Citations

Introduction: Heart failure with preserved ejection fraction (HFpEF) may have different characteristics by age groups. Aim Compare clinical and echocardiographic characteristics in patients with HFpEF enrolled in the PARAGON-HF echocardiography substudy by age groups. Methods: Echocardiography was performed in 1097 patients in PARAGON-HF (Prospective Comparison of ARNI with ARB Global Outcomes in HF with Preserved Ejection Fraction), where patients with left ventricular ejection fraction (LVEF) >=45% were enrolled. Clinical and echocardiographic characteristics were compared in patients who were <65, 65-74, and >75 years of age. Results: Younger patients were more likely to be male, obese, have prior myocardial infarction (MI) and diabetes, but less likely to have atrial fibrillation (AF, Table 1). Older patients had smaller left ventricular (LV) dimension indices, more likely to have concentric hypertrophy or remodeling, higher LVEF and better global longitudinal strain, and more likely to have left atrial enlargement and echocardiographic evidence of higher LV filling pressure (Table 1). Echocardiographic measures of pulmonary pressure and right ventricular function were similar across age groups. After adjusting for sex, race, region, history of MI, diabetes, AF, and body mass index, every 10 years of increase in age was associated with 5.1 mL/m 2 decrease in LV end-diastolic volume index (95% confidence interval [CI] 3.9 - 6.5, p < 0.001), 0.4% increase in absolute global longitudinal strain (95% CI 0.1% - 0.7%, p = 0.017), and 1.07 increase in E/e’ septal ratio (95% CI 0.42 - 1.73, p = 0.001). Conclusions: Compared with younger patients, older patients with HFpEF were more likely to be female with AF, have better systolic function and higher LV filling pressure. These findings demonstrated the heterogeneity in cardiac structure and function in HFpEF by age and may have implications for treatment and future clinical trial recruitment.

Introduction: Cardiac sarcoidosis (CS) results in HFrEF, but its natural history and response to GDMT remains uncertain. Methods: We queried a registry of 631 CS patients evaluated at our institution to identify those with LVEF < 35% on TTE within 30 d of diagnosis and a follow-up TTE 12-18 months later (external CS diagnoses were excluded). Demographics, LVEF, NT-proBNP, GDMT, immunosuppression, CIED, and outcomes (hyperkalemia, LVAD/transplant, CV hospitalization, death) were recorded. Results: Overall, 25/631 (4%) of CS patients had an LVEF <35% at initial presentation (36% female, 84% white; median age of 58 y; probable/not biopsy-proven CS in 52%). Mean baseline LVEF was 27% (σ=7%), NT-proBNP 1736 pg/mL (n=11), potassium 4.3 mEq/L (n=18), and median NYHA Class II (54%). Patients were on a mean 1.7 GDMT agents at CS diagnosis (Figure). Most were treated with immunosuppression (88%) for mean 0.75 y, usually prednisone (88%, mean 16 mg daily) and fewer with mycophenolate (32%, mean 1875 mg daily), methotrexate (28%, mean 18mg weekly), or TNF-α inhibitor (4%). 64% were on trimethoprim-sulfamethoxazole prophylaxis. At follow-up (median 317 d), 3 patients (12%) required LVAD and 1 (4%) required heart transplant. Mean LVEF improved to 37% (excluding LVAD/transplant, σ=11%, p=0.001). CIED prevalence increased from 48 to 81% (p=0.03). Overall GDMT did not change (mean 2.0 agents, p=0.2) and only beta-blocker dose changed (64 to 138 mg mean daily metoprolol equivalents). Potassium did not change (p=0.4) and hyperkalemic events were infrequent (16%). No patients died, but unplanned hospitalization occurred in 36% of patients. Conclusions: CS patients with HFrEF are usually on partial GDMT at diagnosis, presumably due to earlier recognition of HFrEF, but intensification of GDMT may be limited. Despite improved LVEF which may be due to GDMT, immunosuppression, and/or CIED, over 1/3 patients were hospitalized within 18 mo, highlighting the high-risk profile of HFrEF in CS.

Introduction: Poor sleep contributes to outcomes in heart failure (HF). Phenotypes reflecting multi-dimensional sleep health may better predict outcomes and support more precise interventions than single measures, but few studies have used this approach. Aims: 1) Describe phenotypes of sleep health and 2) Evaluate whether sleep health phenotypes predict symptoms, cognitive function, and time to first hospitalization and emergency department (ED) visits among HF patients. Methods: We performed secondary analysis of a randomized controlled trial of cognitive behavioral therapy for insomnia compared with HF self-management education with HF patients. We measured sleep [rest-activity rhythms, sleep duration, and efficiency (2-week wrist actigraphy/follow-ups); insomnia; CPAP use; and daytime sleepiness (Epworth Scale)]; perceived stress; fatigue; anxiety; depression; pain; and cognitive ability (Patient-Reported Outcomes Measurement System: PROMIS) at baseline and 3, 6, and 12 months post-intervention and tracked hospitalizations and ED visits. We used K-means clustering to identify sleep phenotypes and generalized linear mixed models and Cox models to predict outcomes, adjusted for demographic factors. Results: Among 166 participants (M age = 63.2 (SD = 12.8) years; 57% female; 23% NYHA Class 3/4 HF; 35% EF less than 45%), there were 4 phenotypes [poor sleep (14%); short sleep/high sleep efficiency (39%); long sleep/low sleep efficiency (25%); and healthy sleep (21%)] at baseline. The healthy sleep group was older and had lower BMIs and less comorbidity and had better outcomes compared to all other phenotypes. In particular, this group had better fatigue (-2.43±0.87), pain (-1.86±1.05), anxiety (-1.50±0.89), stress (-1.46±0.69), and cognitive ability (1.76±0.80) than the poor sleep group. The poor sleep group had shorter time to adverse events compared to the other phenotypes (Hazard ratios of 0.41 to 0.56 for hospitalizations and 0.35 to 0.60 for ED visits). Conclusions: Multidimensional aspects of sleep predict HF outcomes and may improve on prediction with single measures. Future tailored sleep interventions focused on phenotypes may more precisely improve outcomes among HF patients than traditional approaches.

Introduction: Incident cardiac arrhythmias are common in hospitalized COVID-19 patients, but the impact of a second incident cardiac arrhythmia during hospitalization on clinical outcomes remain unclear. Hypothesis: The aim of this study was to assess the frequency and prognostic implications of a second incident arrhythmia in hospitalized COVID-19 patients over the pandemic course till vaccine availability. Methods: The multicenter COVID-RHYTHM Registry enrolled hospitalized COVID-19 patients from 3/8/2020 to 1/24/2021. Cardiac arrhythmia included documented atrial fibrillation (AF), atrial flutter (AFL), ventricular tachycardia (VT), severe sinus bradycardia (<40 bpm) or AV block. Outcomes were compared between patients with single vs. more than one incident arrhythmias adjusting for baseline variables. Results: Of 6299 hospitalized COVID-19 patients, 4398 had an had an incident cardiac arrhythmia during hospitalization, of which 822 (19%) had a second incident arrhythmia. Those with >1 incident arrhythmia had a higher baseline prevalence of heart failure, chronic kidney disease, coronary artery disease and sleep apnea (Table). Those with >1 incident arrhythmia had higher in-hospital mortality, need for ICU admission and need for rehab upon discharge. Length of stay was similar between the groups. Conclusions: In hospitalized COVID-19 patients with incident cardiac arrhythmias, a second incident arrhythmia during hospitalization was seen in 19%. Having >1 incident arrhythmia was associated with increased need for ICU admission and reduced survival.

Purpose: To compare left-ventricular and aortic pressures obtained using fluid-filled and high-fidelity solid-state pressure catheters in subjects undergoing left heart catheterization. Methods: Twenty subjects scheduled for a left heart catheterization were enrolled and 18 subjects completed this IRB-approved study. During catheterization, left-ventricular (LV) and aortic (AO) pressures were obtained using a fluid-filled pressure catheter (standard-of-care) and a high-fidelity solid-state pressure catheter synchronously. The pressure tracings were analyzed by two independent readers to measure LV systolic (LVSP), LV minimum diastolic (LVMDP), LV end diastolic (LVEDP), AO systolic (AOSP), and AO diastolic (AODP) pressures. Derivatives of the pressure waveforms were computed to estimate the isovolumic contraction and relaxation rates (peak ± dp/dt). The measurements were made over multiple cardiac cycles and averaged results were used for comparisons using repeated measures analysis of variance, post-hoc tests with Bonferroni corrections and Bland-Altman plots. Results: A significant main effect of the pressure catheter was noted for LVSP, LVMDP and AOSP (p < 0.025). The LVSP and AOSP measured with fluid-filled pressure catheters were higher by 6.6 ± 6.9 mmHg and 4.6 ± 5.2 mmHg in comparison to the high-fidelity solid-state pressure catheter assessments. In contrast, the LVMDP measurements were 3.5 ± 5.7 mmHg lower than the solid-state pressure catheter measurements. When evaluating the differences between readers, the maximum difference was noted for LVEDP measurements (3.1 ± 2.4 mmHg; p ≤ 0.001). The differences in isovolumic contraction rate (66.4 ± 116.0 mmHg/s) and relaxation rate (60.5 ± 113.5 mmHg/s) were not significantly different between the two catheter systems after Bonferroni corrections for multiple comparisons (p > 0.06). Bland-Altman analysis revealed a bias ranging from 0.8 to 6.6 mmHg for all the pressure measurements. Conclusions: There are differences in LVSP, LVMDP, and AOSP measured using fluid-filled and high-fidelity solid-state pressure catheters. Clinicians should be aware that the fluid-filled catheters commonly overestimate the true systolic pressures in the left ventricle and aorta.

Introduction: Doppler mean gradient (MG) can underestimate the severity of aortic stenosis (AS) in patients with atrial fibrillation (AF) compared to patients in sinus rhythm (SR). Patients with AF and severe AS may experience delayed diagnosis and more advanced disease at time of referral for transcatheter aortic valve replacement (TAVR). Methods: Patients who underwent TAVR from 2013 to 2017 for native valve severe AS were identified from an institutional database. MG and aortic valve calcium score (AVCS) at time of referral were compared in patients in AF and SR. Retrospective theoretical AVCS in AF patients were derived assuming an average progression of 152 AU/year (published data). Results: There were 820 patients included; mean age was 81.3+/- 8.2 years and 41.6% were female. AF was present in 356 patients at time of referral. Patients in AF were older (82.2 vs. 80.5, p = 0.003), had lower MG compared to SR patients (42.0 vs. 44.9, p = 0.002) and a lower peak velocity (4.1 vs. 4.2, p = 0.010) with a similar indexed aortic valve area (0.4 vs. 0.4, p = 0.17). Stratified by sex, the median calcium score for AF patients was higher (by around 300 AU) among both males (AF 2850.0 vs. SR 2561.0, p = 0.044) and females (AF 1942.0 vs. 1610.5, p = 0.025) ( figure 1 ). A density plot of retrospective theoretical AVCS in AF patients (projected assuming referral to TAVR approximately two years prior) resulted in nearly identical median AVCS between the two cohorts ( figure 2 ). Conclusions: Older age and higher AVCS in patients with AF compared to SR suggests that AS was both underestimated and more advanced at time of referral to TAVR. Patients in AF may have severe AS as early as 2 years prior to TAVR referral.

Background: Clinically significant tricuspid regurgitation (TR) is an important contributor to morbidity and mortality. The Tricuspid Regurgitation Impact on Outcomes (TRIO) score based on 8 parameters (age, gender, chronic heart failure, chronic lung disease, heart rate, creatinine, aspartate transaminase level, and TR severity) is used for risk stratification. The performance of the TRIO score in patients with TR secondary to severe pulmonary hypertension (PH) is unknown. Research Questions: To evaluate whether the TRIO score can be applied in patients with TR and pulmonary hypertension (PH), and to identify other predictors of mortality. Methods: Retrospective study including patients with TR ≥ moderate. Severe PH was defined as right ventricular systolic pressure (RVSP) ≥50 mmHg. The impact of clinical, echocardiographic and laboratory variables on mortality was evaluated with the Cox proportional hazard method. Results: A total of 13,482 patients from the TRIO population were included; severe PH was present in 6,530 (48.4%); mean age 73.4 + 13.3, 55.7% females. TRIO score performance was modest (AUC 0.62) in risk stratification (Figure A). PH patients had significantly higher mortality than those without PH at all levels of TRIO score (Figure B); differences were progressively lower with increasing TRIO scores. At multivariate analysis, only TRIO score [HR = 1.54 (1.35-1.77) per unit change], presence of an elevated NT-proBNP [HR = 1.02 (1.02-1.03) per 1000 pg/ml change], RVSP (HR = 1.08 (1.02-1.16) per 10 mmHg change], presence of RV dysfunction [HR = 1.15, (1.05-1.26)] and pulmonary artery pulsatility index (PAPi) ≤ 4 [HR = 1.34 (1.07-1.67)] were significant predictors of mortality (model AUC 0.65). Conclusions: TRIO score allows risk stratification in PH patients, but performance is modest; incorporating additional factors marginally increased model performance. PH patients have higher mortality at all levels of TRIO score, more so at low scores.

Background: Up to 70% of patients with cardiogenic shock (CS) will survive hospitalization, yet studies on the outcomes of CS survivors are scarce. Hypothesis: We hypothesized that CS survivors would be at high risk of rehospitalization, particularly CS without acute myocardial infarction (AMI) who may have chronic heart failure (HF). Methods: We used the Nationwide Readmissions Database (NRD) to examine adult patients who were discharged alive after an index CS admission from 2016-2019. We compared demographic and clinical features during the index admission and the readmission mortality rate between the AMI-CS versus non-AMI-CS groups. We analyzed the cumulative hazard for all-cause readmission within 90 days after index hospitalization using Kaplan-Meier curves. Results: We included 134,793 survivors of CS hospitalization from 2016 to 2019 who were discharged to home or a long-term facility, including 43.9% with AMI-CS and 56.1% with non-AMI-CS. The demographics and clinical features differed between the AMI-CS and non-AMI-CS groups, with more HF and non-cardiac comorbidities in the non-AMI-CS group. The non-AMI-CS patients were more likely to be readmitted after discharge, with a higher 90-day readmission rate compared to the AMI-CS group (31.6% vs 25.9%, p <0.001; Figure). During the readmission, the non-AMI-CS group had a higher rate of repeat CS presentation compared to the AMI-CS group (8.9% vs.. 6.1%, p < 0.001). In-hospital mortality during readmission was higher in the non-AMI-CS group (8.6% vs.7.2%, p < 0.001). Conclusions: Nearly one-third of CS survivors require readmission within 90 days after their index hospitalization. Non-AMI-CS patients have a higher re-admission rate after their index hospitalization and worse outcomes during readmission. This analysis identifies an unmet need and an important opportunity to improve long-term outcomes for CS survivors.

Introduction: US rural areas have a disproportionate burden of CVD and hypertension. Physical inactivity is a major risk factor, with 25% prevalence in US adults. However, rural disparities in physical inactivity are incompletely described. The aim was to evaluate differences in prevalence of physical inactivity based on county-level rurality. Methods: We used CDC county-level data for adults aged ≥20 years, 2004–2019, available for 97.6% of US counties (n=3148/3226). CDC defined physical inactivity as self-reported lack of leisure time physical activity in the month preceding the survey. Rurality was based on six categories ordered from least rural (large central metro) to most rural (noncore). We used weighted least squares regression to compare prevalence by rurality level, adjusting for 13 county-level sociodemographic factors; results reported as prevalence ratios (95% CI) with partial F -tests and P -values. Results: In age and sex ratio adjusted analysis (model 1), we observed a gradient in the prevalence of physical inactivity across levels of rurality, with significantly higher rates in small metro (1.03 [1.00–1.06]), micropolitan (1.05 [1.02–1.07]), and noncore (1.05 [1.02–1.08]) compared with large central metro counties ( F -statistic=7.5; P <.001). With further adjustment for other sociodemographic factors (model 2), the relative differences were negated in noncore counties (0.99 [0.97-1.02]), attenuated in intermediate counties, and accentuated in large fringe metro counties (1.06 [1.04-1.08]) ( F -statistic=14.3; P <.001). Further adjustment for obesity (model 3) largely attenuated the association of rurality with physical inactivity, resulting in no significant differences relative to large central metro counties ( F -statistic=4.8; P <.001). Conclusions: County-level rurality was associated with prevalence of physical inactivity. These disparities were largely accounted for by obesity, which may be targeted for intervention.

Background: While spontaneous coronary artery dissection (SCAD) patients can present with ventricular arrhythmias (VAs), the in-hospital outcomes among these patients are not well-established. Objective: This study aimed to analyze the in-hospital outcomes of patients with SCAD and concurrent VAs, and to determine the predictors of VAs occurrence in this population. Methods: Using the Nationwide Readmissions Database, our study included patients aged 18 years or older who had SCAD between 2017 and 2020. We categorized the cohort into two groups depending on the presence of VAs during hospitalization. The in-hospital outcomes were assessed between two groups. Through multivariable analysis, we further analyzed the predictors of VAs occurrence among SCAD patients. Results: Our study included 160 (13.45%) SCAD patients with concurrent VAs and 1,030 (86.55%) SCAD patients without concurrent VAs between 2017 and 2020. SCAD patients with concurrent VA were associated with higher rates of early mortality (11.88% vs. 2.04%, p<0.01), prolonged index hospital stay (≥7 days) (31.25% vs. 12.82%, p<0.01), and non-home discharge (21.88% vs. 6.50%, p<0.01). The length of hospital stay was longer in the SCAD with concurrent VAs group (6.93 days vs. 3.73 days, p<0.01), and the median cumulative cost of hospitalization was also higher in this group ($31,450 vs. $14,664, p<0.01). SCAD with concurrent VAs patients also had poorer in-hospital adverse events. In multivariable analysis, the independent predictors for VAs occurrence among SCAD patients were chronic liver disease (aOR: 2.18; 95% CI: 1.15- 4.12, p=0.02) and heart failure (aOR: 3.40; 95%CI: 2.30- 5.02, p<0.01). Conclusion: Ventricular arrhythmias among SCAD patients were associated with poorer in-hospital outcomes. Heart failure and chronic liver disease were the predictors of VAs in SCAD patients. This highlights the importance of closely monitor and aggressive VAs management among these high-risk patients.