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.

OBJECTIVE:To determine whether the risks of infectious mononucleosis (IM) and serious IM outcomes are changing over time. PATIENTS AND METHODS:Individuals with a diagnosis of IM and hospitalizations due to IM were identified among persons residing in an Upper Midwest region between January 1, 2010, and December 31, 2021, using the Rochester Epidemiology Project. Infectious mononucleosis rates were calculated assuming the entire population between 2010 and 2021 was at risk, and IM-associated hospitalization rates were calculated among everyone with a diagnosis of IM. Poisson regression was used to test trends and estimate incidence and hospitalization rate ratios. RESULTS:We identified 5334 individuals with IM; the overall IM rate was 60.60 per 100,000 person-years (95% CI, 58.98 to 62.25). Rates were highest in females, individuals of White race, those with non-Hispanic ethnicity, and individuals 15 to 19 years old (all P<.05). Infectious mononucleosis rates decreased significantly over time among all age groups (all tests for trend, P<.05). Overall, 234 individuals (4.3%) were hospitalized with IM (43.87 per 1000 persons with IM; 95% CI, 38.43 to 49.87), and hospitalization rates among those with IM increased over time (test for trend, P<.05). Individuals younger than 10 years, those 20 years or older, and individuals of Hispanic ethnicity had increased risk for IM-associated hospitalization (all adjusted P<.05). CONCLUSION:Although rates of IM diagnosis have decreased over time, risk of hospitalization in individuals with IM has increased. Age and ethnicity increase the risk of hospitalization due to IM.

Obesity accelerates the onset and progression of age-related conditions. In preclinical models, obesity drives cellular senescence, a cell fate that compromises tissue health and function, in part through a robust and diverse secretome. In humans, components of the secretome have been used as senescence biomarkers that are predictive of age-related disease, disability, and mortality. Here, using biospecimens and clinical data from two large and independent cohorts of older adults, we tested the hypothesis that the circulating concentrations of senescence biomarkers are influenced by body mass index. After adjusting for age, sex, and race, we observed significant increases in activin A, Fas, MDC, PAI1, PARC, TNFR1, and VEGFA, and a significant decrease in RAGE, from normal weight, to overweight, to obesity body mass index categories by linear regression in both cohorts (all p < .05). These results highlight the influence of body mass index on circulating concentrations of senescence biomarkers.

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.

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.

Objective: Infectious mononucleosis (IM) or mono is typically caused by primary infection with Epstein-Barr virus (EBV) and may have a months-long, complicated course. We utilized population-based data to add to the limited literature on health care utilization following EBV infection. Methods: The Rochester Epidemiology Project includes medical records for similar to 60% of residents living in 27 counties of Minnesota (MN) and Wisconsin (WI). Persons meeting a case definition of recent EBV infection from 1 January 1998 to 31 December 2021 were compared to three persons not meeting the definition, matched on case's sex, age, and index date. Emergency department (ED) visits and hospitalizations in the two groups were compared during 5-years' follow-up divided into three periods (short-term <= 3 months, mid-term >3 months-1 year, long-term >1-5 years). Adjusted hazard ratios (AHR) were estimated to account for the potential influence of confounding variables. Results: In total, 6,423 persons had a recent EBV infection and were matched to 19,269 comparators. The risk of an ED visit was significantly higher among cases in the short-term period (24.3% vs referents: 7.6%, p <.001; AHR = 3.71, 95% CI = 3.41-4.03). Cases also had an increased risk of hospitalization in the short-term (5.2% vs 1.6%: referents, p <.001; AHR = 3.53, 95% CI = 2.94-4.24). For ED visits but not hospitalization, the excess risk persisted into the mid-term follow-up period. Persons without a concurrent clinical diagnosis of IM continued to have an increased risk of hospitalizations up to 1 year after index date (AHR = 1.45, 95% CI = 1.09-1.91) and an increased risk of ED visits up to 5 years after the index date (AHR = 1.29, 95% CI = 1.14-1.46). Conclusion: There is a substantial short- and mid-term increased risk of serious health care encounters associated with recent EBV infection. Mid- and long-term risks are increased in patients who do not have a concomitant diagnosis of IM.

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.

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.

OBJECTIVE:Ceramides have been associated with several ageing-related conditions but have not been studied as a general biomarker of multimorbidity (MM). Therefore, we determined whether ceramide levels are associated with the rapid development of MM.DESIGN:Retrospective cohort study.SETTING:Mayo Clinic Biobank.PARTICIPANTS:1809 persons in the Mayo Clinic Biobank ≥65 years without MM at the time of enrolment, and with ceramide levels assayed from stored plasma.PRIMARY OUTCOME MEASURE:Persons were followed for a median of 5.7 years through their medical records to identify new diagnoses of 20 chronic conditions. The number of new conditions was divided by the person-years of follow-up to calculate the rate of accumulation of new chronic conditions.RESULTS:Higher levels of C18:0 and C20:0 were associated with a more rapid rate of accumulation of chronic conditions (C18:0 z score RR: 1.30, 95% CI: 1.10 to 1.53; C20:0 z score RR: 1.26, 95% CI: 1.07 to 1.49). Higher C18:0 and C20:0 levels were also associated with an increased risk of hypertension and coronary artery disease.CONCLUSIONS:C18:0 and C20:0 were associated with an increased risk of cardiometabolic conditions. When combined with biomarkers specific to other diseases of ageing, these ceramides may be a useful component of a biomarker panel for predicting accelerated ageing.

Background: Most patients with heart failure (HF) have multimorbidity which may cause difficulties with self-management. Understanding the determinants of successful self-management and the resources patients draw upon to effectively manage their health is fundamental to designing new practice models to improve outcomes in HF. Methods: We developed a survey guided by the Chronic Care Model to understand the distribution of patient-centric factors, including health literacy, social support, self-management, and functional and mental status in patients with HF. The survey was administered to HF patients from 4 health care systems participating in PCORnet® (the National Patient-Centered Clinical Research Network). Patients were identified utilizing data available in the PCORnet common data model on or after 1/1/2013 at 1 health system and on or after 1/1/2015 at 3 health systems: age ≥30 years, ≥2 HF diagnostic codes, and ≥1 HF-related prescription (positive predictive value of algorithm: 81%). Results: A total of 10,662 patients with HF were identified, 9684 were mailed a survey, and 3330 completed a survey (response rate: 35%). Responders were older than non-responders (74 vs. 71 years), less racially diverse (3% vs. 12% non-White), less likely to have reduced ejection fraction (EF; 22% vs. 27% with EF <40%), and had higher prevalence of most chronic conditions. Responders reported their health was generally good or fair, they frequently had cardiovascular comorbidities, more than half had difficulty climbing stairs, and more than 10% reported difficulties with bathing, preparing meals, and using transportation (Table). Nearly 80% of patients had family or friends sit with them during a doctor visit, and most (54%) manage their health by themselves. Conclusion: More than half of patients with HF manage their health by themselves even though most bring someone along to their health care appointments. Increased understanding of self-management resources may guide the development of interventions to improve HF outcomes.

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: 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.

Abstract Introduction: We tested the ability of our natural language processing (NLP) algorithm to identify delirium episodes in a large-scale study using real-world clinical notes. Methods: We used the Rochester Epidemiology Project to identify persons ≥ 65 years who were hospitalized between 2011 and 2017. We identified all persons with an International Classification of Diseases code for delirium within ±14 days of a hospitalization. We independently applied our NLP algorithm to all clinical notes for this same population. We calculated rates using number of delirium episodes as the numerator and number of hospitalizations as the denominator. Rates were estimated overall, by demographic characteristics, and by year of episode, and differences were tested using Poisson regression. Results: In total, 14,255 persons had 37,554 hospitalizations between 2011 and 2017. The code-based delirium rate was 3.02 per 100 hospitalizations (95% CI: 2.85, 3.20). The NLP-based rate was 7.36 per 100 (95% CI: 7.09, 7.64). Rates increased with age (both p < 0.0001). Code-based rates were higher in men compared to women (p = 0.03), but NLP-based rates were similar by sex (p = 0.89). Code-based rates were similar by race and ethnicity, but NLP-based rates were higher in the White population compared to the Black and Asian populations (p = 0.001). Both types of rates increased significantly over time (both p values < 0.001). Conclusions: The NLP algorithm identified more delirium episodes compared to the ICD code method. However, NLP may still underestimate delirium cases because of limitations in real-world clinical notes, including incomplete documentation, practice changes over time, and missing clinical notes in some time periods.

Background: Multimorbidity (MM) and functional limitation (FL) are associated with poor outcomes in heart failure (HF). However, the individual and combined effect of these on mental health and quality of life in patients with HF is not well understood. Methods: Patients aged ≥ 30 years with 2 or more HF diagnostic codes and 1 or more HF-related prescription drugs from four US institutions were mailed a survey to measure functional status (activities of daily living [ADLs]), quality of life and mental health (PROMIS-29 Health Profile) and social support (PROMIS Informational Support, Instrumental Support, and Social Isolation Short Forms). The sampling frame was restricted to patients with a first ever-diagnosis of HF on or after 1/1/2013 at 1 of the participating sites and on or after 1/1/2015 for the other 3 participating sites. A total of 3330 patients returned the survey (response rate 35%); among these, 3020 completed the questions of interest for this analysis and were retained. MM was defined as the presence of ≥ 2 non-cardiovascular comorbidities, and FL was defined as reporting any limitation in at least 1 of 8 ADLs. Patients were categorized into 4 groups by MM (Yes/No) and FL (Yes/No). We dichotomized the subscale scores of the PROMIS-29 at the median and calculated odd ratios for the 4 MM/FL groups. Results: Among 3020 patients with HF (45% female; mean age 73±12 years), 29% had neither MM or FL, 24% had MM only, 22% had FL only, and 25% had both. After adjustment, having both MM and FL or only FL was associated with increased odds of higher anxiety, depression, fatigue, sleep, and pain scores compared to having neither (Figure); having MM only was associated with a higher pain score. Conclusions: Patients with both FL and MM and only FL have similar odds of poor mental health and quality of life scores, underscoring the importance of the role that FL plays in outcomes in patients with HF.

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: Heart failure (HF) with an ejection fraction (EF) of 41%-49% is recognized as HF with a mildly reduced EF (HFmrEF). However, existing knowledge of the HFmrEF phenotype is based on HF clinical trial and registry cohorts that may be limited by multiple forms of bias. Methods and Results: In a community-based, retrospective cohort study, adult residents of Olmsted County, Minnesota, with validated (Framingham criteria) incident HF from 2007 to 2015 were categorized by echocardiographic EF at first HF diagnosis. Among 2035 adults with incident HF, 12.5% had HFmrEF, 29.9% had HF with reduced EF (HFrEF), and 57.6% had HF with preserved EF (HFpEF). Mean age and sex varied by EF group, with HFmrEF (75.6 years, 45.3% female), HFrEF (70.9 years, 36.5% female), and HFpEF (76.9 years, 59.7% female). Most comorbid conditions were more common in HFmrEF vs HFrEF, but similar in HFmrEF and HFpEF. After a mean follow-up of 4.6 perpendicular to 3.5 years, adjusting for age, sex, and comorbidities, the risks of hospitalization and cardiovascular mortality did not differ by EF category. Of patients who began as HFmrEF, 26.9% declined to an EF of 40% or less and 44.8% improved to an EF of 50% or greater. Conclusions: In this community cohort of incident HF, 12.5% have HFmrEF. Clinical character-istics in HFmrEF resemble HFpEF more than HFrEF. Adjusted hospitalization and mortality risks did not vary by EF group. Patients with incident HFmrEF usually transitioned to a different EF category on follow-up. (J Cardiac Fail 2023;29:124-134)

BackgroundHeart 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 ResultsResidents 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]).ConclusionsAwareness 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.