Schulte, Philip

Introduction: Some patients with heart failure (HF) will go on to develop advanced HF, characterized by severe HF symptoms despite attempts to optimize medical therapy. Our understanding of advanced HF at a population level remains limited. The goals of this study were to determine the risk of developing advanced HF in patients with newly diagnosed HF, identify risk factors for developing advanced HF, and evaluate the impact of advanced HF on outcomes. Methods: This was a population-based, retrospective cohort study of Olmsted County, Minnesota residents with a new clinical diagnosis of HF between 2007-2017. The primary outcome was development of advanced HF, defined using the 2018 European Society of Cardiology Criteria. Risk factors for development of advanced HF from the time of incident HF diagnosis were examined using cause-specific Cox proportional hazard regression models. The associations of development of advanced HF with risks of hospitalization and mortality were examined using Andersen-Gill and Cox models, respectively, with advanced HF treated as a time dependent covariate. Results: There were 4597 residents with incident HF from 2007 to 2017. The cumulative incidence of advanced HF was 6.0%, 9.1%, and 11.5% at 2, 4, and 6 years after incident HF diagnosis. Characteristics independently associated with development of advanced HF included older age, male sex, diabetes, chronic obstructive pulmonary disease, peripheral vascular disease, lower serum sodium, worse right ventricular function, left atrial enlargement, higher estimated filling pressures, and higher right ventricular systolic pressure (p<0.05 for each). Development of advanced HF was associated with increased risks of all-cause hospitalization (adjusted HR 3.00; 95% CI 2.65-3.41, p<0.001), HF hospitalization (HR 10.24, 95% CI 8.67-12.10), all-cause mortality (HR 5.00, 95% CI 4.46-5.59, p<0.001), and cardiovascular mortality (HR 7.84, 95% CI 6.74-9.12). Conclusions: In this population-based study, clinical, laboratory and echocardiographic characteristics at the time of incident HF diagnosis were associated with future risk of advanced HF. Development of advanced HF was associated with markedly increased morbidity and mortality.

Introduction: Guideline-directed medical therapy (GDMT) dramatically improves outcomes in heart failure with reduced ejection fraction (HFrEF). Our goal was to examine GDMT use in community patients with newly diagnosed HFrEF. Methods: We performed a population-based, retrospective cohort study of all Olmsted County, Minnesota residents with newly diagnosed HFrEF (EF≤40%) 2007-2017. We excluded patients with contraindications to medication initiation (allergy, intolerance, heart rate<50 for beta blockers, SBP <80mm Hg for beta blockers, ACEi/ARB/ARNI; high creatinine (>3 mg/dL ACEi/ARB/ARNI, >2.5 men or >2.0 mg/dL women for mineralocorticoid receptor antagonists, MRAs) or hyperkalemia (potassium >5 meQ/L for ACEi/ARB/ARNI, MRA). We examined use and peak dose achieved for beta blockers, HF beta blockers (metoprolol succinate, carvedilol, bisoprolol), ACEi/ARB/ARNI, and MRA in the first year after HFrEF diagnosis. We used logistic regression to evaluate predictors of GDMT use. Results: From 2007-2017, 1160 patients were diagnosed with HFrEF (mean age 69.7 years, 65.6% men). Most eligible patients received beta blockers (92.1%) and ACEi/ARB/ARNI (86.5%) in the first year after HFrEF. However, only 63.6% of patients were treated with a HF beta blocker, and most did not receive MRAs (82.6%). The percentage of treated patients reaching medication target doses was 20.5% for HF beta blockers, 25.3% for ACEi/ARB/ARNI, and 11.2% for MRA. Compared to patients not seen in an HF clinic, patients seen in an HF clinic (n=380, 32.8%) were at greater odds of receiving beta blockers (OR 3.85, 95% CI 1.79-8.33); HF beta blockers (OR 3.85, 95% CI 2.63-5.26); ACEi/ARB/ARNIs (OR 3.85, 95% CI 2.17-6.67); and MRAs (OR 3.03, 95% CI 2.08-4.35). Other independent predictors of GDMT use included younger age (beta blockers, ACEi/ARB/ARNI), male gender (MRAs), higher SBP (beta blockers, ACEi/ARB/ARNI), lower EF (HF beta blockers), higher BMI (MRAs), and diabetes (ACEi/ARB/ARNI, p<0.05 for each). Conclusions: In this population-based study, most patients with newly diagnosed HFrEF received beta blockers and ACEi/ARB/ARNIs, but goal doses were usually not achieved. GDMT use was much higher in patients referred to an HF clinic.

Introduction: Advanced or “Stage D” heart failure (Adv HF) patients are at high risk of poor outcomes. However, recognition of Adv HF is limited as diagnostic criteria are complex. Administrative algorithms to identify Adv HF could enable better understanding of who has advanced HF and lead to enhanced therapeutics development, dissemination, and outcomes. Methods: In a population-based cohort of all Olmsted County, Minnesota residents with ≥1 HF billing code 2007-2017 (n=9881), we identified all patients with Adv HF (n=753) by applying the gold standard European Society of Cardiology Adv HF criteria via manual medical review by a HF cardiologist. The Adv HF index date was the date the patient first met all ESC criteria. We then developed candidate algorithms to identify Adv HF using only administrative data (billing codes and HF prescriptions or comorbidities known to affect HF outcomes), applied them to the entire HF cohort and assessed their ability to identify patients with Adv HF on or after their Adv HF index date. Results: A single hospitalization (Hsp) for HF or ventricular arrhythmias (VA, 2007-2017) identified all patients with Adv HF (sensitivity 100%), but the PPV was low (36.4%). Requiring more Hsp, other signs of Adv HF such as hyponatremia, acute kidney injury, hypotension or high-dose diuretic use decreased the sensitivity but improved specificity and PPV. As Adv HF was relatively uncommon, negative predictive values (NPV) were high regardless of algorithm characteristics. Conclusions: Algorithms using administrative data can identify patients with Adv HF with clinically useful accuracy.

Abstract Introduction Patients discharged after acute decompensated heart failure (ADHF) have elevated risk for readmission due to multiple factors including suboptimal behavioral and social support. Telemonitoring interventions have shown inconsistent effectiveness in reducing HF readmissions. Patient-centered health coaching, when combined with telemonitoring, may be a viable model to engage patients in self-care behaviors and enhance patient experiences following acute hospitalization. Purpose This multicenter randomized trial evaluates whether remote telemonitoring combined with health coaching decreases 60 day readmission rates for patients with ADHF when compared to standard of care. Methods Patients with primary or secondary diagnosis of ADHF were consented and randomized prior to hospital discharge to either standard care or intervention of remote telemonitoring and health coaching. Within 2 days of hospital dismissal, intervention patients were onboarded to the remote monitoring platform, which links personal health sensors which collect on-body physiologic measures (ECG, heart rate, respiration rate, and activity via 3-axis accelerometer) with providers through secure mobile communication. A registered nurse was designated as the primary health coach focusing on disease management - including symptom recognition, adherence to treatment strategies, care coordination, medication matters, and problem solving. A social worker and nutritionist were also assigned. The primary outcome was all-cause mortality or readmission within 60 days of hospital dismissal. Statistical analysis included stratified log-rank tests and stratified Cochran-Mantel-Haenszel Chi-square test to account for site-stratified randomization. Results The study was halted due to low rate of subject accrual. Of planned 304 subjects, 143 were randomized between 2015 and 2019 at 6 sites in the United States. Dropout and withdrawal after randomization of 32 subjects (22%) left 112 analyzable for the primary endpoint. Many subject withdrawals after unblinded disclosure of arm allocation were related to treatment assignment. Immediate withdrawal without follow up in these subjects precluded an intention-to-treat analysis. Mean age was 69 years and subjects were more often male (56%) and non-Hispanic white (70%). In per-protocol analysis, using subjects adherent to protocol specified visits (n=112), we observe no difference in the primary outcome (26% among intervention vs 28% among standard care, Figure, p=0.77). There were also no differences among secondary outcomes of overall mortality (2% vs 7%, p=0.20) or composite emergency department visit, hospital admission, or death (35% vs 34%, p=0.85). Conclusions Among patients with heart failure, an intervention of remote telemonitoring and health coaching did not reduce all-cause readmission or mortality. Significant withdrawal rates suggest future studies may need to improve screening and study retention. Figure 1 Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): National Institutes of Health, National Institute on Aging