Video telehealth visits (VTV) have emerged as a critical tool for oncology care delivery, with potential to address longstanding access disparities. We examined the association between broadband internet availability, individual digital literacy factors, and VTV utilization among patients with cancer. In a retrospective cohort of 13,897 patients across a multi-site practice, VTV utilization was significantly lower in areas with ≤1 internet service provider (ISP) offering download speeds ≥25 Mbps (p = 0.0009). Validation in a regional cohort (n = 6665) confirmed lower VTV utilization in low-broadband areas. Among 1134 surveyed patients, higher digital literacy was the strongest predictor of VTV use (OR 2.5; p < 0.001), even where broadband was limited. This study demonstrates that while both broadband availability and digital literacy independently influence VTV utilization, individual digital skills can partially offset structural limitations, underscoring the need for concurrent investment in broadband infrastructure and targeted digital literacy initiatives to advance access to care.

Objective:To determine whether a low ejection fraction artificial intelligence electrocardiogram (AI-ECG) algorithm predicts incident heart failure with reduced ejection fraction (HFrEF) in patients with atrial fibrillation (AF) independently of known HF risk factors. Patients and Methods:A validated AI-ECG algorithm for low ejection fraction (EF) detection was applied to a cohort of patients aged 18 years or older from Olmsted County, Minnesota, with incident AF between January 1, 2000, and December 31, 2014. Risk of HFrEF by tertiles of AI-ECG score were calculated, and model performance was assessed using C-statistic estimates derived from Cox proportional hazard regression models. Results:Among 2569 patients with AF, 248 (9.7%) developed incident HFrEF (EF<50%) over an average follow-up of 7.0 years. Patients in the highest risk AI-ECG tertile had an increased risk of developing HFrEF in comparison with those in the lowest risk tertile after adjusting for common HF risk factors (hazard ratio [HR], 3.50; 95% CI, 2.51-4.87). The C-statistic was significantly higher in a model combining the AI-ECG + HF risk factors (0.76; 95% CI, 0.73-0.80) than a model with HF risk factors only (0.67; 95% CI, 0.63-0.71; P<.0001) and a model with the AI-ECG only (0.73; 95% CI, 0.69-0.76; P=.019). Similar results were observed for HF with EF of 35% or less (highest vs lowest risk AI-ECG tertile: HR, 5.20; 95% CI, 3.03-8.91). Conclusion:Incorporation of the AI-ECG algorithm into routine clinical care may provide enhanced ability to identify patients with AF at risk of developing HFrEF with predictive performance that is superior to current clinical models.

Abstract Introduction: Lung cancer rates differ across rural and urban areas, but few studies have evaluated rural-urban differences across the risk-screening continuum. We examined smoking, cessation interventions, and lung cancer screening patterns across the rural-urban continuum in a geographically defined area over 10 years. Materials and Methods: A retrospective cohort study (2014-2023) was conducted using clinical data from the Rochester Epidemiology Project, derived from healthcare encounters in a 27-county region of the midwestern United States. Patients ages 40-80 were included. We used Rural-Urban Commuting Area codes to assign residence as urban, rural, or highly rural. We examined yearly smoking prevalence, cessation intervention (pharmacotherapy, counseling), and low-dose computed tomography (LDCT) lung cancer screening. Results: Over the 10-year study period, the sample size ranged from 305,530 to 340,411 people annually with 36-38% urban, 56-57% rural, and 6-7% highly rural. Current smoking prevalence declined from 14 to 12% over the 10-year period (range=12-16%; p=0.06) and was consistently lower in urban areas (range=10-14%) than rural (range=12-17.0%) and highly rural areas (range=12-18%; p=<0.001). Among individuals who currently smoked with no history of lung cancer, yearly smoking cessation intervention ranged from 16% to 23%, increasing over the 10-year period (p=<0.001). Cessation medication (varenicline, bupropion, or nicotine replacement) prescription consistently increased overall over time but remained higher in urban than rural and highly rural areas (p=<0.001). Cessation counseling rates also increased overall over time and were similar between urban and rural areas and lower in highly rural areas (p=<0.001). Among people who had ever-smoked aged 50-80 years with no diagnosis of lung cancer, LDCT screening increased from 0.0% to 3.1% over the study period. Increases were higher in urban areas (0.0-3.6%) and similar in rural and highly rural areas (0.0-2.8% and 0.0-2.9%, respectively; p=<0.001). Among people who currently smoked, LDCT screening also increased over the study period (0.0-6.4%), with higher increases in urban (0.1-8.0%) than rural (0.0-5.7%) or highly rural (0.1-6.1%) areas (p=<0.001). After the new screening guidelines in 2021, rates in all areas increased year-on-year. Conclusion: In this cohort, we found that smoking prevalence, cessation interventions, and lung cancer screening improved from 2014-2023 across the rural-urban continuum. Unfortunately, smoking prevalence remained higher and cessation interventions and screening rates lower in rural compared to urban areas. The persistent patterns underscore the need for strategies to overcome barriers for rural residents and improve lung cancer prevention and early detection in all areas. Citation Format: Brianna Tranby, Paul A. Decker, Jiang Ruoxiang, David Midthun, Lori C. Sakoda, Melinda C. Aldrich, Debra Friedman, Adoma Manful, Oindrila Bhattacharyya, Christi Patten, Chyke A. Doubeni. Patterns of tobacco use, cessation interventions, and lung cancer screening in rural vs. urban areas over time; a 10-year retrospective cohort study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 5045.

Objective: Over 3 million Americans have heart failure with preserved ejection fraction (HFpEF). Effective guideline directed medical treatments (GDMT) are increasingly available but patients with HFpEF are likely under-detected (particularly women) and not receiving GDMT. Limited data are available informing the extent of HFpEF under-detection in the community. Hypothesis: Among patients with undifferentiated dyspnea and a high risk for unrecognized HFpEF, fewer than 50% will receive a HFpEF diagnosis during two years of follow-up; underrecognition will be greater among women. Methods: We identified 22,134 patients aged ≥30 in the Rochester Epidemiology Project (a medical records linkage system comprising a 27-county region of southeastern Minnesota and western Wisconsin) with evidence of unexplained dyspnea (≥2 ICD codes for dyspnea separated by >1 day) without prior documented etiology including HF or lung disease, during the time period 2018-2022. We calculated the recently validated HFpEF-Age-BMI-Atrial Fibrillation (HFpEF-ABA) score to estimate the probability of underlying HFpEF. Cox proportional hazards models regressed risk for incident HFpEF diagnosis on HFpEF-ABA score (dichotomized as ≥80% vs. <80%) with multivariable adjustment for sex, race and ethnicity. HRs(95%CIs) are presented. Results: Patients were 59±16 yrs, 50.8% women, 88.6% white, 3.1% Black, 3.7% other race, 4.6% Hispanic. Atrial fibrillation prevalence was 9.4% and mean BMI=31.4±7.8. Prevalence of HFpEF-ABA≥80% was 22.4%(n=4,958): 22.7% among women and 22.1% among men. The cumulative incidence of clinically-recognized HFpEF diagnosis at two years follow-up was 9.7% among women and 12.8% among men (log-rank p<0.01). HFpEF incidence was elevated with high HFpEF-ABA scores(≥80%): HR(95%CI) among women=3.72 (3.35, 4.14), and men=3.42 (3.11, 3.76). Among those with clinically diagnosed HFpEF during follow-up, mean time from date of first dyspnea ICD code to HFpEF diagnosis was 1.26 years overall: 1.31 and 1.21 years in women and men, respectively (p=0.05). Conclusions: One in five patients with undifferentiated dyspnea have probable undiagnosed HFpEF, and of that group, only 30% receive a clinical diagnosis, requiring an average of over 1 year to be recognized. The rate of diagnosis is even lower among women than men. Further study is warranted to implement evidence-based algorithms supporting detection of undiagnosed HFpEF in the community, especially in women.

This retrospective study estimated the birth prevalence of congenital cytomegalovirus (cCMV) in southeastern Minnesota, US. The birth prevalence was about 3 times and about 18 times greater following implementation of hearing-targeted and universal newborn cCMV screening, respectively. Most infants born with cCMV will not be identified in the absence of systematic newborn screening programs.

PURPOSE:The Collaborative care model (CoCM) is the leading model for integrating behavioral health into primary care for patients with major depressive disorder (MDD). However, CoCM requires engagement and ongoing participation. We aimed to assess whether two area-based measures, the area-deprivation index (ADI) and rurality, were associated with enrollment, participation, and outcomes with CoCM. METHODS:This was an observational analysis of Mayo Clinic patients eligible for CoCM: adults aged ≥18 years, empaneled in primary care, and with a PHQ-9 of ≥10. We operationalized ADI as quintiles with Q1 being least deprived and Q5 being most deprived and rurality using RUCA codes with two categories: urban and rural. We evaluated enrollment in CoCM, drop out defined by leaving the program early, the count and type of contacts with the care coordinator, and clinical improvement measured using the PHQ-9. FINDINGS:We identified 54,030 individuals with 16,532 (30.6%) residing in rural areas and 11,122 (20.6%) residing in the most deprived ADI quintile (Q5). Living in a rural area was associated with lower enrollment in CoCM (-2.3 percentage points [95% confidence interval (CI): -2.5, 2.2]), longer length in CoCM (18.6 days [95% CI: 5.7, 31.5]), more contacts with the care coordinator (1.1 contacts [95% CI: 0.2, 2.0]), and worse response and remission. In contrast, ADI Q5 was only associated with worse response and remission. CONCLUSIONS:Rurality was associated with lower enrollment, greater engagement, and worse clinical outcomes. More work may be needed to address enrollment barriers for individuals living in rural areas to improve clinical outcomes.

BackgroundDuring the COVID-19 outbreak, video appointments became a popular method for health care delivery, particularly in the early stages of the pandemic. Although Mayo Clinic aimed to reduce face-to-face (F2F) appointments to prevent the spread of the virus, some patients continued seeing their health care providers in person. In the later stages of the pandemic, many patients became comfortable with video appointments, even if they were initially hesitant. However, a subset of patients continued to avoid video appointments. It is not yet clear what sociodemographic factors may be associated with this group of patients. ObjectiveThis cross-sectional study aimed to examine demographic and social determinant of health (SDoH) factors associated with persistent nonusers of video appointments among a sample of patients within a multistate health care organization. We also explored patient beliefs about the use of video for health care appointments. MethodsWe conducted a 1-time cross-sectional paper survey, mailed between July and December 2022, of patients matching the eligibility criteria: (1) aged ≥18 years as of April 2020, (2) Mayo Clinic Midwest, Florida, or Arizona patient, (3) did not use video appointment services during April-December 2020 but attended F2F appointments in the departments of primary care and psychiatry/psychology. The survey asked patients, “Have you ever had a video appointment with a healthcare provider?” “Yes” respondents were defined as “users” (adapted to video appointments), and “no” respondents were defined as “persistent nonusers” of video appointments. We analyzed demographics, SDoH, and patient beliefs toward video appointments in 2 groups: persistent nonusers of video appointments and users. We used chi-square and 2-tailed t tests for analysis. ResultsOur findings indicate that patients who were older, lived in rural areas, sought care at Mayo Clinic Midwest, and did not have access to the patient portal system were likely to be persistent nonusers of video appointments. Only 1 SDoH factor (not having a disability, handicap, or chronic disease) was associated with persistent nonuse of video appointments. Persistent nonusers of video appointments held personal beliefs such as discomfort with video communication, difficulty interpreting nonverbal cues, and personal preference for F2F appointments over video. ConclusionsOur study identified demographic (older age and rural residence), sociodemographic factors (not having a disability, handicap, or chronic disease), and personal beliefs associated with patients’ decisions to choose between video versus F2F appointments for health care delivery. Health care institutions should assess patients’ negative attitudes toward technology prior to introducing them to digital health care services. Failing to do so may result in its restricted usage, negative patient experience, and wasted resources. For patients who hold negative beliefs about technology but are willing to learn, a “digital health coordinator” could be assigned to assist with various digital health solutions.

OBJECTIVES:The aims of the study were to identify conditions diagnosed in at least 10% of midlife women living in the US upper midwest and to assess prevalence by age, race, ethnicity, and sociodemographic status. METHODS:The Rochester Epidemiology Project was used to conduct a cross-sectional prevalence study of 86,946 women between 40 and 59 years residing in a 27-county region of the United States on January 1, 2020. Diagnostic billing codes were extracted and grouped into broader condition categories using the Clinical Classification System Refined. The prevalence of 424 conditions was calculated by age, race, ethnicity, and area deprivation index quartiles. Logistic regression was used to examine associations between participant characteristics and conditions that affected 10% or more of the study population. RESULTS:Twenty-eight conditions affected ≥10% of women, and eight conditions increased by ≥45% between the ages of 40 and 59 (disorders of lipid metabolism, hypertension, sleep/wake disorders, thyroid disorders, esophageal disorders, osteoarthritis, tendon and synovial disorders, and menopausal disorders; all test for trend P < 0.01). Black women had a significantly higher prevalence of hypertension and esophageal disorders at all ages (adjusted P values <0.05). Women living in more deprived areas had a significantly higher prevalence of hyperlipidemia, hypertension, sleep/wake disorders, and esophageal disorders (adjusted P values <0.05). Women living in less deprived areas had a significantly higher prevalence of thyroid disorders at age 40 to 44 and menopausal disorders at ages 50 to 59 (adjusted P values <0.05). CONCLUSIONS:These data suggest that additional attention should focus on Black women and women with a lower socioeconomic status to ensure that common midlife conditions are diagnosed and treated.

INTRODUCTION:Limited research has examined how technology and digital literacy may affect patients' use of video visits. This study explored the relationship of demographic factors and patient-reported confidence in digital literacy skills to access to video visits among patients who never used them during the COVID-19 pandemic. METHODS:Using existing survey data, the current study examined data from respondents who did not engage in video appointments but instead attended face-to-face appointments between April and December 2020 for nonemergent health concerns. A multivariable logistic regression model was used to investigate whether demographic and social determinants of health factors, context of care (primary care or psychiatry/psychology), and digital literacy confidence were associated with video visit engagement. Collinearity was assessed using the variance inflation factor. RESULTS:This study found that living in rural areas and having a self-reported lack of confidence in logging video appointments using the Mayo Clinic patient portal were associated with persistent nonuse of video appointments in a cohort of patients who did not use video visits at this institution during the early part of the COVID-19 pandemic. DISCUSSION:The research findings reported herein reveal that individuals living in rural areas and those who lack confidence in logging into patient portals to access video visits tend to persistently avoid using video appointments. More investment is needed at the federal and corporate levels to improve digital connectivity. Digital navigators and community involvement can promote digital adoption. CONCLUSION:To encourage digital competency in rural communities, it is important to implement support strategies through community stakeholders and other resources.

National or statewide estimates of excess deaths have limited value to understanding the impact of the COVID-19 pandemic regionally. We assessed excess deaths in a 9-county geographically defined population that had low rates of COVID-19 and widescale availability of testing early in the pandemic, well-annotated clinical data, and coverage by 2 medical examiner’s offices. We compared mortality rates (MRs) per 100,000 person-years in 2020 and 2021 with those in the 2019 reference period and MR ratios (MRRs). In 2020 and 2021, 177 and 219 deaths, respectively, were attributed to COVID-19 (MR = 52 and 66 per 100,000 person-years, respectively). COVID-19 MRs were highest in males, older persons, those living in rural areas, and those with 7 or more chronic conditions. Compared with 2019, we observed a 10% excess death rate in 2020 (MRR = 1.10 [95% CI, 1.04 to 1.15]), with excess deaths in females, older adults, and those with 7 or more chronic conditions. In contrast, we did not observe excess deaths overall in 2021 compared with 2019 (MRR = 1.04 [95% CI, 0.99 to 1.10]). However, those aged 18 to 39 years (MRR = 1.36 [95% CI, 1.03 to 1.80) and those with 0 or 1 chronic condition (MRR = 1.28 [95% CI, 1.05 to 1.56]) or 7 or more chronic conditions (MRR = 1.09 [95% CI, 1.03 to 1.15]) had increased mortality compared with 2019. This work highlights the value of leveraging regional populations that experienced a similar pandemic wave timeline, mitigation strategies, testing availability, and data quality.

Beyond medication count, complex medication regimens may be especially risky and burdensome for people with dementia or mild cognitive impairment (MCI) and their caregivers. The Medication Regimen Complexity Index (MRCI), which incorporates dosage form, frequency, and additional directions,1, 2 may be a useful tool to identify people with dementia or MCI who would benefit from deprescribing. This study sought to automate MRCI calculation in a large, real-world database of people with MCI or dementia and to examine contributions of specific MRCI components to overall complexity. This was a cross-sectional study using existing medical record data from seven Minnesota counties in the Rochester Epidemiology Project (REP) medical records-linkage sytem,3 which captures information from healthcare provided to 90% of the residents of the region.4 We searched REP electronic indexes to identify residents aged ≥65 with incident MCI or dementia from January 1, 2015 through December 31, 2017 (Supplementary Table S3). We searched the REP for outpatient medication prescriptions and self-reported medications for individuals in the 30 days before and after their dementia diagnosis. The MRCI is a sum of three weighted subscores: form/route (Part A), frequency (Part B), and additional instructions (Part C).1 Information on form, route, and frequency was obtained from electronic prescription information. We examined the free-text and frequency fields for text patterns corresponding to Part C (e.g., "crush," "meal," and "bedtime").5 Decision rules were created for cases not clearly addressed in the MRCI instructions (Supplement). Two geriatricians (ARG, SN) refined the algorithm by searching for text patterns that had not been accounted for in earlier steps and to adjudicate discrepancies. We calculated MRCI scores first as a sum of the weighted scores for Part A and Part B only, and second as a sum of the weighted scores for all three parts.6 Higher scores indicated greater complexity. Patient characteristics were summarized and tested using chi-square tests. MRCI scores were summarized with median (interquartile range [IQR]); differences were tested using the Kruskal–Wallis test. Correlations were summarized with Spearman correlation coefficients. The cohort consisted of 3976 people and 29,059 linked medication records (Supplementary Tables S1 and S2). Among people with ≥1 medication, two central nervous system-active medications—opioids and antidepressants—together comprised 8% of medication prescriptions (Supplementary Table S2). Median MRCI scores across demographic/clinical characteristics are in Table 1. The median MRCI score was 12 (IQR: 5–25) calculated using Parts A and B and 14 (IQR: 6–29) calculated using all three parts. The biggest contributor to MRCI score was dosing frequency (Part B). Medication count was associated with MRCI score (Spearman r = 0.91, p < 0.01; Supplementary Figure S2). There was wide variation in MRCI scores among patients with the same number of medications (Figure 1). Frequency was the biggest contributor to medication regimen complexity in this cohort of patients with MCI or dementia, similar to studies in non-dementia populations.5 MRCI scores varied widely among patients with the same number of medications and may more accurately capture patients' and caregivers' lived experience than medication count. Complex medication regimens may increase the risk of poor health outcomes.7 The steps we undertook to calculate the MRCI could be used to identify people with MCI and dementia who may be most likely to benefit from deprescribing. Opioids and antidepressants were among the top 10 most common medication classes in our cohort. Reducing the use of central nervous system-active medications may be important to reducing complexity for this population, as use of such medications is common among people living with dementia and associated with numerous adverse health outcomes.8 To implement the MRCI for pragmatic deprescribing trials, it would need to be automated for use within electronic medical records in real time. Part C was difficult to automate because of the wide variety of ways in which special administration instructions can be expressed, requiring coder discretion. We found that calculating the MRCI score using only Parts A and B was comparable to incorporating the Part C subscore in terms of identifying patients with high versus low complexity.6 The MRCI may prove to be more useful than number of medications for identifying patients with MCI or dementia who are most likely to benefit from deprescribing interventions. Addressing high medication regimen complexity—for example, by eliminating medications that are taken multiple times per day or have complicated administration instructions—could reduce self-care demands, prevent institutionalization and lessen caregiver strain.9 A limitation of this research is that misclassification of medication use is possible. In conclusion, this study characterized medication regimen complexity among people with MCI or dementia. Future studies should assess the impact of reducing MRCI scores on clinical outcomes, including adverse events and patient- or caregiver-reported measures of treatment burden. Conception and design of study (all), analysis (Ruoxiang Jiang and Susan A. Weston), and interpretation of data (all), drafting manuscript (Ariel R. Green), revising manuscript (all), and final approval of version to be published (all). There are no relevant conflicts of interest. The funding sources had no role in the study concept and design, methods, subject recruitment, data collection, analysis, and preparation of paper. This project was supported by a grant from the National Institute on Aging (NIA AG 052425). In addition, this study used the resources of the Rochester Epidemiology Project (REP) medical records-linkage system, which is supported by the NIA (AG 058738), the Mayo Clinic Research Committee, and fees paid annually by REP users. Dr. Ariel Green acknowledges funding from the NIA (K23 AG054742; R01 AG077011) and NIA Impact Collaboratory (U54AG063546). Dr. Stephanie Nothelle acknowledges funding from the Grants for Early Medical/Surgical Specialists Transitioning to Aging Research (GEMSSTAR) (R03AG060170), her K23 (K23AG072037), both from the National Institute on Aging. The content of this article is solely the responsibility of the authors and does not represent the official views of the National Institutes of Health (NIH) or the Mayo Clinic. Supplementary Table S1. Characteristics of MCI/dementia patients with no medications vs ≥1 medication. Supplementary Table S2. Clinical characteristics of patients with MCI/dementia and ≥1 medication. Supplementary Table S3. ICD-9/10 codes used to identify patients with MCI and dementia decision rules for MRCI scoring. Supplementary Figure S1. Study flow diagram. Supplementary Figure S2. Association of medication count with MRCI score. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Postoperative atrial fibrillation (POAF) after noncardiac surgery comprises around 13% of all new AF diagnoses in the community and has been associated with increased risk of subsequent stroke and transient ischemic attack compared to those without a history of AF.1-3 However, the management of POAF after non-cardiac surgery, including the indications and approaches to ambulatory rhythm monitoring and oral anticoagulation (OAC) for stroke prophylaxis, remains uncertain. Recent data have also demonstrated that AF tends to recur in about one third of patients with POAF within the first year of the index POAF episode4.

Low-density lipoprotein cholesterol (LDL-C) is associated with atherosclerotic cardiovascular disease (ASCVD). Friedewald, Sampson, and Martin-Hopkins equations are used to calculate LDL-C. This study compares the impact of switching between these equations in a large geographically defined population. Data for individuals who had a lipid panel ordered clinically between 2010 and 2019 were included. Comparisons were made across groups using the two-sample t-test or chi-square test as appropriate. Discordances between LDL measures based on clinically actionable thresholds were summarized using contingency tables. The cohort included 198,166 patients (mean age 54 years, 54

Objective:To compare the 1-year health care utilization and mortality in persons living with heart failure (HF) before and during the coronavirus disease 2019 (COVID-19) pandemic. Patients and Methods:Residents of a 9-county area in southeastern Minnesota aged 18 years or older with a HF diagnosis on January 1, 2019; January 1, 2020; and January 1, 2021, were identified and followed up for 1-year for vital status, emergency department (ED) visits, and hospitalizations. Results:We identified 5631 patients with HF (mean age, 76 years; 53% men) on January 1, 2019, 5996 patients (mean age, 76 years; 52% men) on January 1, 2020, and 6162 patients (mean age, 75 years; 54% men) on January 1, 2021. After adjustment for comorbidities and risk factors, patients with HF in 2020 and patients with HF in 2021 experienced similar risks of mortality compared with those in 2019. After adjustment, patients with HF in 2020 and 2021 were less likely to experience all-cause hospitalizations (2020: rate ratio [RR], 0.88; 95% CI, 0.81-0.95; 2021: RR, 0.90; 95% CI, 0.83-0.97) compared with patients in 2019. Patients with HF in 2020 were also less likely to experience ED visits (RR, 0.85; 95% CI, 0.80-0.92). Conclusion:In this large population-based study in southeastern Minnesota, we observed an approximately 10% decrease in hospitalizations among patients with HF in 2020 and 2021 and a 15% decrease in ED visits in 2020 compared with those in 2019. Despite the change in health care utilization, we found no difference in the 1-year mortality between patients with HF in 2020 and those in 2021 compared with those in 2019. It is unknown whether any longer-term consequences will be observed.

BACKGROUND: The Framingham Heart Study Dementia Risk Score (FDRS) was developed in a general population of older persons. It is unknown how the FDRS variables predict Alzheimer's disease and Alzheimer's disease-related dementias (AD/ADRD) in heart failure and atrial fibrillation populations. We aimed to evaluate the predictive ability of the FDRS variables in population-based cohorts of heart failure and atrial fibrillation and to determine whether the addition of other comorbidities and risk factors improves risk prediction for AD/ADRD.METHODS: Residents aged >= 50 years from 7 southeastern Minnesota counties with a first diagnosis of heart failure or atrial fibrillation between January 1, 2013, and December 31, 2017, were identified. Patients with AD/ADRD before or within 6 months after index atrial fibrillation or heart failure and patients who died within 6 months after index were excluded. For both cohorts, models were constructed to predict AD/ ADRD after index including the variables in the FDRS. Additional comorbidities and risk factors were added to the models. For all models, c-statistics using 5-fold cross-validation were calculated.RESULTS: Among 3052 patients with heart failure (mean age 75 years, 53% male), 626 developed AD/ ADRD; among 4107 patients with atrial fibrillation (mean age 74 years, 57% male), 736 developed AD/ ADRD. Among patients with heart failure, the FDRS variables predicted AD/ADRD with c-statistic = 0.69. Adding comorbidities and risk factors improved the c-statistic slightly to 0.70. The FDRS variables also performed well (c-statistic = 0.73) in patients with atrial fibrillation; adding comorbidities and risk factors slightly improved performance (c-statistic = 0.75). CONCLUSIONS: The variables from the FDRS predict AD/ADRD well in both heart failure and atrial fibrillation populations. The addition of comorbidities and risk factors only modestly improved prediction, indicating that the FDRS variables are appropriate to predict AD/ADRD in patients with heart failure and atrial fibrillation. (c) 2022 Elsevier Inc. All rights reserved. center dot The American Journal of Medicine (2023) 136:302-307

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 Postoperative atrial fibrillation (AF) after noncardiac surgery confers increased risks for ischemic stroke and transient ischemic attack (TIA). How outcomes for postoperative AF after noncardiac surgery compare with those for AF occurring outside of the operative setting is unknown. OBJECTIVE To compare the risks for ischemic stroke or TIA and other outcomes in patients with postoperative AF versus those with incident AF not associated with surgery. DESIGN Cohort study. SETTING Olmsted County, Minnesota. PARTICIPANTS Patients with incident AF between 2000 and 2013. MEASUREMENTS Patients were categorized as having AF occurring within 30 days of a noncardiac surgery (postoperative AF) or having AF unrelated to surgery (nonoperative AF). RESULTS Of 4231 patients with incident AF, 550 (13%) had postoperative AF as their first-ever documented AF presentation. Over a mean follow-up of 6.3 years, 486 patients had an ischemic stroke or TIA and 2462 had subsequent AF; a total of 2565 deaths occurred. The risk for stroke or TIA was similar between those with postoperative AF and nonoperative AF (absolute risk difference [ARD] at 5 years, 0.1% [95% CI, -2.9% to 3.1%]; hazard ratio [HR], 1.01 [CI, 0.77 to 1.32]). A lower risk for subsequent AF was seen for patients with postoperative AF (ARD at 5 years, -13.4% [CI, -17.8% to -9.0%]; HR, 0.68 [CI, 0.60 to 0.77]). Finally, no difference was seen for cardiovascular death or all-cause death between patients with postoperative AF and nonoperative AF. LIMITATION The population consisted predominantly of White patients; caution should be used when extrapolating the results to more racially diverse populations. CONCLUSION Postoperative AF after noncardiac surgery is associated with similar risk for thromboembolism compared with nonoperative AF. Our findings have potentially important implications for the early postsurgical and subsequent management of postoperative AF. PRIMARY FUNDING SOURCE National Institute on Aging.

PURPOSE:The Mayo-Baylor RIGHT 10K Study enabled preemptive, sequence-based pharmacogenomics (PGx)-driven drug prescribing practices in routine clinical care within a large cohort. We also generated the tools and resources necessary for clinical PGx implementation and identified challenges that need to be overcome. Furthermore, we measured the frequency of both common genetic variation for which clinical guidelines already exist and rare variation that could be detected by DNA sequencing, rather than genotyping.METHODS:Targeted oligonucleotide-capture sequencing of 77 pharmacogenes was performed using DNA from 10,077 consented Mayo Clinic Biobank volunteers. The resulting predicted drug response-related phenotypes for 13 genes, including CYP2D6 and HLA, affecting 21 drug-gene pairs, were deposited preemptively in the Mayo electronic health record.RESULTS:For the 13 pharmacogenes of interest, the genomes of 79% of participants carried clinically actionable variants in 3 or more genes, and DNA sequencing identified an average of 3.3 additional conservatively predicted deleterious variants that would not have been evident using genotyping.CONCLUSION:Implementation of preemptive rather than reactive and sequence-based rather than genotype-based PGx prescribing revealed nearly universal patient applicability and required integrated institution-wide resources to fully realize individualized drug therapy and to show more efficient use of health care resources.