Identification of clinical phenotypes using cluster analyses in COPD patients with multiple comorbidities

Area of Science:

• Pulmonary Medicine
• Clinical Epidemiology
• Biostatistics

Background:

• Chronic obstructive pulmonary disease (COPD) presents significant heterogeneity in clinical manifestations and outcomes, even among patients with similar airflow limitation.
• Comorbidities are highly prevalent in COPD and are suspected contributors to this observed heterogeneity.
• Recent research employs advanced statistical methods like cluster analysis to identify distinct COPD patient subgroups (phenotypes).

Purpose of the Study:

• To review recent studies utilizing cluster analysis for defining COPD phenotypes in observational cohorts.
• To describe reproducible phenotypes, focusing on age and comorbidity differences, and discuss statistical approach limitations.
• To identify knowledge gaps and propose future research directions for COPD phenotyping.

Main Methods:

• Systematic review of observational cohort studies using cluster analysis to define COPD phenotypes.
• Evaluation of statistical methods, including strengths and weaknesses of cluster analysis.
• Synthesis of findings on reproducible phenotypes, with emphasis on age and comorbidity profiles.

Main Results:

• Cluster analysis has identified distinct COPD patient phenotypes with reproducible characteristics across studies.
• Key differentiating factors for these phenotypes include age and the presence of specific comorbidities, such as cardiovascular diseases.
• Prospective validation has been achieved for some identified phenotypes, supporting their clinical relevance.

Conclusions:

• Cluster analysis is a valuable tool for identifying clinically relevant COPD phenotypes.
• Phenotypes characterized by age and comorbidity profiles offer insights into disease heterogeneity.
• Further research is needed to address knowledge gaps and refine phenotyping strategies for improved COPD patient care.