Symptom Clusters in Adults With Chronic Atrial Fibrillation

More positive patient-reported outcomes in patients newly diagnosed with atrial fibrillation: a comparative longitudinal study

AIMS

To compare patient-reported outcomes (PROs) in patients newly (<6 months) diagnosed with atrial fibrillation (AF) with those who have had a longer diagnosis (≥6 months) and to investigate whether or not these outcomes change over a 6-month period.

METHODS AND RESULTS

In this longitudinal survey study, 129 patients with AF completed the Revised Illness Perception Questionnaire, the Arrhythmia-Specific questionnaire in Tachycardia and Arrhythmia, and the Hospital Anxiety and Depression Scale at baseline and after 6 months. At baseline, patients newly diagnosed with AF (n = 53), compared with patients with a previous diagnosis (n = 76), reported AF as more temporary (P = 0.003) and had a higher belief in personal and treatment control (P = 0.004 and P = 0.041, respectively). At a 6-month follow-up, patients newly diagnosed reported a lower symptom burden (P = 0.004), better health-related quality of life (HRQoL); (P = 0.015), and a higher personal control (P < 0.001) than patients previously diagnosed. Over time, in patients newly diagnosed, symptom burden and the anxiety symptom score decreased (P = 0.001 and P = 0.014, respectively) and HRQoL improved (P = 0.002).

CONCLUSION

Patients newly diagnosed with AF reported more positive PROs both at baseline and at a 6-month follow-up than patients with a previous diagnosis of AF. Therefore, it is important to quickly capture patients newly diagnosed to support their belief in their own abilities. Such support may, alongside medical treatments, help patients manage the disease, which may lead to reduced symptom burden and better HRQoL over time.

Factors Associated With Fatigue in Persons With Atrial Fibrillation in the Atherosclerosis Risk in Communities (ARIC) Study

BACKGROUND

Atrial fibrillation (AF) is a common cardiac arrhythmia affecting over 6 million people in the U.S. Fatigue is a frequent symptom of AF, yet no underlying biological mechanisms have been identified in AF-related fatigue as in other chronic conditions such as cancer or HIV fatigue (inflammation, tissue injury). We aimed to identify biomarkers and correlates of AF-fatigue in ARIC participants.

METHODS

Participants with AF from ARIC visit 5 (2011-2013) were included in the study. Multiple linear regression was used to estimate the association of high sensitivity troponin (hs-TnT), N-terminal fragment B-type natriuretic peptide (NT-proBNP) and high sensitivity C-reactive protein (hsCRP) levels with self-reported fatigue (SF-12 and PROMIS Fatigue Scale), depressive symptoms (Center for Epidemiological Studies Depression survey), and physical functioning (Short Physical Performance Battery) scores. All biomarkers underwent natural-log transformation.

RESULTS

There were 446 participants (mean age: 78 y ± 5; 44% women). In adjusted analyses, NT-proBNP was associated with AF-fatigue (β: 0.11, 95% CI: 0.03, 0.19), increased depressive symptoms (β: 0.44, 95% CI: 0.19, 0.70), and decreased physical function (β: -0.48, 95% CI: -0.72, -0.23). Hs-TnT was also associated with elevated AF-fatigue (β: 0.24, 95% CI: 0.09, 0.39) along with decreased physical function (β: -1.19, 95% CI: -1.64, -0.75). No significant associations were found with hsCRP and fatigue.

CONCLUSION

Increased levels of cardiac injury biomarkers, depressive symptoms, and decreased physical function were associated with AF-fatigue. Inflammation was not associated with AF-fatigue; other physiological pathways, such as cardiac overload or myocardial injury may be more relevant in AF-fatigue.

Diagnosis and Management of Atrial Fibrillation in Acute Ischemic Stroke in the Setting of Reperfusion Therapy: Insights and Strategies for Optimized Care

Reperfusion therapy in the form of intravenous thrombolysis (IVT) and endovascular thrombectomy (EVT) has revolutionised the field of stroke medicine. Atrial fibrillation (AF) patients constitute a major portion of the overall stroke population; however, the prevalence of AF amongst acute ischemic stroke (AIS) patients receiving reperfusion therapy remains unclear. Limitations in our understanding of prevalence in this group of patients are exacerbated by difficulties in appropriately diagnosing AF. Additionally, the benefits of reperfusion therapy are not consistent across all subgroups of AIS patients. More specifically, AIS patients with AF often tend to have poor prognoses despite treatment relative to those without AF. This article aims to present an overview of the diagnostic and therapeutic management of AF and how it mediates outcomes following stroke, most specifically in AIS patients treated with reperfusion therapy. We provide unique insights into AF prevalence and outcomes that could allow healthcare professionals to optimise the treatment and prognosis for AIS patients with AF. Specific indications on acute neurovascular management and secondary stroke prevention in AIS patients with AF are also discussed.

Atrial Fibrillation With Myocardial Infarction Presenting as a Progressive Worsening Fatigue in a Young Male

Atrial fibrillation (AF) is one of the most common arrhythmia exhibiting a dramatic rise in prevalence with associated increased risk of stroke, heart failure, and death. No standard symptoms have been categorized yet to set a gold standard in diagnosing this clinical attribute. A highly variable symptoms array has increased the challenges of management in terms of AF. An obvious relationship has not been established between symptoms and the onset or recurrence of arrhythmia. We present a case of a 43-year-old male patient who complained of chronic fatigue as a primary symptom and was diagnosed with AF with myocardial infarction.

Examining Adherence to Medication in Patients With Atrial Fibrillation: The Role of Medication Beliefs, Attitudes, and Depression

BACKGROUND/OBJECTIVES

This study examined whether beliefs about medicines, drug attitudes, and depression independently predicted anticoagulant and antiarrhythmic adherence (focusing on the implementation phase of nonadherence) in patients with atrial fibrillation (AF).

METHODS

This cross-sectional study was part of a larger longitudinal study. Patients with AF (N = 118) completed the Patient Health Questionnaire-8. The Beliefs about Medicines Questionnaire, Drug Attitude Inventory, and Morisky-Green-Levine Medication Adherence Scale (self-report adherence measure), related to anticoagulants and antiarrhythmics, were also completed. Correlation and multiple logistic regression analyses were conducted.

RESULTS

There were no significant differences in nonadherence to anticoagulants or antiarrhythmics. Greater concerns (r = 0.23, P = .01) were significantly, positively associated with anticoagulant nonadherence only. Depression and drug attitudes were not significantly associated with anticoagulant/antiarrhythmic adherence. Predictors reliably distinguished adherers and nonadherers to anticoagulant medication in the regression model, explaining 14% of the variance, but only concern beliefs (odds ratio, 1.20) made a significant independent contribution to prediction (χ = 11.40, P = .02, with df = 4). When entered independently into a regression model, concerns (odds ratio, 1.24) significantly explained 10.3% of the variance (χ = 7.97, P = .01, with df = 1). Regressions were not significant for antiarrhythmic medication (P = .30).

CONCLUSIONS

Specifying medication type is important when examining nonadherence in chronic conditions. Concerns about anticoagulants, rather than depression, were significantly associated with nonadherence to anticoagulants but not antiarrhythmics. Anticoagulant concerns should be targeted at AF clinics, with an aim to reduce nonadherence and potentially modifiable adverse outcomes such as stroke.

Trajectories of Recovery after Atrial Fibrillation Ablation

Ablation procedures are common for patients with atrial fibrillation (AF), yet evidence is limited about patient perceptions of their recovery following ablation. We sought to expand understanding of this recovery process. Twenty participants undergoing their first AF ablation completed semi-structured interviews prior to ablation (baseline) and at one, three, and six months post AF ablation. Pre-procedure education is modeled after education used for other ablation procedures, preparing patients to expect a single recovery trajectory. We identified two recovery trajectories that varied in speed of symptom resolution: sustained improvement and pseudo improvement. Recovery was slower than expected in both trajectories. Moreover, returning to desired activity levels consistently lagged behind other symptom resolution by approximately two months. A more accurate understanding of what patients experience post-ablation, as illustrated in these findings, serves as a beginning step to alter patient education prior to AF ablation to better prepare individuals for the recovery process.

Machine learning for subtype definition and risk prediction in heart failure, acute coronary syndromes and atrial fibrillation: systematic review of validity and clinical utility

BACKGROUND

Machine learning (ML) is increasingly used in research for subtype definition and risk prediction, particularly in cardiovascular diseases. No existing ML models are routinely used for cardiovascular disease management, and their phase of clinical utility is unknown, partly due to a lack of clear criteria. We evaluated ML for subtype definition and risk prediction in heart failure (HF), acute coronary syndromes (ACS) and atrial fibrillation (AF).

METHODS

For ML studies of subtype definition and risk prediction, we conducted a systematic review in HF, ACS and AF, using PubMed, MEDLINE and Web of Science from January 2000 until December 2019. By adapting published criteria for diagnostic and prognostic studies, we developed a seven-domain, ML-specific checklist.

RESULTS

Of 5918 studies identified, 97 were included. Across studies for subtype definition (n = 40) and risk prediction (n = 57), there was variation in data source, population size (median 606 and median 6769), clinical setting (outpatient, inpatient, different departments), number of covariates (median 19 and median 48) and ML methods. All studies were single disease, most were North American (n = 61/97) and only 14 studies combined definition and risk prediction. Subtype definition and risk prediction studies respectively had limitations in development (e.g. 15.0% and 78.9% of studies related to patient benefit; 15.0% and 15.8% had low patient selection bias), validation (12.5% and 5.3% externally validated) and impact (32.5% and 91.2% improved outcome prediction; no effectiveness or cost-effectiveness evaluations).

CONCLUSIONS

Studies of ML in HF, ACS and AF are limited by number and type of included covariates, ML methods, population size, country, clinical setting and focus on single diseases, not overlap or multimorbidity. Clinical utility and implementation rely on improvements in development, validation and impact, facilitated by simple checklists. We provide clear steps prior to safe implementation of machine learning in clinical practice for cardiovascular diseases and other disease areas.

Management of asymptomatic arrhythmias: a European Heart Rhythm Association (EHRA) consensus document, endorsed by the Heart Failure Association (HFA), Heart Rhythm Society (HRS), Asia Pacific Heart Rhythm Society (APHRS), Cardiac Arrhythmia Society of Southern Africa (CASSA), and Latin America Heart Rhythm Society (LAHRS)

Asymptomatic arrhythmias are frequently encountered in clinical practice. Although studies specifically dedicated to these asymptomatic arrhythmias are lacking, many arrhythmias still require proper diagnostic and prognostic evaluation and treatment to avoid severe consequences, such as stroke or systemic emboli, heart failure, or sudden cardiac death. The present document reviews the evidence, where available, and attempts to reach a consensus, where evidence is insufficient or conflicting.

Acute Coronary Syndrome Symptom Clusters: Illustration of Results Using Multiple Statistical Methods

Researchers have employed various methods to identify symptom clusters in cardiovascular conditions, without identifying rationale. Here, we test clustering techniques and outcomes using a data set from patients with acute coronary syndrome. A total of 474 patients who presented to emergency departments in five United States regions were enrolled. Symptoms were assessed within 15 min of presentation using the validated 13-item ACS Symptom Checklist. Three variable-centered approaches resulted in four-factor solutions. Two of three person-centered approaches resulted in three-cluster solutions. K-means cluster analysis revealed a six-cluster solution but was reduced to three clusters following cluster plot analysis. The number of symptoms and patient characteristics varied within clusters. Based on our findings, we recommend using (a) a variable-centered approach if the research is exploratory, (b) a confirmatory factor analysis if there is a hypothesis about symptom clusters, and (c) a person-centered approach if the aim is to cluster symptoms by individual groups.

Atrial fibrillation symptom clusters and associated clinical characteristics and outcomes: A cross-sectional secondary data analysis

BACKGROUND

Symptom clusters among adults with atrial fibrillation have previously been identified but no study has examined the relationship between symptom clusters and outcomes.

AIMS

The purpose of this study was to identify atrial fibrillation-specific symptom clusters, characterize individuals with each cluster, and determine whether symptom cluster membership is associated with healthcare utilization.

METHODS

This was a cross-sectional secondary data analysis of 1501 adults from the Vanderbilt Atrial Fibrillation Registry with verified atrial fibrillation. Self-reported symptoms were measured with the University of Toronto Atrial Fibrillation Severity Scale. We used hierarchical cluster analysis (Ward's method) to identify clusters and dendrograms, pseudo F, and pseudo T-squared to determine the ideal number of clusters. Next, we used regression analysis to examine the association between cluster membership and healthcare utilization.

RESULTS

Males predominated (67%) and the average age was 58.4 years. Two symptom clusters were identified, a Weary cluster (3.7%, n=56, fatigue at rest, shortness of breath at rest, chest pain, and dizziness) and an Exertional cluster (32.7%, n=491, shortness of breath with activity and exercise intolerance). Several sociodemographic and clinical characteristics varied by symptom cluster group membership, including age, gender, atrial fibrillation type, body mass index, comorbidity status, and treatment strategy. Women were more likely to experience either cluster ( p<0.001). The Weary cluster was associated with nearly triple the rate of emergency department utilization (incident rate ratio [IRR] 2.8, p<0.001) and twice the rate of hospitalizations (IRR 1.9, p<0.001).

CONCLUSION

We identified two symptom clusters. The Weary cluster was associated with a significantly increased rate of healthcare utilization.

Atrial Fibrillation Symptom Perception

Atrial fibrillation (AF), the most common cardiac arrhythmia, is associated with a significantly increased risk of ischemic stroke, heart failure, and death. AF is a heterogenous disease both in terms of the pathophysiologic mechanisms that lead to the disease, and in terms of symptom presentation. Although most patients with AF perceive symptoms, their symptom experience is highly variable. The purpose of this paper is to review the: 1) epidemiology and pathophysiology of AF, 2) symptoms associated with AF, and 3) implications for clinical practice based on disparate symptom perception.

Atrial fibrillation symptom profiles associated with healthcare utilization: A latent class regression analysis

BACKGROUND

Symptoms drive healthcare use among adults with atrial fibrillation, but limited data are available regarding which symptoms are most problematic and which patients are most at-risk. The purpose of this study was to: (1) identify clusters of patients with similar symptom profiles, (2) characterize the individuals within each cluster, and (3) determine whether specific symptom profiles are associated with healthcare utilization.

METHODS

We conducted a cross-sectional secondary data analysis of 1,501 adults from the Vanderbilt Atrial Fibrillation Registry. Participants were recruited from Vanderbilt cardiology clinics, emergency department, and in-patient services. Subjects included in our analysis had clinically verified atrial fibrillation and a completed symptom survey. Symptom and healthcare utilization data were collected with the University of Toronto Atrial Fibrillation Severity Scale. Latent class regression analysis was used to identify symptom clusters, with clinical and demographic variables included as covariates. We used Poisson regression to examine the association between latent class membership and healthcare utilization.

RESULTS

Participants were predominantly male (67%) with a mean age of 58.4 years (±11.9). Four latent classes were evident, including an Asymptomatic cluster (N = 487, 38%), Highly Symptomatic cluster (N = 142, 11%), With Activity cluster (N = 326, 25%), and Mild Diffuse cluster (N = 336, 26%). Highly Symptomatic membership was associated with the greatest rate of emergency department visits and hospitalizations (incident rate ratio 2.4, P < 0.001).

CONCLUSIONS

Clinically meaningful atrial fibrillation symptom profiles were identified that were associated with increased rates of emergency department visits and hospitalizations.

Factors associated with confidence in decision making and satisfaction with risk communication among patients with atrial fibrillation

Background:

Atrial fibrillation is a prevalent cardiac arrhythmia. Effective communication of risks (e.g. stroke risk) and benefits of treatment (e.g. oral anticoagulants) is crucial for the process of shared decision making.

Aim:

The aim of this study was to explore factors associated with confidence in decision making and satisfaction with risk communication after a follow-up visit among patients who three months earlier had visited an emergency room for atrial fibrillation related symptoms.

Methods:

A cross-sectional design was used and 322 patients (34% women), mean age 66.1 years (SD 10.5 years) with atrial fibrillation were included in the south of Sweden. Clinical examinations were done post an atrial fibrillation episode. Self-rating scales for communication (Combined Outcome Measure for Risk Communication and Treatment Decision Making Effectiveness), uncertainty in illness (Mishel Uncertainty in Illness Scale–Community), mastery of daily life (Mastery Scale), depressive symptoms (Hospital Anxiety and Depression Scale) and vitality, physical health and mental health (36-item Short Form Health Survey) were used to collect data.

Results:

Decreased vitality and mastery of daily life, as well as increased uncertainty in illness, were independently associated with lower confidence in decision making. Absence of hypertension and increased uncertainty in illness were independently associated with lower satisfaction with risk communication. Clinical atrial fibrillation variables or depressive symptoms were not associated with satisfaction with confidence in decision making or satisfaction with risk communication. The final models explained 29.1% and 29.5% of the variance in confidence in decision making and satisfaction with risk communication.

Conclusion:

Confidence in decision making is associated with decreased vitality and mastery of daily life, as well as increased uncertainty in illness, while absence of hypertension and increased uncertainty in illness are associated with risk communication satisfaction.

Symptom challenges after atrial fibrillation ablation

BACKGROUND

It is unclear what symptom challenges occur during the recovery phase after atrial fibrillation (AF) ablation.

OBJECTIVES

This longitudinal pilot study explored the patient perspective of the first six months following an AF ablation.

METHODS

Telephone interviews and questionnaires were used with 20 patients at baseline, at 1, 3, and 6 months after AF ablation. Telephone interview data were analyzed using content analysis. Longitudinal outcomes were analyzed using repeated measures analysis of variance (ANOVA).

RESULTS

Mean age was 65 ± 7 years and the sample was 55% female. The severity and duration of fatigue was the most concerning symptom. Patient expectations differed from providers' expectations. Recovery was a much slower process than patients expected.

CONCLUSIONS

Patients struggled to manage symptoms after AF ablation. A more accurate understanding of the symptom challenges following AF ablation could lead to development of more realistic education to improve patient self-management.