Weston, Susan A.

Background: Atrial fibrillation (AF) is the most common cardiac arrhythmia in current clinical practice. Yet, contemporary community data on adverse outcomes, such as ischemic stroke (IS) and heart failure (HF), are scarce. Methods: We assembled a community-based cohort of incident AF among Olmsted County, MN residents ≥18 years of age from 2000-2010. IS, transient ischemic attack (TIA), and incident HF occurring through 12/31/2012 were identified using diagnosis codes and were manually validated. Cox proportional hazards regression was used to determine age- and sex-adjusted predictors of IS/TIA and HF, as well as the association of year of AF diagnosis with these outcomes after adjustment for demographics and comorbidities. Results: Among 3315 incident AF (mean age 72.6 years, 52% male), 346 (10%) developed a subsequent IS (n=230) or TIA (n=116) over a mean follow-up of 4.2 years. In the 2807 AF patients without prior HF, 782 (28%) developed HF over a mean of 3.9 years. Differences in the predictors of subsequent IS/TIA and HF were observed (Figure). Increasing age, female sex, and prior IS/TIA were associated with an increased risk of IS/TIA, whereas increasing age, obesity, smoking, prior myocardial infarction, diabetes, chronic obstructive pulmonary disease, peripheral vascular disease, prior IS/TIA, and renal disease predicted the development of HF after AF. The risk of subsequent IS/TIA or HF did not differ by year of AF diagnosis. Compared to those diagnosed with AF in 2000-2003, the adjusted hazard ratios (HR) (95% CI) for ischemic IS/TIA were 0.84 (0.65-1.08) and 1.02 (0.76-1.36) for those diagnosed with AF in 2004-2007 and 2008-2010, respectively (P time trend =0.58); similar results were observed for IS alone. The corresponding HRs for HF were 0.99 (0.83-1.16) and 0.93 (0.76-1.13) for those diagnosed with AF in 2004-2007 and 2008-2010 compared to 2000-2003 (P time trend =0.43). Conclusions: In the community, adverse outcomes are frequent after AF, and HF occurs more frequently than IS/TIA. Risk factors differed for IS/TIA and HF, suggesting that pathways to the occurrence of these outcomes, as well as prevention strategies, may differ. Finally, the occurrence of these outcomes did not decline over the last decade, highlighting the importance of continued rigorous surveillance of adverse outcomes in AF.

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: Heart failure (HF) patients are experiencing an epidemic of hospitalizations. Nevertheless, data on the frequency and distribution of hospitalizations over the course of the disease are lacking. Methods: We determined the rates of hospitalizations during periods of follow-up in Olmsted County, MN residents with incident HF from 2000-2010. HF was identified using ICD-9 code 428 and validated by the Framingham criteria. All hospitalizations were obtained for the 2 years following incident HF and each was categorized as due to HF, other cardiovascular (ICD-9 codes 390-427, 429-459), or non-cardiovascular causes. Follow-up was divided into discrete time periods (epochs): 0-30, 31-182, 183-365, and 366-730 days. Negative binomial regression examined associations between epochs of follow-up time and hospitalizations. Results: Among 1702 incident HF patients (mean age 76, 44% male), 1143 (67%) were hospitalized at index. Over the 2 year follow-up, 3008 hospitalizations were observed among 1136 patients, and 351 patients were hospitalized within 30 days of incident HF (median time from HF to hospitalization: 11 days). The majority of hospitalizations were due to non-cardiovascular causes (63% vs. 14% HF, 23% other cardiovascular); however, a larger proportion of HF and other cardiovascular hospitalizations were observed within the first 30 days (52% non-cardiovascular, 18% HF, 30% other cardiovascular) compared to the other time periods. The rate of hospitalization was highest within the first 30 days and was similar across sex, presentation of incident HF (inpatient, outpatient), and type of HF (preserved (≥50%), reduced (<50%) ejection fraction) (Table). Conclusions: HF patients experience high rates of hospitalizations, particularly within the first 30 days, and the majority of hospitalizations are for non-cardiovascular causes. Continued efforts to manage comorbid conditions and reduce hospitalizations in HF patients are needed.

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: Hospital readmissions are considered to be a quality of care indicator, yet little is known about their occurrence and predictors after myocardial infarction (MI) in the community. Methods: We identified all Olmsted County residents with incident MI from 1987-2007 and evaluated 30-day rehospitalizations. The reason for readmission was categorized based on ICD-9 code. Safety endpoints of revascularization (percutaneous coronary intervention [PCI] or coronary artery bypass grafting [CABG]) or fibrinolysis included stroke, acute kidney injury (rise in creatinine post-procedure of >0.5 mg/dL), access site complication (PCI), and gastrointestinal or other bleeding. Cox proportional hazard regression models were used to examine the predictors of readmission. Results: A total of 2617 patients (mean age 67 years, 41% female) had incident MI (32% with ST-elevation) from 1987-2007 and survived their initial hospitalization. A total of 583 readmissions occurred within 30 days among 512 (19.6%) patients. Approximately half (n=286, 49.1%) of readmissions were due to cardiovascular causes, and of these, most were due to ischemic heart disease (n=175, 61.2%) or heart failure (n=56, 19.6%). The proportion of non-cardiovascular rehospitalizations increased over time, from 42.2% from 1993-97 to 59.4% from 2003-07 (p for trend=0.01). A minority of those readmitted for cardiovascular reasons required repeat angiogram (n=110, 38.5%) or revascularization (n=88, 50 PCI, 38 CABG). After adjustment for confounders, key factors were associated with increased rehospitalization risk ( Figure ). Following PCI (n=1230), a stroke or bleeding event (adjusted HR 1.69, 95% CI 1.09-2.61, p=0.02) and acute kidney injury (HR 2.50, 95% CI 1.61-3.87, p<0.01) were associated with markedly increased readmission risk. Conclusions: Half of readmissions within 30 days of MI are for non-cardiovascular reasons, and this proportion has increased. Comorbidity and complications of revascularization are key predictors of readmission.

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: Whether age alone explains the comorbidity burden in heart failure (HF) is unclear. In particular, differences in the burden of co-morbid conditions in HF patients compared to population controls has not been well documented. Methods: The prevalence of 17 chronic conditions defined by the US Department of Health and Human Services were obtained in 1746 incident HF patients from 2000-2010 and controls matched 1:1 on sex and age from Olmsted County, MN. Conditions were ascertained requiring 2 occurrences of a diagnostic code. Logistic regression determined associations of each condition with HF. Results: Among the 1746 matched pairs (mean age 76.2 years, 43.5% men), the prevalence was higher in HF cases for all conditions (p<0.05) except dementia and osteoporosis. After adjusting for all conditions, hypertension, coronary artery disease, arrhythmia, asthma, chronic kidney disease, chronic obstructive pulmonary disease, diabetes, hepatitis, and substance abuse were significantly more common in HF (figure). More than a 2-fold increased odds of hepatitis, arrhythmia, and coronary artery disease was observed among HF cases. Arrhythmia (34.2%), hypertension (31.1%), and coronary artery disease (27.8%) had the largest attributable risk of HF; for example, assuming a causal relationship, if arrhythmias were eliminated, 34% of HF would be avoided. Conclusions: Compared to age- and sex-matched controls, HF patients have a higher prevalence of many chronic conditions, indicating the excess comorbidity in HF is not due to age alone. Some cardiovascular conditions, including arrhythmia, coronary artery disease, and hypertension were more common in HF. Of the non-cardiovascular conditions, hepatitis had the strongest association with HF and was an unanticipated finding that deserves additional investigation. It is important to understand comorbidities as they play a key role in the excess mortality and healthcare utilization experienced by HF patients.

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: Differences in the prevalence and duration of co-morbid conditions in atrial fibrillation (AF) patients compared to population controls have not been well documented. Methods: The prevalence and duration of 17 chronic conditions defined by the US Department of Health and Human Services, as well as anxiety, obesity, and smoking status, was obtained in a random sample of 1430 patients with incident AF from 2000-2010 and 1430 controls from Olmsted County, MN. Controls were matched to cases 1:1 on sex and age (within 5 years). Chronic conditions were ascertained electronically requiring 2 occurrences of a diagnostic code; the duration of each condition (up to 25 years) was calculated. Logistic regression determined associations of each condition with AF after adjustment for all other conditions. Results: Among the 1430 matched pairs (median age 76 years, 48.6% men), the prevalence of chronic conditions was higher in AF cases compared to controls for all conditions except asthma, dementia, depression, hepatitis, and osteoporosis (figure). However, the duration of the conditions were similar in AF compared to controls, except for hypertension (median duration 12.3 and 9.9 years in AF cases and controls, respectively; p=0.002). After adjusting for all other conditions, obesity, hypertension, congestive heart failure, coronary artery disease, chronic kidney disease, and chronic obstructive pulmonary disease remained significantly more common in AF compared to controls (figure). Conditions with the largest attributable risk of AF were hypertension (25.4%), coronary artery disease (17.7%), and congestive heart failure (12.3%). Conclusions: AF patients have a higher prevalence of many chronic conditions compared to population controls. However, besides hypertension, these comorbidities do not develop earlier in AF. Nevertheless, the excess comorbidity burden in AF is important to characterize and understand as it may partly explain the excess mortality and healthcare utilization experienced by AF patients.