Weston, Susan A.

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.

Background Temporal declines in cardiac stress tests results, coronary revascularization, and cardiovascular mortality have suggested a decline in the population burden of coronary disease until the 2000s. However, recent data indicate these favorable trends could be ending. We aimed to assess the evolution of the population burden of coronary disease in the community by examining trends in angiography and revascularization. Methods and Results We analyzed age‐ and sex‐adjusted trends from all coronary angiographic diagnostic procedures and revascularizations performed in Olmsted County, MN from 2000 to 2018. A total of 12 981 invasive angiograms were performed among 9049 individuals (64% men; 55% aged ≥65 years). Adjusted angiography rates decreased by 30% (95% CI , 25%–34%) between 2000 and 2009 and leveled off thereafter. Including computed tomography, angiography uncovered an increase in angiography use in recent years (risk ratio=1.15 [95% CI , 1.07–1.23] for 2018 versus 2014) and a decline in the prevalence of anatomic CAD from 2000 to 2018. CAD severity declined substantially from 2000 to 2009, followed by a plateau. Among 6570 revascularizations (72% men; 57% aged ≥65 years), 77% were percutaneous coronary interventions and 23% coronary artery bypass graft surgeries. The adjusted revascularization rates declined by 34% (95% CI , 27%–39%) from 2000 to 2009, followed by a plateau (risk ratio=1.10 [95% CI , 1.00–1.22]). Conclusions Between 2000 and 2018 in the community, coronary angiography use declined initially, leveled off, and then increased. Trends in CAD severity and revascularization use decreased then plateaued. The most recent trends are concerning as they suggest the burden of coronary disease is no longer declining. This warrants reinvigorated primary prevention and population surveillance.

Background: The effective self-management of heart failure (HF) is highly dependent on cognitive function. Dementia frequently coexists with HF, but its exact prevalence and impact on health care utilization and death is not well understood. Methods: Residents from 7 southeast Minnesota counties with a first-ever code for HF (ICD-9 428 and ICD-10 I50) between 1/1/2012 and 12/31/2017 were identified. Dementia before and after the HF diagnosis was ascertained with codes recommended by the US Department of Health and Human Services. Patients were followed through 12/31/2018 for death and hospitalizations. Cox and Andersen-Gill models were used to examine associations between dementia (prior and post HF) and death and hospitalizations, respectively. Dementia after HF was analyzed as a time-dependent variable. Results: Among 6,312 patients with HF (mean age 75 years, 49% female), 636 (10%) had a prior diagnosis of dementia. After a mean (SD) follow-up of 3.1 (1.8) years, 601 (9.5%) patients were diagnosed with dementia post HF. The median time from HF to dementia was 1.3 years, with 43% of dementia cases occurring within 1 year and 64% within 2 years post HF. During follow-up, 2,678 deaths and 15,095 hospitalizations occurred. After adjustment, patients with dementia before HF had nearly a 2-times increased risk of death and 10% increased risk of hospitalization compared to those that did not have dementia (Table). Dementia after HF was associated with a 78% increased risk of death and 63% increased risk of hospitalization compared to those that did not have dementia. Conclusions: Approximately 10% of patients had dementia prior to HF and 10% develop dementia shortly after HF diagnosis. Patients with HF and dementia have an increased risk of death and hospitalization compared to dementia-free counterparts. These data underscore the importance of recognizing the presence and impact of dementia on outcomes in HF to optimize care delivery.

Background: Patient reported measures are fundamental to patient centered research and are often collected by questionnaires. Low participation can compromise the validity of the results; transparent reporting and comparing characteristics of participants to nonparticipants is important to assess the generalizability of survey results. We analyzed participation in a survey of social determinants of health among a population-based sample of patients with heart failure (HF). Methods: A total of 7920 residents from 11 Minnesota counties were identified with a first-ever code for HF (ICD-9 428 and ICD-10 I50) between 1/1/2013 and 12/31/2016. Participants completed a battery of 12 questionnaires by mail or telephone. Characteristics and outcomes data were extracted from medical records and compared between participants and nonparticipants. Response rate (RR) was calculated using guidelines of the American Association for Public Opinion Research (AAPOR). The association between nonparticipation and outcomes was examined by Cox proportional hazards regression for death and Andersen-Gill modeling for hospitalizations. Results: Among 7920 patients, 3438 responded to the survey (AAPOR RR#2= 43%). Numerous clinical and demographic differences between participants and non-participants were noted (Table), particularly for education, marital status, and neuropsychiatric conditions. After a mean (±SD) of 1.5±1 years after survey administration, 1,577 deaths and 5,873 hospitalizations had occurred. Nonparticipation was associated with a two-fold increased risk of death (HR: 2.30; 95% CI: 2.06-2.57; p<0.001) and a 10% increased risk of hospitalizations (HR: 1.10; 95% CI: 1.01-1.21; p=0.03) after adjusting for age, sex, marital status, coronary disease, arrhythmia, hyperlipidemia, cancer, chronic kidney disease, depression and anxiety. Conclusions: In a large survey of patients with HF, participation was associated with large differences in clinical and demographic characteristics and outcomes. Examining the impact of participation is critical to draw inference from studies of patient reported measures.

Background: The decline in cardiovascular disease (CVD) mortality in the United States noted since the mid 1970s has recently leveled off. This has generated concern and a need for further analyses to understand geographic variation, cause-specific deaths, location of death, and age-specific trends. Methods: Death certificate data obtained from the Minnesota Department of Health were used for analysis. The American Heart Association (AHA) classification was applied to the underlying cause of death to generate the following CVD categories: coronary heart disease (CHD), non-CHD diseases of the heart, and non-cardiac circulatory diseases. Temporal trends in death rates were explored by negative binomial regression, which enables estimates of age- and sex-adjusted average annual percentage changes (AAPCs). Differences in AAPCs between 2000-2009 and 2010-2017 were tested overall and across demographic, CVD, and location of death categories. Results: CVD mortality rates, which were declining in 2000-2009, have leveled off in 2010-2017 (Table). These trends were observed across all subgroups. After adjusting for sex, the age group that drove the decline in rates from 2000-2009 was the 65-84 year old group, which experienced no change in rates from 2010-2017. Across CVD categories, after adjusting for age and sex, the decline in rates from 2000-2009 was largely attributable to steep reductions in CHD and non-cardiac circulatory diseases, with lesser reduction in non-CHD diseases of the heart. In 2010-2017, CHD and non-cardiac circulatory diseases rates showed no change, whereas the rate of non-CHD diseases of the heart increased. The rates for in- and out-of-hospital deaths improved in 2000-2009 with no further improvement observed in 2010-2017. Conclusions: CVD mortality rates are no longer declining in Minnesota, paralleling national trends. These trends are particularly concerning for non-CHD diseases of the heart.

Background: Previous studies have indicated that living in a rural or non-urban area may be associated with poor health outcomes. However, data on rurality and death in heart failure (HF) are scarce. Methods: Residents from 11 southeast MN counties with a first-ever code for HF (including codes ICD-9 428 and ICD-10 I50) between 1/1/2013 and 12/31/2016 were identified. Resident address was geo-coded and classified according to the Rural-Urban Commuting Area (RUCA) codes, which classifies US census tracts to measure population density, urbanization and daily commuting. Rurality was defined as living in a non-metropolitan area. Cox proportional hazards regression was used to determine the association between living in a rural vs. urban area and death. Results: Among 6,996 patients (mean age 73 years, 52% male), 54% of patients lived in a rural area. These patients were older and had a lower level of educational attainment and comorbidity burden (p<0.001). After a mean (SD) follow-up of 2.9 (1.3) years, 1,856 deaths occurred. Living in a rural area was univariately associated with a higher risk of death (HR 1.20, 95% CI: 1.09-1.31; Table). After adjustment for age, sex, education status and comorbidity burden, rurality remained associated with an increased risk of death (HR 1.15; 95% CI: 1.04-1.27). Conclusions: Among patients with HF, living in a rural area is associated with an increased risk of death. Further study to identify and address the mechanisms through which rural residence influences mortality in patients with HF is needed.

Background: Multimorbidity is common in atrial fibrillation (AF); however, the impact of the number and type of comorbid conditions on death and hospitalizations is uncertain. Methods: The prevalence of 17 chronic conditions was obtained in 1430 patients with incident AF from 2000-2010 from Olmsted County, MN. Chronic conditions were ascertained electronically prior to AF and were classified into 3 groups: cardiovascular (CV), other physical, and mental. All hospitalizations and deaths were obtained through 12/31/2014. Associations of the number and type of condition and each individual condition with hospitalizations and death were determined using Andersen-Gill and Cox regression, respectively. Results: Among the 1430 patients with AF (median age 76 years, 48.6% men), the mean (SD) number of chronic conditions was 4.7 (2.7). A total of 4496 hospitalizations and 812 deaths occurred over a mean (SD) follow-up of 5.4 (4.1) years. An increase in the number of conditions of each type was associated with increased risks of death and hospitalizations (Table). Mental conditions (which included depression, anxiety, dementia, schizophrenia, and substance abuse) exhibited the strongest association with death, whereas other physical conditions exhibited the strongest association with hospitalizations. Individual predictors of death included heart failure (HF), hypertension, chronic obstructive pulmonary disease (COPD), chronic kidney disease (CKD), depression, dementia, and substance abuse. Predictors of hospitalizations included HF, coronary artery disease, diabetes, COPD, CKD, arthritis, osteoporosis, and substance abuse. Conclusions: Patients with AF have many comorbid conditions which are associated with death and hospitalizations. Although conditions of each type were associated with outcomes, mental conditions exhibited strongest association with death. These data highlight the importance of management of not only CV comorbidities but other physical and mental comorbidities in patients with AF.

Background As the population with cardiovascular disease ages, geriatric conditions are of increasing relevance. A possible geriatric prognostic indicator may be a fall risk score, which is mandated by The Joint Commission to be measured on all hospitalized patients. The prognostic value of a fall risk score on outcomes after dismissal is not well known. Thus, we aimed to determine whether a fall risk score is associated with death and hospital readmissions in patients with a recent incident cardiovascular disease event. Methods and Results In this retrospective cohort study, Olmsted County, MN patients with incident heart failure, myocardial infarction, or atrial fibrillation between August 1, 2005, and December 31, 2011, who were hospitalized within 180 days after the event were studied. Fall risk was measured by the Hendrich II fall risk model. Patients were followed for death or readmission within 30 days or 1 year. Among 2456 hospitalized patients with recent incident cardiovascular disease (549 heart failure, 784 myocardial infarction, 1123 atrial fibrillation; mean [SD] age, 71 [15] years; 55% men), the fall risk score was high in 22% of patients and moderate in 38%. The risk of death was increased if the fall risk score was increased, independent of age and comorbidities (moderate hazard ratio, 1.51; 95% CI, 1.09–2.08; high hazard ratio, 3.49; 95% CI, 2.52–4.85). Similarly, the risk of 30-day readmissions was substantially increased with a greater fall risk score (moderate hazard ratio, 1.29; 95% CI, 1.03–1.62; high hazard ratio, 1.63; 95% CI, 1.23–2.15). Results were similar for readmissions within 1 year. Conclusions More than half of hospitalized patients with recent incident cardiovascular disease have an elevated fall risk score, which is associated with an increased risk in readmissions and death. These results delineate an approach for risk stratification and management that may prevent readmissions and improve survival.