This study explores the application of k-means cluster analysis to identify distinct asthma phenotypes. The research involved three independent asthma populations: one managed in primary care with mild to moderate disease (n = 184), another in secondary care with refractory asthma (n = 187), and a third with predominantly refractory asthma (n = 68) participating in a randomized trial. Cluster analysis revealed two common clusters across both populations: early-onset atopic and obese, noneosinophilic asthma. Two clusters specific to refractory asthma were characterized by marked discordance between symptom expression and eosinophilic airway inflammation: early-onset symptom predominant and late-onset inflammation predominant. Inflammation-guided management was superior for both discordant subgroups, leading to reduced exacerbation frequency in the inflammation-predominant cluster and reduced inhaled corticosteroid dose in the symptom-predominant cluster. Cluster analysis offers a novel multidimensional approach for identifying asthma phenotypes that exhibit differences in clinical response to treatment algorithms. The study highlights the importance of considering the multidimensionality of asthma in classification and treatment strategies. The findings suggest that cluster analysis can help identify subgroups with consistent disease patterns, which may inform targeted therapies and improve patient outcomes. The study also underscores the need for a robust classification system that incorporates the multidimensionality of asthma to better understand and manage refractory asthma.This study explores the application of k-means cluster analysis to identify distinct asthma phenotypes. The research involved three independent asthma populations: one managed in primary care with mild to moderate disease (n = 184), another in secondary care with refractory asthma (n = 187), and a third with predominantly refractory asthma (n = 68) participating in a randomized trial. Cluster analysis revealed two common clusters across both populations: early-onset atopic and obese, noneosinophilic asthma. Two clusters specific to refractory asthma were characterized by marked discordance between symptom expression and eosinophilic airway inflammation: early-onset symptom predominant and late-onset inflammation predominant. Inflammation-guided management was superior for both discordant subgroups, leading to reduced exacerbation frequency in the inflammation-predominant cluster and reduced inhaled corticosteroid dose in the symptom-predominant cluster. Cluster analysis offers a novel multidimensional approach for identifying asthma phenotypes that exhibit differences in clinical response to treatment algorithms. The study highlights the importance of considering the multidimensionality of asthma in classification and treatment strategies. The findings suggest that cluster analysis can help identify subgroups with consistent disease patterns, which may inform targeted therapies and improve patient outcomes. The study also underscores the need for a robust classification system that incorporates the multidimensionality of asthma to better understand and manage refractory asthma.