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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.

Background: Several chronic diseases can contribute to functional disability. However, little is known about functional disability and its progression overtime in chronic heart failure (HF). We aimed to investigate the prevalence and progression of difficulty with activities of daily living (ADLs) and its association with mortality in patients with HF. Methods: We enrolled Southeastern Minnesota residents with HF into a longitudinal study from September 2, 2003 through January 31, 2012. A patient’s difficulty with 9 ADLs (feeding themselves, dressing, toileting, housekeeping, climbing stairs, bathing, walking, using transportation, managing medications) was assessed by questionnaire. Patients were followed for all-cause mortality. Using Rasch analysis to determine the order of ADL difficulty, patients were divided into 3 categories (minimal, moderate, severe difficulty). Predictors of ADL difficulty were assessed using negative binomial regression and the association between ADL difficulty and death with Cox proportional hazard regression. Results: Among 1128 patients (mean age 74.7 years, 49.2% female), most (59.4%) reported difficulty with one or more ADLs at enrollment, with 272 (24.1%) and 146 (12.9%) reporting moderate and severe difficulty, respectively. The independent predictors of difficulty with ADLs were older age, female sex, diabetes, morbid obesity, cerebrovascular disease, dementia, anemia, and unmarried status. After a mean (SD) follow-up of 3.2 (2.4) years, 615 (54.5%) patients had died. There was a stepwise increase in the risk of death as difficulty with ADLs increased ( Figure ). After adjusting for age, sex, and comorbidity, the HR (95% CI) for mortality was 1.52 (1.25-1.83, p<0.001) for patients with moderate and 2.28 (1.82-2.85, p<0.001) for those with severe difficulty with ADLs compared to those with minimal difficulty (p for trend<0.001). In most patients (73.5%), difficulty with ADLs was stable over time. However, survivors reporting persistently severe or worsening ADL difficulty in a second assessment (median 9 months later) were at an increased risk of subsequent mortality (adjusted HR 2.08, 95% CI 1.70-2.55, p<0.001). Conclusions: Functional disability is common in patients with HF, can progress over time, and is independently associated with adverse prognosis.

Background: There is growing awareness of an association between cardiovascular disease (CVD) and fractures, and a temporal increase in fracture risk after myocardial infarction has been noted but the mechanisms are not delineated. To evaluate this relationship, we systematically examined the association of hip fracture with all disease categories and assessed related secular trends. Methods: Using resources of the Rochester Epidemiology Project, a population-based incident case-control study was conducted. Disease history was compared between all Olmsted County, Minnesota, residents aged 50 years or older with a first hip fracture in 1985–2006 and community control subjects individually matched (1:1) to cases on age, sex, and index year (n=3,808; mean age [SD], 82 [9] years; 76% women). Results: All CVD and numerous non-CVD categories (e.g., infectious diseases, nutritional and metabolic diseases, mental disorders, diseases of the nervous system and sense organs, and diseases of the respiratory system) were associated with fracture risk. However, increasing temporal trends were almost exclusively detected in categories of CVD (Figure). The largest increases in association were observed for ischemic heart disease, other forms of heart disease (including heart failure), hypertension, and diabetes and were more pronounced among elderly women than other demographic groups. Conclusions: While the association with hip fracture was not specific to CVD, temporal increases were mainly detected in cardio-metabolic diseases, all of which have also been linked previously to frailty. This could delineate a common pathway that warrants further investigation.

Background: The goal of heart failure (HF) performance measures is to improve the quality of care by enabling the practice implementation of guidelines. The guidelines define the population eligible for each measure. Little is known about the proportion of eligible patients to which these measures are applicable in the community. Methods: We determined the eligibility for and the adherence to performance measures and beta-blocker therapy in a community-based cohort of hospitalized patients diagnosed with incident HF from January 2005 to April 2011. Results: Of 294 inpatients with first ever HF (mean age 72 ± 15 years, 48% men), ejection fraction was measured in 292 (99%) patients during hospitalization. Of these, 97 (33%) had an ejection fraction <40% and were thus candidates for beta-blocker and angiotensin converting enzyme (ACE) inhibitors or angiotensin receptor blocker (ARB) therapy. After excluding individuals with absolute contraindications, 71 patients were eligible to receive ACE inhibitors or ARB, and 86 were eligible to receive beta-blockers (figure). Among those eligible, 83% received ACEI/ARB and 91% received beta-blockers. Excluding those with relative contraindications, 70 patients were eligible for beta-blockers and 90% of them received it. Among the 139 (47%) individuals with HF and atrial fibrillation, 35 were eligible for warfarin and 49% received it. Of the 36 current smokers, 22 (61%) received cessation counseling during hospitalization. Conclusions: In the community, among patients hospitalized with HF eligible for performance measures, the implementation of performance measures varies by measure and can be improved for some. However, most community HF patients are not eligible for performance measures and other approaches should be identified to improve their care and outcomes.

Objective: To compare two measures of frailty in a community cohort of heart failure (HF) patients. Background: Frailty is increasingly recognized as an important prognostic indicator in HF. Several methods have been proposed to measure frailty. Some conceptualize frailty as a biological phenotype, while others define it as an accumulation of deficits (impairments, disabilities, comorbid diseases), termed the Cumulative Deficit Index (CDI). Each approach has conceptual strengths and limitations; a distinct feature of the CDI is the ability to extract data from medical records. These two methods have never been compared in a community HF cohort. Methods: Olmsted, Dodge and Fillmore County, MN residents with HF between 10/2007 and 12/2010 were prospectively recruited to assess the frailty phenotype. The biological frailty phenotype was defined as ≥3 of the following: unintentional weight loss >10 lbs. in 1 year, physical exhaustion, weak grip strength, and slowness and low activity measured by the SF-12 physical component score. Intermediate frailty was defined as having 1–2 components. The CDI was based on 33 deficits and defined as the proportion of deficits present for each patient. Results: Among 131 patients (mean age 70.5±14, 62% male), according to the biological frailty phenotype definition 18% were frail, 50% intermediate frail and 31% not frail. The CDI ranged from 0.03–0.70, with a mean (SD) of 0.22 (0.12). The CDI was significantly correlated with the frailty phenotype (r=0.59, p<0.001). The mean (SD) of the CDI was 0.35 (0.14), 0.21 (0.10) and 0.15 (0.07) among the frail, intermediate frail and not frail phenotypes (Figure). Conclusion: The CDI is significantly associated with the biological frailty phenotype and can thus be used as an alternative approach to measure frailty in HF. As the components of the biological frailty phenotype are not routinely assessed clinically, the CDI, which can be ascertained from medical records, is a feasible approach to ascertain frailty in large cohorts.

Background: Little is known about the association between coronary artery disease (CAD) and the risk of heart failure (HF) after myocardial infarction (MI), and whether it differs by reduced (HFrEF) or preserved (HFpEF) ejection fraction (EF) has yet to be determined. Subjects and Methods: Olmsted County, Minnesota residents (n=1,924; mean age, 64 years; 66% male) with first MI diagnosed in 1990-2010 and no prior HF were followed through 2013. Framingham Heart Study criteria were used to define HF, which was further classified according to EF (applying a 50% cutoff). The extent of angiographic CAD was defined at index MI according to the number of major epicardial coronary arteries with ≥50% lumen diameter obstruction. Fine & Gray and Cox proportional hazards regression models were used to assess the association of CAD categories with incidence of HF, and multiple imputation methodology was applied to account for the 19% with missing EF data. Results: During a mean (SD) follow-up of 6.7 (5.9) years, 594 patients developed HF. Adjusted for age and sex, with death considered a competing risk, the cumulative incidence rates of HF among patients with 1- (n=581), 2- (n=622), and 3-vessel disease (n=721) were 11.2%, 14.6% and 20.5% at 30 days; and 18.1%, 22.3% and 29.4% at 5 years after MI, respectively. The increased risk of HF with greater number of occluded vessels was only modestly attenuated after further adjustment for patient and MI characteristics, and did not differ materially by EF (Table). Conclusions: The extent of angiographic CAD expressed by the number of diseased vessels is independently associated with HF incidence after MI. The association is evident promptly after MI and applies to both HFrEF and HFpEF.

Background: Referral to a skilled nursing facility (SNF) should contribute to reducing hospital readmissions; however, a “revolving door” phenomenon after admission to SNF has been hypothesized to drive readmissions. The urgent need to study the impact of SNF on readmissions in heart failure (HF) was recently emphasized, yet this has never been studied in the community. Objectives: To evaluate the association between discharge to SNF and 30-day readmissions in a community cohort of hospitalized incident HF patients. Methods: Olmsted County, MN residents hospitalized with first ever (incident) HF (International Classification of Diseases-9 th Revision code 428) from 1995 through 2010 were identified. HF was validated by Framingham criteria. Patients residing in a SNF prior to hospitalization were excluded from the analysis. Logistic regression was used to examine the association between discharge to SNF and 30-day readmissions. Results: Among 1360 HF patients (mean age 74±14, 47% male), 241(18%) were referred to a SNF. Overall, 296 (22%) patients were readmitted within 30-days after index hospitalization. The proportion of 30-day readmissions was greater among patients discharged to a SNF compared to patients discharged home (27% vs 21%, p=0.031). After adjustment for age and sex, patients discharged to a SNF had a 40% increase in the odds of having a hospital readmission within 30 days post HF compared to those discharged home (OR: 1.42, 95% CI 1.01-1.99). Further adjustment for year of HF diagnosis, ejection fraction, anemia, renal function, dementia and cancer did not alter the strength of the association (OR: 1.43, 95% CI: 0.99-2.09). Conclusion: Among community patients with HF, 30-day readmissions remain frequent and are more likely to occur among patients discharged to a SNF compared to those discharged home. These data provide new insight into the drivers of HF readmissions and suggest that interventions targeted to HF patients in SNFs may be warranted.

Objectives Sex as a biological variable affects response to opioids. However, few reports describe the prevalence of specific adverse reactions to commonly prescribed opioids in men and women separately. A large cohort was used to investigate sex differences in type and occurrence of adverse reactions associated with use of codeine, tramadol, oxycodone and hydrocodone. Design Retrospective cohort study. Setting Participants in the Right Drug, Right Dose, Right Time (RIGHT) Study. Participants The medical records of 8457 participants in the RIGHT Study who received an opioid prescription between 1 January 2004 and 31 December 2017 were reviewed 61% women, 94% white, median age (Q1–Q3)=58 (47–66). Primary and secondary outcome measures Adverse reactions including gastrointestinal, skin, psychiatric and nervous system issues were collected from the allergy section of each patient’s medical record. Sex differences in the risk of adverse reactions due to prescribed opioids were modelled using logistic regression adjusted for age, body mass index, race and ethnicity. Results From 8457 participants (of which 449 (5.3%) reported adverse reactions), more women (6.5%) than men (3.4%) reported adverse reactions to at least one opioid (OR (95% CI)=2.3 (1.8 to 2.8), p<0.001). Women were more likely to report adverse reactions to tramadol (OR (95% CI)=2.8 (1.8 to 4.4), p<0.001) and oxycodone (OR (95% CI)=2.2 (1.7 to 2.9), p<0.001). Women were more likely to report gastrointestinal (OR (95% CI)=3.1 (2.3 to 4.3), p<0.001), skin (OR (95% CI)=2.1 (1.4 to 3.3), p=0.001) and nervous system issues (OR (95% CI)=2.3 (1.3 to 4.2), p=0.004). Conclusions These findings support the importance of sex as a biological variable to be factored into pain management studies.

Background Prior reports indicate that living in a rural area may be associated with worse health outcomes. However, data on rurality and heart failure (HF) outcomes are scarce. Methods and Results Residents from 6 southeastern Minnesota counties with a first‐ever code for HF ( International Classification of Diseases, Ninth Revision [ ICD‐9 ], code 428, and International Classification of Diseases, Tenth Revision [ ICD‐10 ] code I50) between January 1, 2013 and December 31, 2016, were identified. Resident address was classified according to the rural‐urban commuting area codes. Rurality was defined as living in a nonmetropolitan area. Cox regression was used to analyze the association between living in a rural versus urban area and death; Andersen‐Gill models were used for hospitalization and emergency department visits. Among 6003 patients with HF (mean age 74 years, 48% women), 43% lived in a rural area. Rural patients were older and had a lower educational attainment and less comorbidity compared with patients living in urban areas ( P <0.001). After a mean (SD) follow‐up of 2.8 (1.7) years, 2440 deaths, 20 506 emergency department visits, and 11 311 hospitalizations occurred. After adjustment, rurality was independently associated with an increased risk of death (hazard ratio [HR], 1.18; 95% CI, 1.09–1.29) and a reduced risk of emergency department visits (HR, 0.89; 95% CI, 0.82–0.97) and hospitalizations (HR, 0.78; 95% CI, 0.73–0.84). Conclusions Among patients with HF, living in a rural area is associated with an increased risk of death and fewer emergency department visits and hospitalizations. Further study to identify and address the mechanisms through which rural residence influences mortality and healthcare utilization in HF is needed in order to reduce disparities in rural health.

AbstractThe purpose of this analysis was to develop and validate computable phenotypes for heart failure (HF) with preserved ejection fraction (HFpEF) using claims‐type measures using the Rochester Epidemiology Project. This retrospective study utilized an existing cohort of Olmsted County, Minnesota residents aged ≥ 20 years diagnosed with HF between 2007 and 2015. The gold standard definition of HFpEF included meeting the validated Framingham criteria for HF and having an LVEF ≥ 50%. Computable phenotypes of claims‐type data elements (including ICD‐9/ICD‐10 diagnostic codes and lab test codes) both individually and in combinations were assessed via sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) with respect to the gold standard. In the Framingham‐validated cohort, 2,035 patients had HF; 1,172 (58%) had HFpEF. One in‐patient or two out‐patient diagnosis codes of ICD‐9 428.3X or ICD‐10 I50.3X had 46% sensitivity, 88% specificity, 84% PPV, and 54% NPV. The addition of a BNP/NT‐proBNP test code reduced sensitivity to 35% while increasing specificity to 91% (PPV = 84%, NPV = 51%). Broadening the diagnostic codes to ICD‐9 428.0, 428.3X, and 428.9/ICD‐10 I50.3X and I50.9 increased sensitivity at the expense of decreasing specificity (diagnostic code‐only model: 87% sensitivity, 8% specificity, 56% PPV, 30% NPV; diagnostic code and BNP lab code model: 61% sensitivity, 43% specificity, 60% PPV, 45% NPV). In an analysis conducted to mimic real‐world use of the computable phenotypes, any one in‐patient or out‐patient code of ICD‐9 428/ICD‐10 150 among the broader population (N = 3,755) resulted in lower PPV values compared with the Framingham cohort. However, one in‐patient or two out‐patient instances of ICD‐9 428.0, 428.9, or 428.3X/ICD‐10 150.3X or 150.9 brought the PPV values from the two cohorts closer together. While some misclassification remains, the computable phenotypes defined here may be used in claims databases to identify HFpEF patients and to gain a greater understanding of the characteristics of patients with HFpEF.