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AbstractIntroductionWe investigated the association of the area deprivation index (ADI) with cognitive decline, mild cognitive impairment (MCI), and dementia in older adults (≥50 years old). ADI is a neighborhood socioeconomic disadvantage measure assessed at the census block group level.MethodsThe study included 4699 participants, initially without dementia, with available ADI values for 2015 and at least one study visit in 2008 through 2018. Using logistic regression and Cox proportional hazards models with age as the time scale, we assessed the odds for MCI and the risk for dementia, respectively.ResultsIn cognitively unimpaired (CU) adults at baseline, the risk for progression to dementia increased for every decile increase in the ADI state ranking (hazard ratio = 1.06, 95% confidence interval (1.01–1.11), P = .01). Higher ADI values were associated with subtly faster cognitive decline.DiscussionIn older CU adults, higher baseline neighborhood socioeconomic deprivation levels were associated with progression to dementia and slightly faster cognitive decline.Highlights The study used area deprivation index, a composite freely available neighborhood deprivation measure. Higher levels of neighborhood deprivation were associated with greater mild cognitive impairment odds. Higher neighborhood deprivation levels were associated with higher dementia risk.

Background Persons who accumulate chronic conditions at a rate faster than their peers may experience accelerated aging and poor health outcomes, including functional limitations. Methods Adults aged ≥40 years who resided in Olmsted County, Minnesota on 1 January 2006 were identified. The prevalence of 21 chronic conditions was ascertained, and age-specific quartiles of the number of chronic conditions was estimated within 4 age groups: 40–54, 55–64, 65–74, and ≥75 years. Difficulty with nine patient-reported functional limitations (including basic and instrumental activities of daily living and mobility activities) were ascertained through 31 October 2018. Cox regression was used to model associations of chronic condition quartiles with new-onset functional limitations considered separately. We estimated absolute risk differences and hazard ratios stratified by age group, and adjusted for sex, race, ethnicity, marital status, education, and the residual effect of age. Results Among 39,624 persons (44.5% men, 93.2% white), the most common reported new functional limitations were difficulty with climbing stairs, walking, and housekeeping. For all functional limitations, the absolute risk differences were largest among the oldest age group (≥75 years). Approximately twofold increased hazard ratios were observed among those in the highest vs. lowest quartile for the three oldest age groups, and approximately threefold or higher hazard ratios were observed for persons aged 40-54 years. Conclusion Persons with increased accumulation of chronic conditions experience increased risks of developing functional limitations compared to their peers. These findings underscore the importance of assessing health status and of employing interventions to prevent and effectively manage multi-morbidity at all ages.

Abstract Introduction End‐of‐life care differs by serious illness diagnosis. Cancer and dementia are serious illnesses that have been associated with less intensive end‐of‐life health care use. It is not known how health care utilization varies in the presence of >1 serious illness. Methods We used the Rochester Epidemiology Project to identify persons living in a midwestern area who died on July 1, 2017–June 30, 2018 at age ≥65 years, and were seriously ill. We examined the number of emergency department (ED), hospital, and intensive care unit (ICU) stays in the last 6 months and the last 30 days of life. We used Poisson regression to determine the incidence rate ratio for ED, hospital, and ICU stay in the last 6 months and 30 days of life by number of serious illness diagnoses. For cancer and dementia, we examined the effect of an additional serious illness. Results We included a population of 1372 adults who were, on average, 84 years, 52% female, and 96% white. Approximately 41% had multiple serious illnesses. Compared to older adults with 1 serious illness diagnosis, rates of hospitalization, and ICU stay for adults with 2 or ≥3 serious illness diagnoses were at least 1.5 times higher in the last 6 months and the last 30 days of life. Rates of ED visits were significantly higher for older adults with 2 or ≥3 serious illness diagnoses in the last 6 months of life, but only higher for those with ≥3 versus 1 serious illness diagnosis in the last 30 days of life. For both cancer and dementia, rates of ED visits, hospitalization and ICU stay were lower for the condition alone than when an additional serious illness diagnosis was present. Conclusion Having multiple serious illnesses increases the risk of health care utilization at the end of life.

Cardiovascular disease surveillance involves quantifying the evolving population‐level burden of cardiovascular outcomes and risk factors as a data‐driven initial step followed by the implementation of interventional strategies designed to alleviate this burden in the target population. Despite widespread acknowledgement of its potential value, a national surveillance system dedicated specifically to cardiovascular disease does not currently exist in the United States. Routinely collected health care data such as from electronic health records (EHRs) are a possible means of achieving national surveillance. Accordingly, this article elaborates on some key strengths and limitations of using EHR data for establishing a national cardiovascular disease surveillance system. Key strengths discussed include the: (1) ubiquity of EHRs and consequent ability to create a more “national” surveillance system, (2) existence of a common data infrastructure underlying the health care enterprise with respect to data domains and the nomenclature by which these data are expressed, (3) longitudinal length and detail that define EHR data when individuals repeatedly patronize a health care organization, and (4) breadth of outcomes capable of being surveilled with EHRs. Key limitations discussed include the: (1) incomplete ascertainment of health information related to health care–seeking behavior and the disconnect of health care data generated at separate health care organizations, (2) suspect data quality resulting from the default information‐gathering processes within the clinical enterprise, (3) questionable ability to surveil patients through EHRs in the absence of documented interactions, and (4) the challenge in interpreting temporal trends in health metrics, which can be obscured by changing clinical and administrative processes.

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.

Background: Delay in seeking treatment for symptoms of atrial fibrillation (AF) represents a missed opportunity for early treatment that is critical for reducing serious complications of AF such as stroke and heart failure. Delays ranging from a few days to years after symptom onset have been reported. Little is known about the symptom experiences and cognitive behavioral responses that differentiate those who seek treatment early after symptom onset from those who delay treatment-seeking. Purpose: This study compared symptoms and cognitive behavioral responses of early and late (up to 24 hours versus greater than 24 hours after symptom onset) treatment seekers with symptoms of AF. Methods: A descriptive design was used. Participants (N = 150) were recruited from a Midwest academic medical center. Data regarding 1) type and character of symptoms 2) cognitive and behavioral responses to the symptoms and 3) time from symptom onset to seeking treatment were collected using a structured interview. Independent t-tests for continuous variables and Chi-square tests for categorical variables were used to compare treatment seeking groups. Results: Participants were 51% female, with a mean age of 66.5 SD = 11.1) years. Early treatment seekers (n = 27) were more likely to experience a rapid heartbeat (p <.01), unremitting symptoms (<.001), report fear associated with symptoms (p = .005), and appraise symptoms as very serious (p <.001). They were less likely to believe symptoms could be self-managed (p <.001). Late treatment seekers (n = 123) were more likely to experience fatigue and dyspnea as primary symptoms (p≤ .01), report intermittent symptoms (p <.001), attribute symptoms to stress (p ≤ .02), deconditioning (p <.001), or lack of sleep (p = .009), and take a “wait and see” approach to managing symptoms (p <.001). There were no differences between early and late treatment seekers for age, sex, marital status, education level, and history of coronary artery disease or heart failure. Conclusions: Symptom type and characteristic, and cognitive behavioral responses of early treatment seekers differed from those of late treatment seekers. Experiencing a rapid heartbeat and unremitting symptoms may have helped participants form a cognitive appraisal and behavioral response conducive to early treatment-seeking. Experiencing intermittent symptoms less identifiable with a cardiac origin may have hindered development of cognitive appraisals and behavioral responses that promote early treatment- seeking. Education to promote early treatment-seeking for symptoms should include information about the diverse type and characteristics of symptoms of AF.

Background: Heart failure (HF) with improved ejection fraction (HFiEF) is defined as a new subset in addition to the subsets of HF with reduced EF (≤40%; HFrEF) and HF with preserved EF (>40%). The characteristics and outcomes of patients with ischemic HFiEF after coronary revascularization are unknown. Methods and Results: We identified 1,816 patients with coronary artery diseases and reduced EF (≤40%) in a large hospital in China, who underwent either coronary artery bypass graft surgery (n=1,126) or percutaneous coronary intervention with drug eluting stent (n=687) from January 2005 to December 2014. All patients had EF reassessment during follow-up. The improvement of EF peaked at 3 months and persisted for more than one year after revascularization. Thus, 589 patients who had EF reassessment between 3 to 12 months after revascularization were included in the definitions of HFiEF and HFrEF patient cohort and subsequent survival analysis. 367 (62.3%) patients had EF >40% which constituted HFiEF cohort while 222 (37.7%) patients with EF≤40% constituted HFrEF cohort. Using stepwise logistic regression, we identified preoperative EF (odds ratio [OR], 1.06; 95% confidence interval [CI], 1.02-1.11; P =0.006), left ventricular end-systolic diameter (OR, 0.93; 95% CI, 0.91-0.95; P =0.000) and history of myocardial infarction (OR, 0.61; 95% CI, 0.42-0.88; P =0.008) were associated with being in HFiEF group. Using Cox proportional hazard models, HFiEF patients had lower risk of all-cause mortality (hazard ratios [HR], 0.39; 95% CI, 0.23-0.68; P =0.001), sudden cardiac death (HR, 0.32; 95% CI, 0.14-0.75; P =0.009) and death due to HF (HR, 0.13; 95% CI, 0.03-0.52; P =0.004) compared to HFrEF patients. Nonproportional outcome of composite end points (i.e., combination of all-cause mortality, HF hospitalization and repeat revascularization) between HFiEF and HFrEF groups was detected by using the Schoenfeld residuals. By using flexible parametric model, patients with HFiEF had similar risk of composite end points in the first 5 years (HR, 0.84; 95% CI, 0.53-1.31; P =0.433), but significantly lower risk during years 6 to 11(HR, 0.12; 95% CI, 0.03-0.56; P =0.007). In the HFiEF group, the risk of death were age (HR, 1.07; 95% CI, 1.02-1.12; P =0.008), current smoking (HR, 5.62; 95% CI, 1.93-18.18; P =0.002) and history of myocardial infarction (HR, 2.89; 95% CI, 1.05-7.93; P =0.039). By contrast, in the HFrEF group, the risk factors were moderate or severe mitral regurgitation (HR, 1.81; 95% CI, 1.06-3.11; P =0.031) and estimated glomerular filtration rate (HR, 0.97; 95% CI, 0.95-0.99; P=0.014). Conclusions: Patients with ischemic HFiEF have distinct clinical characteristics and favorable outcomes. Our data support HFiEF as a distinct HF category in ischemic HF patients after revascularization therapy.