Roger, Véronique L.

Background: Multimorbidity is common in atrial fibrillation (AF), particularly among older patients who generally have multiple cardiovascular and non-cardiovascular chronic conditions. However, less is known about the accumulation of chronic conditions in younger patients with AF. Methods: Patients aged <65 with incident AF from 2013-2019 from a 27-county region in the Midwest (N=4907) were matched 1:1 on age (±5 years) and sex to referents from the same community. The index date for the matched pair was the date of first diagnosis of AF. Diagnosis of 20 other chronic conditions was ascertained from 3 years prior to index through 12/31/2022. Mean cumulative function curves were used to visualize the accumulation of conditions; Andersen-Gill models estimated associations between AF/referent status and accumulation of conditions. Results: The mean age was 55 years for both patients with AF and matched referents; 71% were male. In the 3 years leading up to AF diagnosis, only 4% of patients with AF had no additional chronic conditions and 69% had ≥3 chronic conditions. In contrast, 35% of referents had 0 and 32% had ≥3 chronic conditions. The accumulation of chronic conditions was accelerated in patients with AF compared to referents prior to AF diagnosis/index (Figure). After index, patients with AF had increased accumulation of chronic conditions (HR (95% CI): 1.93 (1.71-2.20), 1.82 (1.62-2.05), 1.68 (1.55-1.83), and 1.53 (1.44-1.64) for ages <50, 50-54, 55-59, and 60-64 years, respectively). After 1 year, the differences were attenuated in all age groups with no difference observed between patients with AF and referents in the oldest age group (1.26 (1.08-1.46), 1.28 (1.11-1.47), 1.13 (1.02-1.24), and 1.05 (0.97-1.13) for ages <50, 50-54, 55-59, and 60-64 years). Conclusion: Patients <65 years with AF have an increased accumulation of chronic conditions compared to those without AF, in particular prior to their AF diagnosis and within the first year after AF diagnosis.

Abstract Background and Aims Bicuspid aortic valve (BAV) is the most common congenital heart anomaly. Lifetime morbidity and whether long-term survival varies according to BAV patient-sub-groups are unknown. This study aimed to assess lifetime morbidity and long-term survival in BAV patients in the community. Methods The authors retrospectively identified all Olmsted County (Minnesota) residents with an echocardiographic diagnosis of BAV from 1 January 1980 to 31 December 2009, including patients with typical valvulo-aortopathy (BAV without accelerated valvulo-aortopathy or associated disorders), and those with complex valvulo-aortopathy (BAV with accelerated valvulo-aortopathy or associated disorders). Results 652 consecutive diagnosed BAV patients [median (IQR) age 37 (22–53) years; 525 (81%) adult and 127 (19%) paediatric] were followed for a median (IQR) of 19.1 (12.9–25.8) years. The total cumulative lifetime morbidity burden (from birth to age 90) was 86% (95% CI 82.5–89.7); cumulative lifetime progression to ≥ moderate aortic stenosis or regurgitation, aortic valve surgery, aortic aneurysm ≥45 mm or z-score ≥3, aorta surgery, infective endocarditis and aortic dissection was 80.3%, 68.5%, 75.4%, 27%, 6% and 1.6%, respectively. Survival of patients with typical valvulo-aortopathy [562 (86%), age 40 (28–55) years, 86% adults] was similar to age-sex-matched Minnesota population (P = .12). Conversely, survival of patients with complex valvulo-aortopathy [90 (14%), age 14 (3–26) years, 57% paediatric] was lower than expected, with a relative excess mortality risk of 2.25 (95% CI 1.21–4.19) (P = .01). Conclusion The BAV condition exhibits a high lifetime morbidity burden where valvulo-aortopathy is close to unavoidable by age 90. The lifetime incidence of infective endocarditis is higher than that of aortic dissection. The most common BAV clinical presentation is the typical valvulo-aortopathy with preserved expected long-term survival, while the complex valvulo-aortopathy presentation incurs higher mortality.

Background Among patients with heart failure (HF), social risk factors (SRFs) are associated with poor outcomes. However, less is known about how co‐occurrence of SRFs affect all‐cause health care utilization for patients with HF. The objective was to address this gap using a novel approach to classify co‐occurrence of SRFs. Methods and Results This was a cohort study of residents living in an 11‐county region of southeast Minnesota, aged ≥18 years with a first‐ever diagnosis for HF between January 2013 and June 2017. SRFs, including education, health literacy, social isolation, and race and ethnicity, were obtained via surveys. Area‐deprivation index and rural‐urban commuting area codes were determined from patient addresses. Associations between SRFs and outcomes (emergency department visits and hospitalizations) were assessed using Andersen‐Gill models. Latent class analysis was used to identify subgroups of SRFs; associations with outcomes were examined. A total of 3142 patients with HF (mean age, 73.4 years; 45% women) had SRF data available. The SRFs with the strongest association with hospitalizations were education, social isolation, and area‐deprivation index. We identified 4 groups using latent class analysis, with group 3, characterized by more SRFs, at increased risk of emergency department visits (hazard ratio [HR], 1.33 [95% CI, 1.23–1.45]) and hospitalizations (HR, 1.42 [95% CI, 1.28–1.58]). Conclusions Low educational attainment, high social isolation, and high area‐deprivation index had the strongest associations. We identified meaningful subgroups with respect to SRFs, and these subgroups were associated with outcomes. These findings suggest that it is possible to apply latent class analysis to better understand the co‐occurrence of SRFs among patients with HF.

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: Coronary disease is the most common cause of heart failure (HF). HF is classified as ischemic (IHF) or non-ischemic (NIHF) based on this etiology. IHF and NIHF have distinct mechanisms not accounted for by ejection fraction (EF) and an improved understanding of the differences could drive the discovery of novel therapeutic targets. We therefore sought to identify proteomic signatures between IHF and NIHF. Methods: Proteomic profiling using the SomaScan®1.7K was performed using plasma samples from HF patients in a community cohort (Rochester Epidemiology Project, 2003-2012). IHF was defined by prior myocardial infarction, percutaneous coronary intervention or coronary artery bypass graft, or obstructive disease in at least 1 vessel on coronary angiography. We compared the protein expression levels between IHF and NIHF using t-test and applied the Benjamini-Hochberg false-discovery rate (FDR) procedure for multiple comparisons. We explored pathway analysis for functional annotations using the biological process component of the Gene Ontology Resource (Panther 17v). Results: Among 1,388 patients, there were 518 IHF patients (age: 75±12years, 39.4% female, NYHA Class III/IV: 72.5%, EF:45±15%), and 870 NIHF patients (age: 75±14years, 53.6% female, NYHA Class III/IV: 66.9%, EF:49±17%). Among 7335 plasma proteins quantified, 63 unique proteins (FDR < 0.05) were differentially expressed between IHF and NIHF. Thirty-seven proteins including Troponin-I and T, and Myoglobin showed increased expression in IHF compared to NIHF and 26 including IGFBP-3 showed decreased expression. Pathophysiologic pathways including angiogenesis, coagulation, and lipid metabolism were represented in IHF, while inflammation and remodeling signaling pathways were dominant in relation to NIHF. Conclusions: High-throughput proteomic techniques are able to delineate IHF versus NIHF patients based on distinct biomarkers and pathophysiologic processes.

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.

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.

Introduction: Over 6 million people in the U.S. have heart failure (HF), less than half are expected to survive beyond 5 years. The need to better stratify mortality risk in HF is recognized. Frailty is associated with mortality in HF but not routinely measured clinically. As frailty is linked to inflammation and malnutrition, we hypothesized that the Metabolic Vulnerability Index (MVX) a multimarker score of systemic inflammation (small HDL particles, GlycA) and malnutrition (leucine, valine, isoleucine, citrate), could serve as a biomarker of frailty to predict mortality risk. Methods: Clinical data and plasma were collected from 1,389 patients from a HF community cohort between 2003-2012. We measured frailty using the Rockwood Index as the proportion of deficits present out of 32 physical limitations and comorbidities. MVX was calculated from the nuclear magnetic resonance LipoProfile® test. Patients were categorized by frailty (0-0.15; 0.16-0.27; 0.28-0.78) and MVX (33.4-50, 50-60, 60-70, 70-85.8) cutpoints. Cox models estimated the association of frailty and MVX assignment with mortality, adjusted for Meta-Analysis Global Group in Chronic HF (MAGGIC) score, a validated clinical risk score for HF mortality. Results: Frailty and MVX scores were available in 985 patients (median age 77, IQR: 67-84; 48% women). Higher frailty was associated with higher MVX (p-trend < 0.001). The highest frailty and MVX groups experienced large increases in risk of death, after adjustment for MAGGIC score (HR=3.3, 95% CI=2.6-4.2) and (HR=2.7, 95% CI=2.1-3.5), respectively. When adjusted for one another and MAGGIC score, MVX and frailty associations with death were only minimally attenuated: frailty (HR=3.2, 95% CI=2.5-4.0) and MVX (HR=2.4, 95% CI=1.9-3.2) (Figure 1). Conclusion: In this community cohort of patients with HF, frailty and MVX are positively associated with one another. However, both indicators are independently associated with an increased risk of death and can contribute to risk stratification.

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.