COPD phenotypes and machine learning cluster analysis: A systematic review and future research agenda

Area of Science:

• Pulmonary Medicine
• Computational Biology
• Biostatistics

Background:

• Chronic Obstructive Pulmonary Disease (COPD) is a complex, heterogeneous respiratory condition with significant global mortality.
• Current COPD phenotyping relies on clinical symptoms, but this approach has limitations in fully characterizing patient diversity.

Purpose of the Study:

• To systematically review the application of cluster analysis in identifying COPD phenotypes over the past decade.
• To evaluate the strengths and weaknesses of various cluster analysis methods used in COPD research.
• To identify research gaps and propose future directions for COPD phenotyping.

Main Methods:

• Systematic literature review of studies employing cluster analysis for COPD phenotyping.
• Analysis of integrated patient data including symptoms, exacerbations, comorbidities, biomarkers, and genomics.
• Assessment of methodological approaches and their effectiveness in identifying distinct COPD phenotypes.

Main Results:

• Cluster analysis, particularly with machine learning, offers advanced capabilities for COPD phenotyping.
• Integration of multi-dimensional patient data enhances the reliability of identifying new and existing COPD phenotypes.
• The review synthesizes evidence on the utility and limitations of various clustering techniques.

Conclusions:

• Cluster analysis holds significant promise for refining COPD classification, leading to improved diagnostic accuracy.
• Further research integrating comprehensive patient data with advanced analytical methods is crucial for developing targeted COPD treatments.
• This review provides a roadmap for future investigations into data-driven COPD phenotyping.