Comparative Effectiveness of Aspirin Dosing in Cardiovascular Disease.

BACKGROUND: The appropriate dose of aspirin to lower the risk of death, myocardial infarction, and stroke and to minimize major bleeding in patients with established atherosclerotic cardiovascular disease is a subject of controversy. METHODS: Using an open-label, pragmatic design, we randomly assigned patients with established atherosclerotic cardiovascular disease to a strategy of 81 mg or 325 mg of aspirin per day. The primary effectiveness outcome was a composite of death from any cause, hospitalization for myocardial infarction, or hospitalization for stroke, assessed in a time-to-event analysis. The primary safety outcome was hospitalization for major bleeding, also assessed in a time-to-event analysis. RESULTS: A total of 15,076 patients were followed for a median of 26.2 months (interquartile range [IQR], 19.0 to 34.9). Before randomization, 13,537 (96.0% of those with available information on previous aspirin use) were already taking aspirin, and 85.3% of these patients were previously taking 81 mg of daily aspirin. Death, hospitalization for myocardial infarction, or hospitalization for stroke occurred in 590 patients (estimated percentage, 7.28%) in the 81-mg group and 569 patients (estimated percentage, 7.51%) in the 325-mg group (hazard ratio, 1.02; 95% confidence interval [CI], 0.91 to 1.14). Hospitalization for major bleeding occurred in 53 patients (estimated percentage, 0.63%) in the 81-mg group and 44 patients (estimated percentage, 0.60%) in the 325-mg group (hazard ratio, 1.18; 95% CI, 0.79 to 1.77). Patients assigned to 325 mg had a higher incidence of dose switching than those assigned to 81 mg (41.6% vs. 7.1%) and fewer median days of exposure to the assigned dose (434 days [IQR, 139 to 737] vs. 650 days [IQR, 415 to 922]). CONCLUSIONS: In this pragmatic trial involving patients with established cardiovascular disease, there was substantial dose switching to 81 mg of daily aspirin and no significant differences in cardiovascular events or major bleeding between patients assigned to 81 mg and those assigned to 325 mg of aspirin daily. (Funded by the Patient-Centered Outcomes Research Institute; ADAPTABLE ClinicalTrials.gov number, NCT02697916.).

"I Just Wanted Nothing More Than to Get in a Real Shower": Patient Experience of the Inpatient Wait for a Heart Transplant.

BACKGROUND: Patients waiting for heart transplant may be hospitalized for weeks to months before undergoing transplantation. This high-stress period is further complicated by restrictions of daily privileges including diet, rooming, access to the outdoors, and hygiene (eg, limited in ability to shower). However, there is a paucity of research on the experience of this waiting period. We sought to describe the inpatient experience among patients awaiting heart transplantation and to better understand the needs of inpatients waiting for heart transplant. METHODS AND RESULTS: We conducted in-depth, semistructured phone interviews with a purposeful sample of patients who received a heart transplant in the past 10 years and waited in the hospital for at least 2 weeks before surgery. Using the prior literature, the lived experience of the lead author, and input from qualitative experts, we developed an interview guide. Interviews were recorded, transcribed, and analyzed in an iterative process until theoretical saturation was achieved. A 3-person coding team identified, discussed, and reconciled emergent themes. We conducted interviews with 15 patients. Overarching themes included food, hygiene, relationship with health care professionals, living environment, and stressors. Patients reported that strong bonds were formed between the patients and the staff, and the overwhelming majority only had positive comments about these relationships. However, many expressed negative comments about the experience of the food and limitations in personal hygiene. Other stressors included the unknown length of the waiting period, lack of communication about position on the transplant list, worry about family, and concerns that their life must be saved by the death of another. Many participants described that they would benefit from more interaction with recent heart transplant recipients. CONCLUSIONS: Hospitals and care units have the opportunity to make small changes that could greatly benefit the experience of waiting for a heart transplant, as well as the experience of hospitalization more generally.

Association Between End-Stage Liver Disease and Incident Heart Failure in an Integrated Health System.

BACKGROUND: End-stage liver disease (ESLD) and heart failure (HF) often coexist and are associated with significant morbidity and mortality. However, the true incidence of HF among patients with ESLD remains understudied. OBJECTIVE: This study aims to evaluate the association between ESLD and incident HF in a real-world clinical cohort. DESIGN AND PARTICIPANTS: A retrospective electronic health records database analysis of individuals with ESLD and frequency-matched controls without ESLD in a large integrated health system. MAIN MEASURES: The primary outcome was incident HF, which was defined by the International Classification of Disease codes and manually adjudicated by physician reviewers. The Kaplan-Meier method was used to estimate the cumulative incidence of HF. Multivariate proportional hazards models adjusted for shared metabolic factors (diabetes, hypertension, chronic kidney disease, coronary heart disease, body mass index) were used to compare the risk of HF in patients with and without ESLD. KEY RESULTS: Of 5004 patients (2502 with ESLD and 2502 without ESLD), the median (Q1-Q3) age was 57.0 (55.0-65.0) years, 59% were male, and 18% had diabetes. Over a median (Q1-Q3) follow-up of 2.3 (0.6-6.0) years, 121 incident HF cases occurred. Risk for incident HF was significantly higher for patients with ESLD compared with the non-ESLD group (adjusted HR: 4.67; 95% CI: 2.82-7.75; p < 0.001), with the majority of the ESLD group (70.7%) having HF with preserved ejection fraction (ejection fraction ≥ 50%). CONCLUSION: ESLD was significantly associated with a higher risk of incident HF, independent of shared metabolic risk factors, with the predominant phenotype being HF with preserved ejection fraction.

The Emerging Role of Artificial Intelligence in Valvular Heart Disease.

Valvular heart disease (VHD) is a morbid condition in which timely identification and evidence-based treatments can lead to improved outcomes. Artificial intelligence broadly refers to the ability for computers to perform tasks and problem solve like the human mind. Studies applying AI to VHD have used a variety of structured (eg, sociodemographic, clinical) and unstructured (eg, electrocardiogram, phonocardiogram, and echocardiograms) and machine learning modeling approaches. Additional researches in diverse populations, including prospective clinical trials, are needed to evaluate the effectiveness and value of AI-enabled medical technologies in clinical care for patients with VHD.

A Cohort Analysis of Statin Treatment Patterns Among Small-Sized Primary Care Practices.

BACKGROUND: Small-sized primary care practices, defined as practices with fewer than 10 clinicians, delivered the majority of outpatient visits in the USA. Statin therapy in high-risk individuals reduces atherosclerotic cardiovascular disease (ASCVD) events, but prescribing patterns in small primary care practices are not well known. This study describes statin treatment patterns in small-sized primary care practices and examines patient- and practice-level factors associated with lack of statin treatment. METHODS: We conducted a retrospective cohort analysis of statin-eligible patients from practices that participated in Healthy Hearts in the Heartland (H3), a quality improvement initiative aimed at improving cardiovascular care measures in small primary care practices. All statin-eligible adults who received care in one of 53 H3 practices from 2013 to 2016. Statin-eligible adults include those aged at least 21 with (1) clinical ASCVD, (2) low-density lipoprotein cholesterol (LDL-C) ≥ 190 mg/dL, or (3) diabetes aged 40-75 and with LDL-C 70-189 mg/dL. Eligible patients with no record of moderate- to high-intensity statin prescription are defined by ACC/AHA guidelines. RESULTS: Among the 13,330 statin-eligible adults, the mean age was 58 years and 52% were women. Overall, there was no record of moderate- to high-intensity statin prescription among 5,780 (43%) patients. Younger age, female sex, and lower LDL-C were independently associated with a lack of appropriate intensity statin therapy. Higher proportions of patients insured by Medicaid and having only family medicine trained physicians (versus having at least one internal medicine trained physician) at the practice were also associated with lower appropriate intensity statin use. Lack of appropriate intensity statin therapy was higher in independent practices than in Federally Qualified Health Centers (FQHCs) (50% vs. 40%, p value < 0.01). CONCLUSIONS: There is an opportunity for improved ASCVD risk reduction in small primary care practices. Statin treatment patterns and factors influencing lack of treatment vary by practice setting, highlighting the importance of tailored approaches to each setting.

Advances in Machine Learning Approaches to Heart Failure with Preserved Ejection Fraction.

Heart failure with preserved ejection fraction (HFpEF) represents a prototypical cardiovascular condition in which machine learning may improve targeted therapies and mechanistic understanding of pathogenesis. Machine learning, which involves algorithms that learn from data, has the potential to guide precision medicine approaches for complex clinical syndromes such as HFpEF. It is therefore important to understand the potential utility and common pitfalls of machine learning so that it can be applied and interpreted appropriately. Although machine learning holds considerable promise for HFpEF, it is subject to several potential pitfalls, which are important factors to consider when interpreting machine learning studies.

Divergent Trends in Emergency Department Presentations Amid the Novel Coronavirus Disease 2019 Pandemic in Chicago, Illinois.

Excess deaths during the coronavirus disease 2019 (COVID-19) pandemic have been largely attributed to cardiovascular disease (CVD); however, patterns in CVD hospitalizations after the first surge of the pandemic have not well-documented. Our brief report, examining trends in health care avoidance documents that CVD hospitalizations decreased in Chicago before significant burden of COVID-19 cases or deaths and normalized during the first COVID-19 surge. These data may help to inform health care systems responses in the coming months while mobilizing vaccinations to the population at large.

Association of fasting glucose with lifetime risk of incident heart failure: the Lifetime Risk Pooling Project.

BACKGROUND: Given the rising prevalence of dysglycemia and disparities in heart failure (HF) burden, we determined race- and sex-specific lifetime risk of HF across the spectrum of fasting plasma glucose (FPG). METHODS: Individual-level data from adults without baseline HF was pooled from 6 population-based cohorts. Modified Kaplan-Meier analysis, Cox models adjusted for the competing risk of death, and Irwin's restricted mean were used to estimate the lifetime risk, adjusted hazard ratio (aHR), and years lived free from HF in middle-aged (40-59 years) and older (60-79 years) adults with FPG < 100 mg/dL, prediabetes (FPG 100-125 mg/dL) and diabetes (FPG ≥ 126 mg/dL or on antihyperglycemic agents) across race-sex groups. RESULTS: In 40,117 participants with 638,910 person-years of follow-up, 4846 cases of incident HF occurred. The lifetime risk of HF was significantly higher among middle-aged White adults and Black women with prediabetes (range: 6.1% [95% CI 4.8%, 7.4%] to 10.8% [95% CI 8.3%, 13.4%]) compared with normoglycemic adults (range: 3.5% [95% CI 3.0%, 4.1%] to 6.5% [95% CI 4.9%, 8.1%]). Middle-aged Black women with diabetes had the highest lifetime risk (32.4% [95% CI 26.0%, 38.7%]) and aHR (4.0 [95% CI 3.0, 5.4]) for HF across race-sex groups. Middle-aged adults with prediabetes and diabetes lived on average 0.9-1.6 and 4.1-6.0 fewer years free from HF, respectively. Findings were similar in older adults except older Black women with prediabetes did not have a higher lifetime risk of HF. CONCLUSIONS: Prediabetes was associated with higher lifetime risk of HF in middle-aged White adults and Black women, with the association attenuating in older Black women. Black women with diabetes had the highest lifetime risk of HF compared with other race-sex groups.

Neighborhood Poverty and Incident Heart Failure: an Analysis of Electronic Health Records from 2005 to 2018.

BACKGROUND: Neighborhood-level characteristics, such as poverty, have been associated with risk factors for heart failure (HF), including hypertension and diabetes mellitus. However, the independent association between neighborhood poverty and incident HF remains understudied. OBJECTIVE: To evaluate the association between neighborhood poverty and incident HF using a "real-world" clinical cohort. DESIGN: Retrospective cohort study of electronic health records from a large healthcare network. Individuals' residential addresses were geocoded at the census-tract level and categorized by poverty tertiles based on American Community Survey data (2007-2011). PARTICIPANTS: Patients from Northwestern Medicine who were 30-80 years, free of cardiovascular disease at index visit (January 1, 2005-December 1, 2013), and followed for at least 5 years. MAIN MEASURES: The association of neighborhood-level poverty tertile (low, intermediate, and high) and incident HF was analyzed using generalized linear mixed effect models adjusting for demographics (age, sex, race/ethnicity) and HF risk factors (body mass index, diabetes mellitus, hypertension, smoking status). KEY RESULTS: Of 28,858 patients included, 75% were non-Hispanic (NH) White, 43% were men, 15% lived in a high-poverty neighborhood, and 522 (1.8%) were diagnosed with incident HF. High-poverty neighborhoods were associated with a 1.80 (1.35, 2.39) times higher risk of incident HF compared with low-poverty neighborhoods after adjustment for demographics and HF risk factors. CONCLUSIONS: In a large healthcare network, incident HF was associated with neighborhood poverty independent of demographic and clinical risk factors. Neighborhood-level interventions may be needed to complement individual-level strategies to prevent and curb the growing burden of HF.

Integrating hypertension phenotype and genotype with hybrid non-negative matrix factorization.

MOTIVATION: Hypertension is a heterogeneous syndrome in need of improved subtyping using phenotypic and genetic measurements with the goal of identifying subtypes of patients who share similar pathophysiologic mechanisms and may respond more uniformly to targeted treatments. Existing machine learning approaches often face challenges in integrating phenotype and genotype information and presenting to clinicians an interpretable model. We aim to provide informed patient stratification based on phenotype and genotype features. RESULTS: In this article, we present a hybrid non-negative matrix factorization (HNMF) method to integrate phenotype and genotype information for patient stratification. HNMF simultaneously approximates the phenotypic and genetic feature matrices using different appropriate loss functions, and generates patient subtypes, phenotypic groups and genetic groups. Unlike previous methods, HNMF approximates phenotypic matrix under Frobenius loss, and genetic matrix under Kullback-Leibler (KL) loss. We propose an alternating projected gradient method to solve the approximation problem. Simulation shows HNMF converges fast and accurately to the true factor matrices. On a real-world clinical dataset, we used the patient factor matrix as features and examined the association of these features with indices of cardiac mechanics. We compared HNMF with six different models using phenotype or genotype features alone, with or without NMF, or using joint NMF with only one type of loss We also compared HNMF with 3 recently published methods for integrative clustering analysis, including iClusterBayes, Bayesian joint analysis and JIVE. HNMF significantly outperforms all comparison models. HNMF also reveals intuitive phenotype-genotype interactions that characterize cardiac abnormalities. AVAILABILITY AND IMPLEMENTATION: Our code is publicly available on github at https://github.com/yuanluo/hnmf. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Effects of 2 Forms of Practice Facilitation on Cardiovascular Prevention in Primary Care: A Practice-randomized, Comparative Effectiveness Trial.

BACKGROUND: Effective quality improvement (QI) strategies are needed for small practices. OBJECTIVE: The objective of this study was to compare practice facilitation implementing point-of-care (POC) QI strategies alone versus facilitation implementing point-of-care plus population management (POC+PM) strategies on preventive cardiovascular care. DESIGN: Two arm, practice-randomized, comparative effectiveness study. PARTICIPANTS: Small and mid-sized primary care practices. INTERVENTIONS: Practices worked with facilitators on QI for 12 months to implement POC or POC+PM strategies. MEASURES: Proportion of eligible patients in a practice meeting "ABCS" measures: (Aspirin) Aspirin/antiplatelet therapy for ischemic vascular disease, (Blood pressure) Controlling High Blood Pressure, (Cholesterol) Statin Therapy for the Prevention and Treatment of Cardiovascular Disease, and (Smoking) Tobacco Use: Screening and Cessation Intervention, and the Change Process Capability Questionnaire. Measurements were performed at baseline, 12, and 18 months. RESULTS: A total of 226 practices were randomized, 179 contributed follow-up data. The mean proportion of patients meeting each performance measure was greater at 12 months compared with baseline: Aspirin 0.04 (95% confidence interval: 0.02-0.06), Blood pressure 0.04 (0.02-0.06), Cholesterol 0.05 (0.03-0.07), Smoking 0.05 (0.02-0.07); P<0.001 for each. Improvements were sustained at 18 months. At 12 months, baseline-adjusted difference-in-differences in proportions for the POC+PM arm versus POC was: Aspirin 0.02 (-0.02 to 0.05), Blood pressure -0.01 (-0.04 to 0.03), Cholesterol 0.03 (0.00-0.07), and Smoking 0.02 (-0.02 to 0.06); P>0.05 for all. Change Process Capability Questionnaire improved slightly, mean change 0.30 (0.09-0.51) but did not significantly differ across arms. CONCLUSION: Facilitator-led QI promoting population management approaches plus POC improvement strategies was not clearly superior to POC strategies alone.

Predicting High-Risk Patients and High-Risk Outcomes in Heart Failure.

Identifying patients with heart failure at high risk for poor outcomes is important for patient care, resource allocation, and process improvement. Although numerous risk models exist to predict mortality, hospitalization, and patient-reported health status, they are infrequently used for several reasons, including modest performance, lack of evidence to support routine clinical use, and barriers to implementation. Artificial intelligence has the potential to enhance the performance of risk prediction models, but has its own limitations and remains unproved.

Defragmenting Heart Failure Care: Medical Records Integration.

Heart failure management requires intensive care coordination. Guideline-directed medical therapies have been shown to save lives but are practically challenging to implement because of the fragmented care that heart failure patients experience. Electronic health record adoption has transformed the collection and storage of clinical data, but accessing these data often remains prohibitively difficult. Current legislation aims to increase the interoperability of software systems so that providers and patients can easily access the clinical information they desire. Novel heart failure devices and technologies leverage patient-generated data to manage heart failure patients, whereas new data standards make it possible for this information to guide clinical decision-making.

Validity of Cardiovascular Data From Electronic Sources: The Multi-Ethnic Study of Atherosclerosis and HealthLNK.

BACKGROUND: Understanding the validity of data from electronic data research networks is critical to national research initiatives and learning healthcare systems for cardiovascular care. Our goal was to evaluate the degree of agreement of electronic data research networks in comparison with data collected by standardized research approaches in a cohort study. METHODS: We linked individual-level data from MESA (Multi-Ethnic Study of Atherosclerosis), a community-based cohort, with HealthLNK, a 2006 to 2012 database of electronic health records from 6 Chicago health systems. To evaluate the correlation and agreement of blood pressure in HealthLNK in comparison with in-person MESA examinations, and body mass index in HealthLNK in comparison with MESA, we used Pearson correlation coefficients and Bland-Altman plots. Using diagnoses in MESA as the criterion standard, we calculated the performance of HealthLNK for hypertension, obesity, and diabetes mellitus diagnosis by using International Classification of Diseases, Ninth Revision codes and clinical data. We also identified potential myocardial infarctions, strokes, and heart failure events in HealthLNK and compared them with adjudicated events in MESA. RESULTS: Of the 1164 MESA participants enrolled at the Chicago Field Center, 802 (68.9%) participants had data in HealthLNK. The correlation was low for systolic blood pressure (0.39; P<0.0001). In comparison with MESA, HealthLNK overestimated systolic blood pressure by 6.5 mm Hg (95% confidence interval, 4.2-7.8). There was a high correlation between body mass index in MESA and HealthLNK (0.94; P<0.0001). HealthLNK underestimated body mass index by 0.3 kg/m2 (95% confidence interval, -0.4 to -0.1). With the use of International Classification of Diseases, Ninth Revision codes and clinical data, the sensitivity and specificity of HealthLNK queries for hypertension were 82.4% and 59.4%, for obesity were 73.0% and 89.8%, and for diabetes mellitus were 79.8% and 93.3%. In comparison with adjudicated cardiovascular events in MESA, the concordance rates for myocardial infarction, stroke, and heart failure were, respectively, 41.7% (5/12), 61.5% (8/13), and 62.5% (10/16). CONCLUSIONS: These findings illustrate the limitations and strengths of electronic data repositories in comparison with information collected by traditional standardized epidemiological approaches for the ascertainment of cardiovascular risk factors and events.

Incorporating patient-centered factors into heart failure readmission risk prediction: A mixed-methods study.

BACKGROUND: Capturing and incorporating patient-centered factors into 30-day readmission risk prediction after hospitalized heart failure (HF) could improve the modest performance of current models. METHODS: Using a mixed-methods approach, we developed a patient-centered survey and evaluated the additional predictive utility of the survey compared to a traditional readmission risk model (the Krumholz et al. model). Area under the receiver operating characteristic curve (AUC) and the Hosmer-Lemeshow goodness-of-fit statistic quantified the performance of both models. We measured the amount of model improvement with the addition of patient-centered factors to the Krumholz et al. model with the integrated discrimination improvement (IDI). In an exploratory analysis, we used hierarchical clustering algorithms to identify groups with similar survey responses and tested for differences between clusters using standard descriptive statistics. RESULTS: From 3/24/2014 to 3/12/2015, 183 patients hospitalized with HF were enrolled from an urban, academic health system and followed for 30days after discharge. The Krumholz et al. plus patient-centered factors model had similar-to-slightly lower performance (AUC [95%CI]:0.62 [0.52, 0.71]; goodness-of-fit P=.10) than the Krumholz et al. model (AUC [95%CI]:0.66 [0.57, 0.76]; goodness-of-fit P=.19). The IDI (95%CI) was 0.003 (-0.014,0.020). We identified three patient clusters based on patient-centered survey responses. The clusters differed with respect to gender, self-rated health, employment status, and prior hospitalization frequency (all P<.05). CONCLUSIONS: The addition of patient-centered factors did not improve 30-day readmission model performance. Rather than designing interventions based on predicted readmission risk, tailoring interventions to all patients, based on their characteristics, could inform the design of targeted, readmission reduction strategies.

Heart Failure Home Management Challenges and Reasons for Readmission: a Qualitative Study to Understand the Patient's Perspective.

BACKGROUND: Heart failure patients have high 30-day hospital readmission rates. Interventions designed to prevent readmissions have had mixed success. Understanding heart failure home management through the patient's experience may reframe the readmission "problem" and, ultimately, inform alternative strategies. OBJECTIVE: To understand patient and caregiver challenges to heart failure home management and perceived reasons for readmission. DESIGN: Observational qualitative study. PARTICIPANTS: Heart failure patients were recruited from two hospitals and included those who were hospitalized for heart failure at least twice within 30 days and those who had been recently discharged after their first heart failure admission. APPROACH: Open-ended, semi-structured interviews. Conclusions vetted using focus groups. KEY RESULTS: Semi-structured interviews with 31 patients revealed a combination of physical and socio-emotional influences on patients' home heart failure management. Major themes identified were home management as a struggle between adherence and adaptation, and hospital readmission as a rational choice in response to distressing symptoms. Patients identified uncertainty regarding recommendations, caused by unclear instructions and temporal incongruence between behavior and symptom onset. This uncertainty impaired their competence in making routine management decisions, resulting in a cycle of limit testing and decreasing adherence. Patients reported experiencing hopelessness and frustration in response to perceiving a deteriorating functional status. This led some to a cycle of despair characterized by worsening adherence and negative emotions. As these cycles progressed and distressing symptoms worsened, patients viewed the hospital as the safest place for recovery and not a "negative" outcome. CONCLUSION: Cycles of limit testing and despair represent important patient-centered struggles in managing heart failure. The resulting distress and fear make readmission a rational choice for patients rather than a negative outcome. Interventions (e.g., palliative care) that focus on methods to address these patient-centered factors should be further studied rather than methods to reduce hospital readmissions.

Endovascular repair of ventricular assist device outflow cannula stenosis.

This report describes a case of endovascular repair of an outflow cannula obstruction in a heart failure patient with biventricular assist devices. The patient presented with cardiogenic shock and was diagnosed via multimodality imaging with outflow cannula obstruction of the left ventricular assist device, likely from a hematoma. A transesophageal echocardiogram-guided endovascular approach was undertaken. A 10.0 mm × 38 mm covered stent was successfully deployed and post-dilated. Normal flow in the outflow cannula was restored. Hemodynamic and left ventricular flow parameters returned close to baseline post-procedure. The growth in ventricular assist device implantation and associated complications will create new opportunities for endovascular repair. © 2016 Wiley Periodicals, Inc.

Comparing Perspectives of Patients, Caregivers, and Clinicians on Heart Failure Management.

BACKGROUND: Although substantial effort has been devoted to reducing readmissions among heart failure (HF) patients, little is known about factors identified by patients and caregivers that may contribute to readmissions. The goal of this study was to compare the perspectives of HF patients, their caregivers, and their care team on HF management and hospital admissions. Understanding these perspectives may lead to better strategies for improving care during the post-hospital transition and for reducing preventable readmissions. METHODS AND RESULTS: We performed freelisting, an anthropologic technique in which participants list items in response to a question, with hospitalized HF patients (n = 58), their caregivers (n = 32), and clinicians (n = 67). We asked about home HF management tasks, difficulties in managing HF, and perceived reasons for hospital admission. Results were analyzed with the use of Anthropac. Salience indices (measures of the most important words for defining the domain of interest) were calculated. Patients and clinicians described similar home HF management tasks, whereas caregivers described tasks related to activities of daily living. Clinicians cited socioeconomic factors as challenges to HF management, whereas patients and caregivers cited limited functional status and daily activities. When asked about reasons for hospitalization, patients and caregivers listed distressing symptoms and illness, whereas clinicians viewed patient behaviors to be primarily responsible for admission. CONCLUSIONS: These findings highlight that although some similarities exist, there are important differences among patients, caregivers, and clinicians in how they perceive the challenges of HF management and reasons for readmission. Understanding these differences may be critical to developing strategies to reduce readmissions.