Manemann, Sheila M.

Background: The communities in which people live affect their health. We aimed to determine whether neighborhood characteristics are associated with outcomes in patients with atrial fibrillation (AF) in a Midwest community. Methods: Patients with incident AF from 2013-2017 from a 27-county region who survived >90 days after their diagnosis were identified (N=14,725). Patient addresses were linked to publicly-available data on population density (rural-urban commuting area; RUCA) and neighborhood socioeconomic status (area deprivation index; ADI). Patients were followed for ischemic stroke or transient ischemic attack (TIA), heart failure (HF), dementia/delirium, and all-cause death through 2021. Cox regression determined risk of outcomes by RUCA category (urban, micropolitan, small rural, isolated) and ADI quartile after adjustment for age, sex, race/ethnicity, and CHA2DS2-VASc score. Results: After excluding persons missing race/ethnicity (n=46), RUCA (n=1,465), and ADI (n=1,640), 13,233 patients with AF (mean age 73.8 years, 57% men) remained. After a median follow-up of 4.7 years, 5,989 patients died; 582 experienced a stroke/TIA, 4,122 (of 9,156 patients at risk) developed HF, and 1,758 (of 12,154 patients at risk) developed dementia/delirium. Persons living in isolated areas (HR 1.14, 95% CI 1.05-1.24) and the highest ADI quartile (HR 1.59, 95% CI 1.37-1.85) experienced the greatest risk of death (Figure). A trend toward an increased risk of HF was observed for the 2 highest ADI quartiles (p-trend <0.01), although the estimates did not reach statistical significance. The risk of other outcomes did not differ by RUCA or ADI category. Conclusion: Patients with AF residing in isolated areas and neighborhoods with greater area deprivation experience increased risks of death. Additional research is needed to better understand how neighborhood characteristics influence treatment, co-morbid conditions, and other outcomes in patients with AF.

Introduction: Heart failure (HF) is a complex syndrome with high mortality often associated with impaired kidney function. Estimated glomerular filtration rate (eGFR) is central to kidney function assessment in HF. However, novel proteins related to kidney function (lipocalin 2 [LCN2], macrophage inhibitory cytokine 1 [MIC1], T cell immunoglobulin and mucin domain 1 [TIM1]) may improve risk stratification of HF patients, beyond eGFR. Their prognostic contribution remain to be fully explored. Hypothesis: Proteomic assessment of kidney function biomarkers improves risk stratification in HF. Methods: Clinical data and biospecimens were collected from a HF community cohort in Southeastern Minnesota from 2003-2012. Serum creatinine was measured in 1,388 patients and eGFR was calculated with the 2021 Chronic Kidney Disease (CKD) Epidemiology Collaboration equation. CKD was defined as an eGFR <60 mL/min/1.73m 2 . Plasma proteins were measured using the SomaLogic aptamer-based technology SomaScan®. In HF patients with and without CKD, Cox proportional hazard models were used to estimate the association between kidney function biomarkers and mortality, after adjustment for Meta-Analysis Global Group in Chronic HF (MAGGIC) risk score. Results: At HF index, patients were on average 75±13 years of age and 48% female. Patients had a median eGFR of 57 mL/min/1.73m 2 and 64% had CKD. Adjusted geometric mean levels of LCN2, MIC1, and TIM1 levels were 20-30% higher in patients with CKD. Patients with CKD were at higher risk of dying (HR 1.6, 95% CI 1.4-1.8). Patients with above median levels of LCN2, TIM1, MIC1 were at higher risk of dying, irrespective of CKD (See Figure). Conclusions: In a HF community cohort, several novel kidney function biomarkers provided further risk stratification in patients with and without CKD.

Abstract Funding Acknowledgements Type of funding sources: None. Background Falls are the leading cause of injury-related morbidity and mortality among older adults in the United States, including those with cardiovascular disease. Cardiac rehabilitation (CR) helps to optimize functional capacity, strength, and balance in patients with cardiovascular disease and, in theory, may reduce the risk of falls. However, little evidence has been published in this regard. Purpose The purpose of this study was to assess the association between CR participation and risk of falls in a community-based sample of individuals 65 years of age and older with cardiovascular disease who were eligible for CR participation. Methods Individuals from Southeastern MN and Western WI, USA, 65 years of age and older who had experienced a cardiovascular event that qualified them for CR (MI, PCI, CABG, Valve Surgery, Transplant) between January 1, 2000, and December 31, 2018, were included in this study. Participation in CR (≥1 sessions, CR-group) and subsequent falls (moderate or severe, using the Campbell classification system) were ascertained using the Rochester Epidemiology Project records-linkage system. Logistic model to create propensity weighting were used to test the association between CR participation and falls after adjustment for potentially confounding factors. Results A total of 5004 individuals met inclusion criteria for the study, with 2183 (44%) included in the CR group and 2821 (56%) in the non-CR group (CR was indicated, but the individual did not participate). Overall, 1122 patients experienced a fall within the first 5 years (16% of those in the CR-group, 23% of those in the non-CR group). After adjustment, CR participation was not associated with falls, but older individuals, females, and those with a history of falls were more likely to have had a fall, compared to young individuals, males, and those without a history of a fall. Conclusions While CR is beneficial for patients with cardiovascular disease, our study did not find evidence of an association between CR participation and fall risk in individuals 65 years of age and older who have cardiovascular disease. Additional research is needed to evaluate the role of CR components that may potentially reduce modifiable risk factors for falls and thereby reduce the risk of falls.

Background: Multimorbidity (MM) and functional limitation (FL) are associated with poor outcomes in heart failure (HF). However, the individual and combined effect of these on mental health and quality of life in patients with HF is not well understood. Methods: Patients aged ≥ 30 years with 2 or more HF diagnostic codes and 1 or more HF-related prescription drugs from four US institutions were mailed a survey to measure functional status (activities of daily living [ADLs]), quality of life and mental health (PROMIS-29 Health Profile) and social support (PROMIS Informational Support, Instrumental Support, and Social Isolation Short Forms). The sampling frame was restricted to patients with a first ever-diagnosis of HF on or after 1/1/2013 at 1 of the participating sites and on or after 1/1/2015 for the other 3 participating sites. A total of 3330 patients returned the survey (response rate 35%); among these, 3020 completed the questions of interest for this analysis and were retained. MM was defined as the presence of ≥ 2 non-cardiovascular comorbidities, and FL was defined as reporting any limitation in at least 1 of 8 ADLs. Patients were categorized into 4 groups by MM (Yes/No) and FL (Yes/No). We dichotomized the subscale scores of the PROMIS-29 at the median and calculated odd ratios for the 4 MM/FL groups. Results: Among 3020 patients with HF (45% female; mean age 73±12 years), 29% had neither MM or FL, 24% had MM only, 22% had FL only, and 25% had both. After adjustment, having both MM and FL or only FL was associated with increased odds of higher anxiety, depression, fatigue, sleep, and pain scores compared to having neither (Figure); having MM only was associated with a higher pain score. Conclusions: Patients with both FL and MM and only FL have similar odds of poor mental health and quality of life scores, underscoring the importance of the role that FL plays in outcomes in patients with HF.

Background: Most patients with heart failure (HF) have multimorbidity which may cause difficulties with self-management. Understanding the determinants of successful self-management and the resources patients draw upon to effectively manage their health is fundamental to designing new practice models to improve outcomes in HF. Methods: We developed a survey guided by the Chronic Care Model to understand the distribution of patient-centric factors, including health literacy, social support, self-management, and functional and mental status in patients with HF. The survey was administered to HF patients from 4 health care systems participating in PCORnet® (the National Patient-Centered Clinical Research Network). Patients were identified utilizing data available in the PCORnet common data model on or after 1/1/2013 at 1 health system and on or after 1/1/2015 at 3 health systems: age ≥30 years, ≥2 HF diagnostic codes, and ≥1 HF-related prescription (positive predictive value of algorithm: 81%). Results: A total of 10,662 patients with HF were identified, 9684 were mailed a survey, and 3330 completed a survey (response rate: 35%). Responders were older than non-responders (74 vs. 71 years), less racially diverse (3% vs. 12% non-White), less likely to have reduced ejection fraction (EF; 22% vs. 27% with EF <40%), and had higher prevalence of most chronic conditions. Responders reported their health was generally good or fair, they frequently had cardiovascular comorbidities, more than half had difficulty climbing stairs, and more than 10% reported difficulties with bathing, preparing meals, and using transportation (Table). Nearly 80% of patients had family or friends sit with them during a doctor visit, and most (54%) manage their health by themselves. Conclusion: More than half of patients with HF manage their health by themselves even though most bring someone along to their health care appointments. Increased understanding of self-management resources may guide the development of interventions to improve HF outcomes.

Background Larger within‐patient variability of lipid levels has been associated with increased risk of cardiovascular disease (CVD); however, measures of lipid variability require ≥3 measurements and are not currently used clinically. We investigated the feasibility of calculating lipid variability within a large electronic health record–based population cohort and assessed associations with incident CVD. Methods and Results We identified all individuals ≥40 years of age who resided in Olmsted County, MN, on January 1, 2006 (index date), without prior CVD, defined as myocardial infarction, coronary artery bypass graft surgery, percutaneous coronary intervention, or CVD death. Patients with ≥3 measurements of total cholesterol, low‐density lipoprotein cholesterol, high‐density lipoprotein cholesterol, or triglycerides during the 5 years before the index date were retained. Lipid variability was calculated using variability independent of the mean. Patients were followed through December 31, 2020 for incident CVD. We identified 19 652 individuals (mean age 61 years; 55% female), who were CVD‐free and had variability independent of the mean calculated for at least 1 lipid type. After adjustment, those with highest total cholesterol variability had a 20% increased risk of CVD (Q5 versus Q1 hazard ratio, 1.20 [95% CI, 1.06–1.37]). Results were similar for low‐density lipoprotein cholesterol and high‐density lipoprotein cholesterol. Conclusions In a large electronic health record–based population cohort, high variability in total cholesterol, high‐density lipoprotein cholesterol, and low‐density lipoprotein cholesterol was associated with an increased risk of CVD, independent of traditional risk factors, suggesting it may be a possible risk marker and target for intervention. Lipid variability can be calculated in the electronic health record environment, but more research is needed to determine its clinical utility.

Background: Among patients who experience a myocardial infarction (MI), elevated low-density lipoprotein cholesterol (LDL-C) is a major risk factor for experiencing a subsequent cardiovascular (CV) event; however, real-world evidence is lacking in regard to how changes in LDL-C levels after a MI hospitalization are related to subsequent CV events. Methods: Adults with a hospitalized MI and an available baseline LDL-C (closest LDL-C within 2 years prior to hospital discharge) between 2012 and 2018 within a 27-county region across MN and WI were included for study. For each LDL-C measurement during follow-up (starting with the first LDL-C >30 days after baseline and ≤1-year post-discharge), percent change from baseline LDL-C was calculated. Patients were followed for subsequent CV events (composite endpoint 1: MI, ischemic stroke [IS], coronary artery bypass grafting [CABG], percutaneous coronary intervention [PCI]; composite endpoint 2: MI, IS, CABG, PCI, CV death). Cox models were used to evaluate the association between time-dependent LDL-C percent change and CV events, stratified by baseline LDL-C (<70, 70-99, ≥100 mg/dL). Results: Among 3,034 MI patients identified, 740 (24%), 940 (31%) and 1354 (45%) had a baseline LDL-C <70, 70-99 and ≥100 mg/dL, respectively. After a mean follow-up of 3.4 years, 471 and 667 patients experienced the composite endpoint 1 and 2, respectively. After covariate adjustment, a trend towards a lower risk of CV events with decreasing LDL-C from baseline, compared to those with no change or an increase in LDL-C, was observed among those with a LDL-C <70 and > 100 mg/dl but not in those with a LDL-C 70-99 mg/dL (Table). Conclusions: In a population-based cohort of MI patients, there is evidence that patients with a decrease in LDL-C after MI may have a reduced risk of subsequent CV events. These results underscore the importance of lowering LDL-C after a MI and may help to identify subgroups at highest risk for additional CV events and those who may benefit from targeted LDL-C treatment recommendations.

Introduction: The Framingham Heart Study Dementia Risk Score (FDRS) was developed in a general population of elderly individuals. It is unknown how the FDRS variables predict dementia in heart failure (HF) and atrial fibrillation (AF) populations. Thus, we aimed to evaluate the predictive ability of the FDRS variables in population-based cohorts of individuals with HF and AF and to determine whether the addition of other comorbidities and risk factors improves risk prediction for dementia. Methods: Residents aged ≥50 from 7 southeast MN counties with a first diagnosis of HF (ICD-9 428/ICD-10 I50) or AF (ICD-9 427.3/ICD-10 I48 with AF on electrocardiogram/Holter monitor) between 1/1/2013 and 12/31/2017 were identified. Patients with dementia (defined by ICD codes) prior to or within 6 months after their index event and patients who died within 6 months after index were excluded. For both cohorts, models were constructed to predict dementia ≥ 6 months post index including the variables in the FDRS (age, marital status, BMI, stroke/TIA, diabetes and cancer). Additional comorbidities and risk factors were added to the models. For all models c-statistics were calculated. Results: Among 3,291 patients with HF (mean age 76 years, 53% male), 626 cases of dementia occurred and among 4,107 patients with AF (mean age 74 years, 57% male), 736 cases of dementia occurred. Among patients with HF, the FDRS variables predicted dementia with a c-statistic of 0.68 (Table). Adding additonal comorbidities and risk factors impoved the c-statistic slightly to 0.69 (p=0.01). Among patients with AF, the FDRS variables also performed well (c-stastic: 0.73; Table) and adding other comorbidities and risk factors improved the performance (c-statistic: 0.75; p<0.01). Conclusions: The variables from the FDRS predict dementia well in both HF and AF populations. The addition of comorbidities and other CVD risk factors only modestly improved prediction, thus indicating that the FDRS variables are appropriate to predict dementia in patients with HF and AF.

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.