Factors associated with low health-related quality of life among younger and older Thai patients with non-valvular atrial fibrillation

Bayesian Meta-Analysis of Health State Utility Values: A Tutorial with a Practical Application in Heart Failure

Researchers incorporate health state utility values as inputs to inform economic models. However, for a particular health state or condition, multiple utility values derived from different studies typically exist and a single study is often insufficient to represent the best available source of utility needed to inform policy decisions. The purpose of this paper is to provide an introductory guidance for conducting Bayesian meta-analysis of health state utility values to generate a single parameter input for economic evaluation, using R. The tutorial is illustrated using data from a systematic review of health state utilities of patients with heart failure, with 21 studies that reported utilities measured using the EuroQol-5D (EQ-5D). Explanations, key considerations and suggested readings are provided for each step of the tutorial, adhering to a clear workflow for conducting Bayesian meta-analysis: (1) setting-up the data; (2) employing methods to impute missing standard deviations; (3) defining the priors; (4) fitting the model; (5) diagnosing model convergence; (6) interpreting the results; and (7) performing sensitivity analyses. The posterior distributions for the pooled effect size (i.e. mean health state utility) and between-study heterogeneity are discussed and interpreted in light of the data, priors and models used. We hope that this tutorial will foster interest in Bayesian methods and their applications in the meta-analysis of utilities.

Self-care Behavior Based on Integrated Behavioral Model in Patients With Atrial Fibrillation: A Structural Equation Modeling Approach

BACKGROUND

There is limited research exploring the behavioral intentions, beliefs, and application of theoretical models in relation to self-care in patients with atrial fibrillation (AF).

OBJECTIVE

This study aimed to identify the factors that influence self-care behavior in patients with AF.

METHODS

The study used an integrated behavioral model and collected data from 216 patients diagnosed with AF. Data were analyzed using SPSS 24.0 and AMOS/WIN 24.0 to verify the fit of the hypothesis model, confirm factor analysis, and the validity of the hypothesis itself.

RESULTS

Self-care behavioral intention (β = 0.433, p < .001) and habit (β = 0.395, p = .005) had a significant direct effect, while instrumental attitude (β = 0.077, p = .045), injunctive norm (β = 0.084, p = .037), and self-efficacy (β = 0.249, p = .011) had a significant indirect effect on self-care behavior, explaining 64.4% of the variance.

CONCLUSION

The final model validated the factors that impact self-care behavior in patients with AF, highlighting the importance of fostering positive recognition of instrumental attitude, bolstering social influence and self-efficacy through significant individuals to improve self-care behavior. It is recommended to create an intervention program that encourages intentions and motivations for self-care behavior and incorporates tactics to make self-care behavior a habit. The study's path diagram can serve as a conceptual framework for designing strategies to enhance self-care behavior in patients with AF.

Characteristics associated with poor atrial fibrillation-related quality of life in adults with atrial fibrillation

PURPOSE

Few studies have examined the relationship between poor atrial fibrillation-related quality of life (AFQoL) and a battery of geriatric factors. The objective of this study is to describe factors associated with poor AFQoL in older adults with atrial fibrillation (AF) with a focus on sociodemographic and clinical factors and a battery of geriatric factors.

METHODS

Cross-sectional analysis of a prospective cohort study of participants aged 65+ with high stroke risk and AF. AFQoL was measured using the validated Atrial Fibrillation Effect on Quality of Life (score 0-100) and categorized as poor (<80) or good (80-100). Chi-square and t -tests evaluated differences in factors across poor AFQoL and significant characteristics ( P  < 0.05) were entered into a logistic regression model to identify variables related to poor AFQoL.

RESULTS

Of 1244 participants (mean age 75.5), 42% reported poor AFQoL. Falls in the past 6 months, pre/frail and frailty, depression, anxiety, social isolation, vision impairment, oral anticoagulant therapy, rhythm control, chronic obstructive pulmonary disease and polypharmacy were associated with higher odds of poor AFQoL. Marriage and college education were associated with a lower odds of poor AFQoL.

CONCLUSIONS

More than 4 out of 10 older adults with AF reported poor AFQoL. Geriatric factors associated with higher odds of reporting poor AFQoL include recent falls, frailty, depression, anxiety, social isolation and vision impairment. Findings from this study may help clinicians screen for patients with poor AFQoL who could benefit from tailored management to ensure the delivery of patient-centered care and improved well being among older adults with AF.