Weston, Susan A.

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.

Objective Ceramides have been associated with several ageing-related conditions but have not been studied as a general biomarker of multimorbidity (MM). Therefore, we determined whether ceramide levels are associated with the rapid development of MM. Design Retrospective cohort study. Setting Mayo Clinic Biobank. Participants 1809 persons in the Mayo Clinic Biobank ≥65 years without MM at the time of enrolment, and with ceramide levels assayed from stored plasma. Primary outcome measure Persons were followed for a median of 5.7 years through their medical records to identify new diagnoses of 20 chronic conditions. The number of new conditions was divided by the person-years of follow-up to calculate the rate of accumulation of new chronic conditions. Results Higher levels of C18:0 and C20:0 were associated with a more rapid rate of accumulation of chronic conditions (C18:0 z score RR: 1.30, 95% CI: 1.10 to 1.53; C20:0 z score RR: 1.26, 95% CI: 1.07 to 1.49). Higher C18:0 and C20:0 levels were also associated with an increased risk of hypertension and coronary artery disease. Conclusions C18:0 and C20:0 were associated with an increased risk of cardiometabolic conditions. When combined with biomarkers specific to other diseases of ageing, these ceramides may be a useful component of a biomarker panel for predicting accelerated ageing.

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: 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.

Background Electronic medical records are increasingly used to identify disease cohorts; however, computable phenotypes using electronic medical record data are often unable to distinguish between prevalent and incident cases. Methods and Results We identified all Olmsted County, Minnesota residents aged ≥18 with a first‐ever International Classification of Diseases, Ninth Revision (ICD‐9) diagnostic code for atrial fibrillation or atrial flutter from 2000 to 2014 (N=6177), and a random sample with an International Classification of Diseases, Tenth Revision (ICD‐10) code from 2016 to 2018 (N=200). Trained nurse abstractors reviewed all medical records to validate the events and ascertain the date of onset (incidence date). Various algorithms based on number and types of codes (inpatient/outpatient), medications, and procedures were evaluated. Positive predictive value (PPV) and sensitivity of the algorithms were calculated. The lowest PPV was observed for 1 code (64.4%), and the highest PPV was observed for 2 codes (any type) >7 days apart but within 1 year (71.6%). Requiring either 1 inpatient or 2 outpatient codes separated by >7 days but within 1 year had the best balance between PPV (69.9%) and sensitivity (95.5%). PPVs were slightly higher using ICD‐10 codes. Requiring an anticoagulant or antiarrhythmic prescription or electrical cardioversion in addition to diagnostic code(s) modestly improved the PPVs at the expense of large reductions in sensitivity. Conclusions We developed simple, exportable, computable phenotypes for atrial fibrillation using structured electronic medical record data. However, use of diagnostic codes to identify incident atrial fibrillation is prone to some misclassification. Further study is warranted to determine whether more complex phenotypes, including unstructured data sources or using machine learning techniques, may improve the accuracy of identifying incident atrial fibrillation.

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