant RV and LV filling pressures.

Background

The burden of amyloidosis among hospitalized patients is increasing over time. However, amyloidosis remains an underdiagnosed cause of heart failure (HF) hospitalization among older adults.

Objectives

We investigated the prevalence and prognostic implications of amyloidosis among patients hospitalized with HF.

Methods

All hospitalizations for primary diagnosis of HF between January 1, 2010, and August 31, 2015, identified in the Nationwide Readmissions Database were categorized into those with and without a secondary diagnosis of amyloidosis. HF hospitalizations with amyloidosis were then matched in a 3:1 fashion to HF hospitalizations without amyloidosis using the year of admission, discharge quarter, age, sex, and Charlson comorbidity index. Primary outcomes were inpatient mortality and 30-day readmission. Multivariable logistic regression was used to estimate the association between HF with amyloidosis and clinical outcomes.

Results

Of 1,593,360 HF hospitalizations that met inclusion criteria, 2,846 (0.18%) had HF with a secondary diagnosis of amyloidosis and were matched to 8,515 hospitalizations for HF without amyloidosis. Hospitalizations for HF with amyloidosis were associated with higher prevalence of kidney disease (56% vs. 45%), malignancy (20% vs. 4%), and higher inpatient mortality (6% vs. 3%) as compared with HF without amyloidosis. In adjusted analyses, HF with amyloidosis was associated with higher odds of in-hospital mortality (odds ratio: 1.46; 95% confidence interval [CI]: 1.17 to 1.82), 30-day readmission (odds ratio: 1.17; 95% CI: 1.05 to 1.31), and longer mean length of stay (least-squares mean difference: 1.46; 95% CI: 1.12 to 1.80).

Conclusions

In patients hospitalized with decompensated HF, presence of amyloidosis was associated with higher risk of inpatient mortality and 30-day readmission.

Objective

To develop and validate a novel, machine learning-derived model to predict the risk of heart failure (HF) among patients with type 2 diabetes mellitus (T2DM).

Research design and methods

Using data from 8,756 patients free at baseline of HF, with <10% missing data, and enrolled in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial, we used random survival forest (RSF) methods, a nonparametric decision tree machine learning approach, to identify predictors of incident HF. The RSF model was externally validated in a cohort of individuals with T2DM using the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT).

Results

Over a median follow-up of 4.9 years, 319 patients (3.6%) developed incident HF. The RSF models demonstrated better discrimination than the best performing Cox-based method (C-index 0.77 [95% CI 0.75-0.80] vs. 0.73 [0.70-0.76] respectively) and had acceptable calibration (Hosmer-Lemeshow statistic χ² = 9.63, P = 0.29) in the internal validation data set. From the identified predictors, an integer-based risk score for 5-year HF incidence was created: the WATCH-DM (Weight [BMI], Age, hyperTension, Creatinine, HDL-C, Diabetes control [fasting plasma glucose], QRS Duration, MI, and CABG) risk score. Each 1-unit increment in the risk score was associated with a 24% higher relative risk of HF within 5 years. The cumulative 5-year incidence of HF increased in a graded fashion from 1.1% in quintile 1 (WATCH-DM score ≤7) to 17.4% in quintile 5 (WATCH-DM score ≥14). In the external validation cohort, the RSF-based risk prediction model and the WATCH-DM risk score performed well with good discrimination (C-index = 0.74 and 0.70, respectively), acceptable calibration (P ≥0.20 for both), and broad risk stratification (5-year HF risk range from 2.5 to 18.7% across quintiles 1-5).

Conclusions

We developed and validated a novel, machine learning-derived risk score that integrates readily available clinical, laboratory, and electrocardiographic variables to predict the risk of HF among outpatients with T2DM.