Manemann, Sheila M.

Background: Contemporary data on trends in survival after incident atrial fibrillation (AF) are limited. Methods: Residents of a 27-county area in southern Minnesota and western Wisconsin with new-onset atrial fibrillation or atrial flutter between 2013 and 2023 were identified. An electronic algorithm requiring 1 inpatient code or 2 outpatient codes separated by >7 days but within 1 year was used to define AF. Patients were followed for all-cause mortality through March 31, 2025. Standardized mortality ratios of observed versus expected survival were calculated, and time trends in survival were examined using Cox regression adjusting for demographics, cardiovascular risk factors, comorbid conditions, and area-level measures of socioeconomic status and rurality. Results: There were 44,930 individuals identified with incident AF from 2013-2023 (56.4% male; mean (SD) age 72.8 (13.5) years; 95.8% were non-Hispanic white). Within the first 90 days, the risk of all-cause mortality was greatly elevated compared with individuals of a similar age and sex distribution in the general population (standardized mortality ratios 12.6, 95% CI, 12.1-13.0 and 3.8, 95% CI, 3.6-4.0 for the first 30 days and 31 to 90 days after diagnosis, respectively). This excess risk was observed for all age groups but was greatest in the youngest age group (standardized mortality ratios among 18-64 year olds for the first 30 days: 49.9, 95% CI, 44.6-55.7 and 31 to 90 days: 11.1, 95% CI: 9.4-13.1; Figure ). In the youngest age group, the excess risk persists through 5 years post AF, however, in the oldest age group the excess risk only persisted through 1 year. Survival within the first 90 days improved over the study period (adjusted hazard ratio [aHR], 0.88; 95% CI, 0.79-0.99 for 2023 vs 2013); likewise, among 90-day survivors, survival improved between 2013 and 2023 (aHR,0.82; 95% CI, 0.76-0.88). These trends were driven by improvements in the younger age groups, particularly for death among 90-day survivors (aHR for 18-64 years, 0.66, 95% CI, 0.52-0.83; 65-74 years: 0.77, 95% CI: 0.65-0.90; 75-84 years, 0.84, 95% CI: 0.75-0.95; 85+ years, 0.99, 95% CI: 0.98-1.01). Conclusion: In a Midwestern community, the excess risk of death is high within the first 90 days after incident AF. Furthermore, survival after AF has improved between 2013 and 2023, with the improvement greatest in younger age groups.

Background Multimorbidity is common in patients with atrial fibrillation (AF); however, the impact of the number and type of comorbid conditions on outcomes remains uncertain. Methods This cohort study included patients with new‐onset AF from a Midwest region between 2013 and 2017. Eighteen chronic conditions at the time of AF were classified into groups: cardiometabolic, other somatic, and mental health. Cox regression determined associations between the number of each condition type with death, ischemic stroke/transient ischemic attack, and congestive heart failure, stratified by age. Results Among 16 509 patients with AF (mean age, 74 years; 43% women), the mean number of cardiometabolic, other somatic, and mental health conditions was 2.7, 1.4, and 0.5, respectively. The number and type of conditions had a varying impact on outcomes and differed by age. A higher number of cardiometabolic conditions were associated with increased risk of death within 90 days only in people aged ≥85 years (≥4 conditions versus 0: hazard ratio [HR], 1.74 [95% CI, 1.05–2.87]), whereas for death after 90 days, associations were strongest in the youngest age group (<65 years: HR, 1.83 [95% CI, 1.35–2.46]; 65–74 years: HR, 1.34 [95% CI, 1.04–1.73]; 75–84 years: HR, 1.32 [95% CI, 1.09–1.60]; ≥85 years: HR, 1.14 [95% CI, 0.96–1.35]). Associations with outcomes were generally strongest in the youngest age group and attenuated with older age for higher number of other somatic conditions, whereas the pattern was less consistent for mental health conditions. Conclusions Along with cardiometabolic‐related conditions, other somatic and mental health conditions are important predictors of outcomes in AF, with effects differing by age, and should be considered when caring for these patients.

Abstract Background Computational phenotyping from electronic health records (EHRs) is essential for clinical research, decision support, and quality/population health assessment, but the proliferation of algorithms for the same conditions makes it difficult to identify which algorithm is most appropriate for reuse. Objective To develop a framework for assessing phenotyping algorithm fitness for purpose and reuse. Fitness for Purpose Phenotyping algorithms are fit for purpose when they identify the intended population with performance characteristics appropriate for the intended application. Fitness for Reuse Phenotyping algorithms are fit for reuse when the algorithm is implementable and generalizable—that is, it identifies the same intended population with similar performance characteristics when applied to a new setting. Conclusions The PhenoFit framework provides a structured approach to evaluate and adapt phenotyping algorithms for new contexts increasing efficiency and consistency of identifying patient populations from EHRs.

Background: Atrial fibrillation (AF) is the most common sustained arrhythmia with prevalence increasing over the last decade. However, data on recent temporal trends in incidence of AF have been inconsistent, with some reports of increasing trends over time and others indicating no change in incidence over time. Therefore, we aimed to determine how the incidence of AF has changed over the last decade in a community in the Midwest USA. Methods: Between 2013 and 2023, adults (aged 18 and older) with incident AF were identified in a 27-county region in the Midwest. AF was identified using diagnostic codes by employing an electronic algorithm requiring 1 inpatient code or 2 outpatient codes separated by >7 days but within 1 year. For total rates, age - and sex-adjusted incidence rates were standardized to the 2010 US total population. Poisson regression was used to calculate the incidence rate ratio of AF for 2023 vs 2013. Results: We identified 44,930 individuals with incident AF from 2013 to 2023. Of these, 56.4% were male, the mean (SD) age was 72.8 (13.5) years, and 95.8% were non-Hispanic white. The age-and sex-adjusted incidence rates (per 1,000) and 95% confidence intervals standardized to the 2010 US total population were 5.24 (5.07-5.41) in 2013, 5.73 (5.56-5.91) in 2018, and 5.44 (5.28-5.61) in 2023. Although a drop in incidence was observed corresponding to the COVID-19 pandemic, a small increase in the overall incidence of AF was observed over time. The overall incidence rate ratio (95% CI) for 2023 vs 2013 was 1.07 (1.02-1.11), P=0.003. The incidence rates were higher in men compared to women and increased with older age ( Figure 1 ). However, differences in AF incidence by sex were greatest in the youngest age group and attenuated with older age (incidence rate ratio (95% CI): 2.18 (2.10-2.28) for ages 18-64 years, 1.76 (1.70-1.83) for ages 65-74 years, 1.38 (1.33-1.42) for ages 75-84 years, and 1.23 (1.18-1.28) for ages 85 years and older). Conclusion: In this 27-county region in the Midwest, although small in magnitude, a statistically significant increase in AF incidence was observed over time from 2013 to 2023. Furthermore, the incidence of AF was higher in men compared with women, especially in younger age groups.

Background Multimorbidity is common in patients with atrial fibrillation (AF), yet comorbidity patterns are not well documented. Methods The prevalence of 18 chronic conditions (6 cardiometabolic, 7 other somatic, 5 mental health) was obtained in patients with new-onset AF from 2013-2017 from a 27-county region and controls matched 1:1 on age, sex, and county of residence. For AF patients and controls separately, clustering of conditions and co-occurrence beyond chance was estimated (using the asymmetric Somers’ D statistic), overall and for ages <65, 65-74, 75-84, and ≥85 years. Results Among 16,509 patients with AF (median age 76 years, 57% men), few (4%) did not have any of the 18 chronic conditions, whereas nearly one-quarter of controls (23%) did not have any chronic conditions. Clustering of cardiometabolic conditions was common in both AF patients and controls, but clustering of other somatic conditions was more common in AF. Although the prevalence of most condition pairs was higher in AF patients, controls had a larger number of condition pairs occurring together beyond chance. In persons aged <65 years, AF patients more frequently exhibited concordance of condition pairs that included either pairs of somatic conditions or a combination of conditions from different condition groups. In persons aged 65-74 years, AF patients more frequently had pairs of other somatic conditions. Conclusion Patterns of co-existing conditions differed between patients with AF and controls, particularly in younger ages. A better understanding of the clinical consequences of multimorbidity in AF patients, including those diagnosed at younger ages, is needed.

Abstract Background Multimorbidity and functional limitation are associated with poor outcomes in heart failure (HF). However, the individual and combined effect of these on health‐related quality of life in patients with HF is not well understood. Methods Patients aged ≥30 years with two or more HF diagnostic codes and one or more HF‐related prescription drugs from four U.S. institutions were mailed a survey to measure patient‐centric factors including functional status (activities of daily living [ADLs]) and health‐related quality of life (PROMIS‐29 Health Profile). Patients with HF from January 1, 2013 to February 1, 2018 were included. Multimorbidity was defined as ≥2 non‐cardiovascular comorbidities; functional limitation as any limitation in at least one of eight ADLs. Patients were categorized into four groups by multimorbidity (Yes/No) and functional limitation (Yes/No). We dichotomized the PROMIS‐29 sub‐scale scores at the median and calculated odd ratios for the four multimorbidity/functional limitation groups. Results A total of 3330 patients with HF returned the survey (response rate 31%); 3020 completed the questions of interest and were retained. Among these patients (45% female; mean age 73 [standard deviation: 12] years), 29% had neither multimorbidity nor functional limitation, 24% had multimorbidity only, 22% had functional limitation only, and 25% had both. After adjustment, having functional limitation only was associated with higher anxiety (odds ratio [OR]: 3.44, 95% confidence interval [CI]: 2.66–4.45), depression (OR: 3.11, 95% CI: 2.39–4.06), and fatigue (OR: 4.19, 95% CI: 3.25–5.40); worse sleep (OR: 2.14, 95% CI: 1.69–2.72) and pain (OR: 6.73, 95% CI: 5.15–8.78); and greater difficulty with social activities (OR: 9.40, 95% CI: 7.19–12.28) compared with having neither. Results were similar for having both multimorbidity and functional limitation. Conclusion Patients with only functional limitation have similar poor health‐related quality of life scores as those with both multimorbidity and functional limitation, underscoring the important role that physical functioning plays in the well‐being of patients with HF.

Background Clinical risk scores are used to identify those at high risk of atherosclerotic cardiovascular disease (ASCVD). Despite preventative efforts, residual risk remains for many individuals. Very low‐density lipoprotein cholesterol (VLDL‐C) and lipid discordance could be contributors to the residual risk of ASCVD. Methods and Results Cardiovascular disease–free residents, aged ≥40 years, living in Olmsted County, Minnesota, were identified through the Rochester Epidemiology Project. Low‐density lipoprotein cholesterol (LDL‐C) and VLDL‐C were estimated from clinically ordered lipid panels using the Sampson equation. Participants were categorized into concordant and discordant lipid pairings based on clinical cut points. Rates of incident ASCVD, including percutaneous coronary intervention, coronary artery bypass grafting, stroke, or myocardial infarction, were calculated during follow‐up. The association of LDL‐C and VLDL‐C with ASCVD was assessed using Cox proportional hazards regression. Interaction between LDL‐C and VLDL‐C was assessed. The study population (n=39 098) was primarily White race (94%) and female sex (57%), with a mean age of 54 years. VLDL‐C (per 10‐mg/dL increase) was significantly associated with an increased risk of incident ASCVD (hazard ratio, 1.07 [95% CI, 1.05–1.09]; P <0.001]) after adjustment for traditional risk factors. The interaction between LDL‐C and VLDL‐C was not statistically significant ( P =0.11). Discordant individuals with high VLDL‐C and low LDL‐C experienced the highest rate of incident ASCVD events, 16.9 per 1000 person‐years, during follow‐up. Conclusions VLDL‐C and lipid discordance are associated with a greater risk of ASCVD and can be estimated from clinically ordered lipid panels to improve ASCVD risk assessment.

Background Heart failure (HF) is a complex disease that contributes to a high number of hospitalizations, deaths, and economic health care costs each year. However, among patients with HF, there is a lack of awareness of their HF diagnosis that has not been fully examined. Methods and Results Residents from 3 counties of southeast Minnesota with a first‐ever International Classification of Diseases, Ninth Revision ( ICD‐9 ) code 428 or Tenth Revision ( ICD‐10 ) code I50 between January 1, 2013 and March 31, 2016 (N=2461) were prospectively surveyed to measure HF self‐awareness. A total of 1114 patients returned the survey (response rate, 45%), and 787 had validated HF upon medical record review. Among these 787 patients with HF (mean age, 76 years; 53% men), 37% (n=293) were aware of their HF diagnosis. After adjustment, being a woman (odds ratio [OR], 1.56 [95% CI, 1.10–2.22]), having HF with reduced ejection fraction (OR, 1.58 [95% CI, 1.13–2.22]), attending the HF clinic (OR, 4.07 [95% CI, 2.25–7.36]), and having coronary artery disease (OR, 1.65 [95% CI, 1.16–2.37]) were all associated with increased awareness of an HF diagnosis. Conversely, having diabetes was associated with decreased awareness of an HF diagnosis (adjusted OR, 0.69 [95% CI, 0.50–0.95]). Conclusions Awareness of an HF diagnosis is low in a community population of patients with HF. Strategies to improve patient awareness of their diagnosis should be implemented to improve self‐care behaviors and outcomes in patients with HF.

Background: Multimorbidity is common in atrial fibrillation (AF), particularly among older patients who generally have multiple cardiovascular and non-cardiovascular chronic conditions. However, less is known about the accumulation of chronic conditions in younger patients with AF. Methods: Patients aged <65 with incident AF from 2013-2019 from a 27-county region in the Midwest (N=4907) were matched 1:1 on age (±5 years) and sex to referents from the same community. The index date for the matched pair was the date of first diagnosis of AF. Diagnosis of 20 other chronic conditions was ascertained from 3 years prior to index through 12/31/2022. Mean cumulative function curves were used to visualize the accumulation of conditions; Andersen-Gill models estimated associations between AF/referent status and accumulation of conditions. Results: The mean age was 55 years for both patients with AF and matched referents; 71% were male. In the 3 years leading up to AF diagnosis, only 4% of patients with AF had no additional chronic conditions and 69% had ≥3 chronic conditions. In contrast, 35% of referents had 0 and 32% had ≥3 chronic conditions. The accumulation of chronic conditions was accelerated in patients with AF compared to referents prior to AF diagnosis/index (Figure). After index, patients with AF had increased accumulation of chronic conditions (HR (95% CI): 1.93 (1.71-2.20), 1.82 (1.62-2.05), 1.68 (1.55-1.83), and 1.53 (1.44-1.64) for ages <50, 50-54, 55-59, and 60-64 years, respectively). After 1 year, the differences were attenuated in all age groups with no difference observed between patients with AF and referents in the oldest age group (1.26 (1.08-1.46), 1.28 (1.11-1.47), 1.13 (1.02-1.24), and 1.05 (0.97-1.13) for ages <50, 50-54, 55-59, and 60-64 years). Conclusion: Patients <65 years with AF have an increased accumulation of chronic conditions compared to those without AF, in particular prior to their AF diagnosis and within the first year after AF diagnosis.

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.