Managing With Atrial Fibrillation: An Exploratory Model-Based Cluster Analysis of Clinical and Personal Patient Characteristics.

Background: Examining characteristics of patients with atrial fibrillation (AF) has the potential to help in identifying groups of patients who might benefit from different management approaches. Methods: Secondary analysis of online survey data was combined with clinic referral data abstraction from 196 patients with AF attending an AF specialty clinic. Cluster analyses were performed to identify distinct, homogeneous clusters of AF patients defined by 11 relevant variables: CHA2DS2-VASc score, age, AF symptoms, overall health, mental health, AF knowledge, perceived stress, household and recreation activity, overall AF quality of life, and AF symptom treatment satisfaction. Follow-up analyses examined differences between the cluster groups in additional clinical variables. Results: Evidence emerged for both 2- and 4-cluster solutions. The 2-cluster solution involved a contrast between patients who were doing well on all variables (n = 129; 66%) vs those doing less well (n = 67; 34%). The 4-cluster solution provided a closer-up view of the data, showing that the group doing less well was split into 3 meaningfully different subgroups of patients who were managing in different ways. The final 4 clusters produced were as follows: (i) doing well; (ii) stressed and discontented; (iii) struggling and dissatisfied; and (iv) satisfied and complacent. Conclusions: Patients with AF can be accurately classified into distinct, natural groupings that vary in clinically important ways. Among the patients who were not managing well with AF, we found 3 distinct subgroups of patients who may benefit from tailored approaches to AF management and support. The tailoring of treatment approaches to specific personal and/or behavioural patterns, alongside clinical patterns, holds potential to improve patient outcomes (eg, treatment satisfaction).

Contexte: L'examen des caractéristiques des patients atteints de fibrillation auriculaire (FA) pourrait permettre de mieux cerner les groupes qui pourraient bénéficier de différentes approches de prise en charge. Méthodologie: Nous avons combiné une analyse secondaire de données issues d'un sondage en ligne et les données issues de l'orientation clinique de 196 patients atteints de FA d'une clinique spécialisée en FA. Des analyses par grappes ont été réalisées pour cerner des groupes homogènes et distincts de patients atteints de FA, définis grâce à 11 variables pertinentes : score CHA2DS2-VASc, âge, symptômes de FA, état de santé général, état de santé mentale, niveau de connaissances sur la FA, niveau de stress perçu, activités récréatives et domestiques, qualité de vie générale avec la FA, et satisfaction concernant le traitement des symptômes de FA. Des ana-lyses ultérieures ont permis de se pencher sur les différences entre les groupes pour d'autres variables cliniques. Résultats: Deux solutions de regroupement des patients sont apparues possibles à l'analyse : en 2 groupes ou en 4 groupes. Le regroupement en 2 groupes mettait en relief le contraste entre les patients qui avaient des résultats favorables pour tous les paramètres (n = 129; 66 %) et ceux qui avaient des résultats moins favorables (n = 67; 34 %). Le regroupement en 4 groupes permettait d'observer les données plus en détail, et démontrait que le groupe avec des résultats moins favorables se subdivisait en 3 sous-groupes avec des distinctions pertinentes, qui vivaient leur maladie de façon différente. Les 4 groupes finaux étaient les suivants : (i) patients avec une expérience positive; (ii) patients vivant du stress et du mécontentement; (iii) patients vivant des difficultés et une insatisfaction; et (iv) patients vivant une satisfaction complaisante. Conclusions: Les patients atteints de FA peuvent être classés avec exactitude dans des groupes naturels distincts dont les différences sont d'intérêt clinique. Parmi les patients chez qui la prise en charge de la FA n'est pas optimale, il existe 3 sous-groupes différents qui pourraient tirer profit d'une approche de soutien et de prise en charge adaptée à leur profil. La personnalisation des approches thérapeutiques selon le type de comportements et de traits de personnalité, en plus du tableau clinique, pourrait permettre d'améliorer les résultats des patients (p. ex. la satisfaction par rapport au traitement).

Quality of life among patients with atrial fibrillation: A theoretically-guided cross-sectional study.

BACKGROUND: Patients with atrial fibrillation (AF) have significantly lower health-related quality of life (HRQoL) compared to the general population and patients with other heart diseases. The research emphasis on the influence of AF symptoms on HRQoL overshadows the role of individual characteristics. To address this gap, this study's purpose was to test an incremental predictive model for AF-related HRQoL following an adapted HRQoL conceptual model that incorporates both symptoms and individual characteristics. METHODS: Patients attending an AF specialty clinic were invited to complete an online survey. Hierarchical regression analyses were conducted to examine whether individual characteristics (overall mental health, perceived stress, sex, age, AF knowledge, household and recreational physical activity) incremented prediction of HRQoL and AF treatment satisfaction beyond AF symptom recency and overall health. RESULTS: Of 196 participants (mean age 65.3 years), 63% were male and 90% were Caucasian. Most reported 'excellent' or 'good' overall and mental health, had high overall AF knowledge scores, had low perceived stress scores, and had high household and recreation physical activity. The mean overall AF Effect On Quality-Of-Life Questionnaire (AFEQT) and AF treatment satisfaction scores were 70.62 and 73.84, respectively. Recency of AF symptoms and overall health accounted for 29.6% of the variance in overall HRQoL and 20.2% of the variance in AF treatment satisfaction. Individual characteristics explained an additional 13.6% of the variance in overall HRQoL and 7.6% of the variance in AF treatment satisfaction. Perceived stress and household physical activity were the largest contributors to overall HRQoL, whereas age and AF knowledge made significant contributions to AF treatment satisfaction. CONCLUSIONS: Along with AF symptoms and overall health, individual characteristics are important predictors of HRQoL and AF treatment satisfaction in AF patients. In particular, perceived stress and household physical activity could further be targeted as potential areas to improve HRQoL.

Telehealth Satisfaction in Patients Receiving Virtual Atrial Fibrillation Care: Quantitative Exploratory Study.

BACKGROUND: Telehealth can optimize access to specialty care for patients with atrial fibrillation (AF). Virtual AF care, however, may not fit with the complex needs of patients with AF. OBJECTIVE: This study aims to explore the correlation among attitudes toward health care technologies, self-efficacy, and telehealth satisfaction as part of the future planning of virtual AF clinic care. METHODS: Patients with AF older than 18 years from an urban-based, highly specialized AF clinic who had an upcoming telehealth visit were invited to participate in a web-based survey. The survey asked about demographic characteristics; use of technology; general, computer, and health care technology self-efficacy (HTSE) and health care technology attitudes, using a validated 30-item tool; and telehealth satisfaction questionnaire using a validated 14-item questionnaire. Data were analyzed with descriptive statistics, correlational analyses, and linear regression modeling. RESULTS: Participants (n=195 of 579 invited, for a 34% response rate) were primarily older, male, and White, had postsecondary schooling or more, and had high self-reported overall and mental health ratings. A variety of technologies were used in their daily lives and for health care, with the majority of technologies comprising desktop and laptop computers, smartphones, and tablets. Self-efficacy and telehealth satisfaction questionnaire scores were high overall, with male participants having higher general self-efficacy, computer self-efficacy, HTSE, and technology attitude scores. After controlling for age and sex, only HTSE was significantly related to individuals' attitudes toward health care technology. Both general self-efficacy and attitude toward health care technology were positively related to telehealth satisfaction. CONCLUSIONS: Consistent with a previous study, only HTSE significantly influenced attitudes toward health care technology. This finding confirms that, in this regard, self-efficacy is not a general perception but is domain specific. Considering participants' predominant use of the telephone for virtual care, it follows that general self-efficacy and attitude toward health care technology were significant contributors to telehealth satisfaction. Given our patients' frequent use of technology and high computer self-efficacy and HTSE scores, the use of video for telehealth appointments could be supported.

Usability and Feasibility Testing of an Atrial Fibrillation Educational Website with Patients Referred to an Atrial Fibrillation Specialty Clinic.

BACKGROUND: The purpose of this study was to design, usability test, and explore the feasibility of a web-based educational platform/intervention for patients with atrial fibrillation (AF) as part of their virtual AF care. METHODS: Participants were patients attending a specialized AF clinic. The multiple mixed-methods design included website design, think-aloud usability test, 1-month unstructured pre-testing analysis using Google Analytics, follow-up interviews, and a non-randomized one-group feasibility test using pre/post online surveys and Google Analytics. RESULTS: Usability testing participants (n = 2) guided adjustments for improving navigation. Pre-testing participants' (n = 9) website activity averaged four sessions (SD = 2.6) at 10 (SD 8) minutes per session during a 1-month study period. In the feasibility test, 30 patients referred to AF specialty clinic care completed the baseline survey, and 20 of these completed the 6-month follow-up survey. A total of 19 patients accessed the website over the 6 months, and all 30 participants were sent email prompts containing information from the website. Health-related quality of life, treatment satisfaction, household activity, and AF knowledge scores were higher at follow-up than baseline. There was an overall downward trend in self-reported healthcare utilization at follow-up. CONCLUSIONS: Access to a credible education website for patients with AF has great potential to complement virtual and hybrid models of care.

Patients' Experiences With the Fit of Virtual Atrial Fibrillation Care During the Pandemic: Qualitative Descriptive Study.

BACKGROUND: In-person health care has been the standard model of care delivery for patients with atrial fibrillation (AF). Despite the growing use of remote technology, virtual health care has received limited formal study in populations with AF. Understanding the virtual care experiences of patients in specialized AF clinics is essential to inform future planning of AF clinic care. OBJECTIVE: This qualitative descriptive study aimed to understand patients' virtual AF clinic care experiences during the COVID-19 pandemic. METHODS: Participants were recruited from a pool of patients who were receiving care from an AF clinic and who were enrolled in a larger survey study. A total of 8 virtual focus groups (n=30) were conducted in 2 waves between March 2021 and May 2021. Facilitators used a semistructured discussion guide to ask participants questions about their experiences of virtual care and the perceived quality of virtual care and technology support. Three team members initially open coded group data to create a preliminary coding framework. As the analysis progressed, with subsequent focus groups, the code clusters were refined. RESULTS: The participants were primarily male (21/30, 70%), aged ≥65 years (20/30, 67%), and college graduates (22/30, 73%). Patients found virtual care to be highly beneficial. Central to their experiences of virtual care was its fit or lack of fit with their health needs, which was integrally connected to communication effectiveness and their preferred virtual care future. Practical benefits included flexibility, convenience, and time and cost savings of virtual care. Virtual care fit occurred for small, quick, and mundane issues (eg, medication refills) but was suboptimal for new and more complex issues that patients thought warranted an in-person visit. Fit often reflected the effectiveness of communication between patient and provider and that of in-clinic follow-up. There was near-complete agreement among participants on the acceptability of virtual communication with their providers in addressing their needs, but this depended on adequate reciprocal communication. Without the benefit of in-person physical assessments, patients were uncertain and lacked confidence in communicating the needed, correct, and comprehensive information. Finally, participants described concerns related to ongoing virtual care with recommendations for their preferred future using a hybrid model of care and integrating patient-reported data (ie, blood pressure measurements) in virtual care delivery. CONCLUSIONS: Virtual care from a specialty AF clinic provides practical benefits for patients, but they must be weighed against the need for virtual care's fit with patients' needs and problems. The stability and complexity of patients' health needs, their management, and their perceptions of communication effectiveness with providers and clinics must be considered in decisions about appointment modality. Patients' recommendations for future virtual care through use of hybrid models together with systems for data sharing have the potential to optimize fit.