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

Area of Science:

• Cardiology
• Patient Management
• Health Psychology

Background:

• Understanding patient characteristics in atrial fibrillation (AF) is crucial for optimizing management strategies.
• Identifying distinct patient profiles can aid in targeted interventions for improved outcomes.

Purpose of the Study:

• To identify and characterize distinct patient clusters within an atrial fibrillation (AF) population.
• To explore potential for tailored management approaches based on identified patient subgroups.

Main Methods:

• Secondary analysis of online survey and clinic referral data from 196 AF patients.
• Cluster analysis using 11 variables including CHA2DS2-VASc score, symptoms, mental health, and quality of life.
• Follow-up analyses to compare clinical variables across identified clusters.

Main Results:

• A 4-cluster solution revealed distinct patient groups: 'doing well', 'stressed and discontented', 'struggling and dissatisfied', and 'satisfied and complacent'.
• Two-thirds of patients (66%) were categorized as 'doing well', while 34% represented varying levels of difficulty managing AF.
• The 4-cluster model provided a nuanced view, differentiating subgroups among those not managing well.

Conclusions:

• Atrial fibrillation patients can be classified into clinically meaningful, natural groupings.
• Three distinct subgroups among poorly managing patients may benefit from personalized AF management and support.
• Tailoring treatments to personal, behavioral, and clinical patterns can enhance patient outcomes and satisfaction.