Roger, Véronique L.

Background: Recent studies have suggested that patient activation, or having the knowledge, skills and confidence to manage one’s own health, has a substantial impact on patient behaviors and health outcomes. However, the role of activation in patients with heart failure is unknown. Methods: We prospectively recruited Southeastern Minnesota residents hospitalized with acute decompensated heart failure (ADHF) from January 2014 through July 2015. Activation was measured by face-to-face questionnaire using the Patient Activation Measure (PAM) and patients were categorized into four activation levels (lowest to highest activation) per PAM guidelines. Logistic regression was used to examine the association between activation level and discharge location and early post-discharge outcomes. Results: Among the 302 patients enrolled, the mean age was 77.3 years, 57.3% were men, and 46.8% had preserved ejection fraction. Median (25th-75th percentile) length of stay was 4 (3-7) days and 7 (2.3%) patients died prior to discharge. Among survivors, 73.3% discharged to home, while the remainder went to skilled nursing facilities (23.0%), assisted living facilities (2.0%) or other arrangements (1.4%). The 30-day mortality rate was 7.0% and the 30-day readmission rate was 21.2%. The number (%) of patients in each activation category from highest to lowest was 9 (3.0%), 121 (40.1%), 122 (40.4%), and 50 (16.6%). Patients with lower activation were older, less educated, had worse overall and financial satisfaction, and worse health literacy. They more often discharged to skilled nursing facilities and had higher 30-day mortality, though 30-day readmission did not differ by activation (Figure). Adjusting for age, patients in the lowest activation level were at 3.1-fold increased odds of discharging to a skilled nursing facility and 6.0-fold increased odds of dying within 30 days of hospital discharge compared with patients in the highest two activation levels. Conclusions: Patients hospitalized with ADHF with lower activation are less satisfied, have worse health literacy, more often require skilled care and are at increased risk for early post-discharge mortality. Activation is easy to measure, identifies a high-risk subgroup, and is potentially modifiable.

Troponin is the biomarker of choice for the diagnosis of myocardial infarction (MI) in clinical practice. Data on its value to predict death, while documented in clinical trials, are lacking from community studies. There is little information from population studies of MI on short and long term predictive value of Troponin on death and nonfatal endpoints such as recurrent ischemic events and heart failure. Methods: We evaluated the prognostic value of Troponin T (TnT) among consecutive persons with incident MI in Olmsted County, MN. Results: 1220 persons (mean age 69±15 years, 45% women) experienced an incident MI (20% ST elevation MI) in Olmsted County, MN between 2002 and 2008. Peak TnT value measured at a median of 1 day post-MI was 1.88±4.19 ng/mL (median 0.52 (25%: 0.16, 75%: 1.75)). After 1.27 years of follow-up, 294 deaths occurred, 360 recurrent ischemic events (recurrent MI or hospitalized unstable angina) and 345 episodes of heart failure. Associations between clinical events and TnT tertiles are shown in the table . Conclusions: In the community, the magnitude of TnT elevation is predictive of recurrent ischemic events, heart failure and early death. It is not associated with long-term mortality. Hazard ratio compared to lowest TnT tertile, 95%CI and p value Adjusted for age, sex and comorbidity Outcome All events Events within 30 days Events among 30 day survivors Death TnT tertile 2 TnT tertile 3 p-value 1.02 (0.78-1.33) 1.15 (0.85-1.57) 0.64 0.64 (0.38-1.06) 1.59 (0.98-2.57) 0.004 1.24 (0.90-1.69) 0.93 (0.62-1.38) 0.26 Recurrent ischemic events TnT tertile 2 TnT tertile 3 p-value 1.45 (1.12-1.88) 1.70 (1.29-2.24) <0.001 1.45 (0.96-2.19) 2.07 (1.37-3.12) 0.002 1.46 (1.05-2.04) 1.45 (1.00-2.10) 0.049 Heart failure TnT tertile 2 TnT tertile 3 p-value 1.03 (0.78-1.36) 2.10 (1.58-2.78) <0.001 0.94 (0.68-1.29) 2.10 1.54-2.87) <0.001 1.38 (0.79-2.41) 2.19 (1.14-4.21) 0.067

Background: Recent findings suggest a role for heart failure in the etiology of osteoporotic fractures, but the nature of the relationship is unclear and community data are lacking. Methods and Results: We conducted a community-based case-control study comparing prior osteoporotic fractures among Olmsted County, Minnesota, residents newly diagnosed with heart failure in 1979-2002 (cases) to age- and sex-matched community controls without heart failure (961 pairs; mean age: 76 years; 54% women). Subjects were then followed forward in time to July 2009 to evaluate the risk of fracture subsequent to the heart failure diagnosis. Prior fractures were more frequent in heart failure cases than controls: 23% versus 19%, P =0.02; adjusted odds ratio (OR) for any osteoporotic fracture 1.39 (95% CI: 1.07-1.81). This was related to hip fractures (OR: 1.82; 95% CI: 1.25-2.66) with little or no association with other fractures. Over a mean follow-up of 7.5 years, 536 new osteoporotic fractures occurred in 444 individuals. The adjusted fracture risk was higher among heart failure patients compared to controls (hazard ratio [HR]: 1.32; 95% CI: 0.98-1.79), again largely attributable to hip fractures (HR: 1.58; 95% CI: 1.03-2.41). Conclusions: Patients diagnosed with heart failure experience an excess risk of fractures both before and after the diagnosis of heart failure, which is related to hip fractures. These findings suggest common underlying and co-occurring mechanisms for heart failure and hip fracture and underscore the crucial importance of preventing falls in elderly heart failure patients.

Background: Echocardiography is an important component of the assessment of patients with heart failure (HF). However, its longitudinal use after initial HF diagnosis has not been evaluated. Methods: We studied patterns of echocardiogram use after HF diagnosis among a random sample of validated incident HF cases in Olmsted County, Minnesota, from 1987 to 2006. Results: Among 1472 HF patients (mean age 76.0 years, 45% male), 4817 echocardiograms were obtained over a mean follow-up of 5.1 years. The majority were transthoracic (n=3952, 82%), with fewer transesophageal (n=604, 13%) and stress (n=250, 5%) studies obtained. Most patients died during follow-up (n=1126, 76%), and thus had their entire lifetime after HF diagnosis captured. The number of echocardiograms per person ranged from 0-38 (Figure) with a median (25 th , 75 th percentile) of 2 (1, 4). The mean rate of echocardiograms obtained per 10 years of follow-up was 6.4, and increased over time with mean echocardiograms per 10 years follow-up of 4.9, 5.9, 7.2, and 7.8 for persons diagnosed from 1987-1991, 1992-96, 1997-2001, and 2002-06, respectively (p for trend <0.001). Men had echocardiograms more frequently than women (7.2 vs. 5.7 per 10 person years follow-up, p<0.001). Patients who were younger at diagnosis had more frequent echocardiography with rates of 7.1, 6.8, and 5.2 per 10 years follow-up for those who were <70 years, 70-79 years, and 80 years or older at diagnosis (p for trend <0.001). Conclusions: Most patients have multiple echocardiograms performed after HF diagnosis, and echocardiography use in patients with HF has increased over time. Men and younger patients have higher rates of use after HF diagnosis.

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: Heart failure (HF) is a clinical syndrome with high mortality. Left ventricular ejection fraction (EF) determines clinical phenotyping and HF is classified most often as preserved (HFpEF) or reduced (HFrEF) EF. The importance of expanding phenotyping beyond EF is recognized. We hypothesized that machine learning applied to echocardiography will provide information important for deep phenotyping. Methods: We collected clinical and echo data in a HF community cohort between 2013 and 2017. Unsupervised clustering was applied to 23 echo variables using partitioning around medoids (PAM), with Gower dissimilarity used to address the mixed variable types. Proportional hazards regression examined the association between cluster assignment and death. Results: We identified 4978 persons with HF [mean age 73.9 years (SD 13.9), 47% women] with echocardiography within 6 months of diagnosis. We identified 2 primary clusters using echo variables, as depicted using t-Distributed Stochastic Neighbor Embedding (t-SNE) applied to the dissimilarity matrix (Figure). Diastolic function variables exhibited the largest differences by clusters as shown by absolute standardized differences (Table). After 3.2 + 1.9 years of follow up, 2001 deaths occurred. Assignment to cluster 2 was associated with a near 20% excess risk of death independent of age, sex, comorbidity, and EF [HR 1.17 (1.07, 1.28) p<0.001]. Conclusions: Unsupervised clustering applied to echocardiography in a large community HF cohort identified 2 distinct clusters, which defined HF subgroups with different characteristics and were independently associated with risk of death.

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.

Background: The 2013 ACC/AHA Cholesterol Guidelines departed from prior ATPIII guidelines, recommending statin treatment based on patients’ predicted 10-year ASCVD risk and de-emphasizing LDL-C targets. However, the degree to which these new guidelines have been adopted by clinicians in community practice is unknown. Methods: We surveyed 522 clinicians in 133 cardiology, primary care, and endocrinology practices participating in the Patient and Provider Assessment of Lipid Management (PALM) Registry. Clinicians were asked about their likelihood of prescribing statins in 4 hypothetical patient scenarios. Results: Among responders, 79% were physicians (60% cardiologists, 38% primary care or general practice, 2% endocrinology), 18% were advanced practice providers, and 3% trainees. When queried, 73% of providers reported primarily using the 2013 ACC/AHA Guidelines to guide lipid management. However, only half reported “often” or “always” calculating 10-year ASCVD risk to aid lipid management decisions for primary prevention. When presented with a high 10-year ASCVD risk patient (>15%) with a high LDL-C (150 mg/dL), 92% of providers recommended statins. Similarly, 83% of providers would use statins in a diabetic patient with a LDL-C of 140 mg/dL. Yet, only 37% reported they would prescribe a statin to an older adult (age 70) for whom guidelines recommend statin based on high 10-year ASCVD risk alone (10.1%). And up to 40% would prescribe a statin to a younger (age 40) with a low 10-year risk, but a high LDL (165 mg/dL) who would not otherwise meet criteria for statin therapy under the new guidelines. Cardiologists and non-cardiologists were similarly likely prescribe statins in most scenarios with the exception of to the younger patient with elevated LDL-C were cardiologists used statins more commonly (32% vs. 42%. OR 1.5, 95% CI 1.1-2.2, p=0.02). Conclusion: Initial survey results show that while most clinicians claim adoption of the 2013 ACC/AHA Guidelines, reported practices are not always adherent to these guidelines. Providers do not always calculate 10-year ASCVD risk to guide lipid management and many do not prescribe statins to patients eligible based on 10-year ASCVD risk.

Abstract Background Cognitive function is essential to effective self‐management of heart failure (HF). Alzheimer's disease and Alzheimer's disease‐related dementias (AD/ADRD) can coexist with HF, but its exact prevalence and impact on health care utilization and death are not well defined. Methods Residents from 7 southeast Minnesota counties with a first‐ever diagnosis code for HF between January 1, 2013 and December 31, 2018 were identified. Clinically diagnosed AD/ADRD was ascertained using the Centers for Medicare and Medicaid (CMS) Chronic Conditions Data Warehouse algorithm. Patients were followed through March 31, 2020. Cox and Andersen‐Gill models were used to examine associations between AD/ADRD (before and after HF) and death and hospitalizations, respectively. Results Among 6336 patients with HF (mean age [SD] 75 years [14], 48% female), 644 (10%) carried a diagnosis of AD/ADRD at index HF diagnosis. The 3‐year cumulative incidence of AD/ADRD after HF diagnosis was 17%. During follow‐up (mean [SD] 3.2 [1.9] years), 2618 deaths and 15,475 hospitalizations occurred. After adjustment, patients with AD/ADRD before HF had nearly a 2.7 times increased risk of death, but no increased risk of hospitalization compared to those without AD/ADRD. When AD/ADRD was diagnosed after the index HF date, patients experienced a 3.7 times increased risk of death and a 73% increased risk of hospitalization compared to those who remain free of AD/ADRD. Conclusions In a large, community cohort of patients with incident HF, the burden of AD/ADRD is quite high as more than one‐fourth of patients with HF received a diagnosis of AD/ADRD either before or after HF diagnosis. AD/ADRD markedly increases the risk of adverse outcomes in HF underscoring the need for future studies focused on holistic approaches to improve outcomes.

Cardiovascular disease remains the leading cause of death in women. Given accumulating evidence on sex- and gender-based differences in cardiovascular disease development and outcomes, the need for more effective approaches to screening for risk factors and phenotypes in women is ever urgent. Public health surveillance and health care delivery systems now continuously generate massive amounts of data that could be leveraged to enable both screening of cardiovascular risk and implementation of tailored preventive interventions across a woman’s life span. However, health care providers, clinical guidelines committees, and health policy experts are not yet sufficiently equipped to optimize the collection of data on women, use or interpret these data, or develop approaches to targeting interventions. Therefore, we provide a broad overview of the key opportunities for cardiovascular screening in women while highlighting the potential applications of artificial intelligence along with digital technologies and tools.