A Systematic Review of Asthma Phenotypes Derived by Data-Driven Methods

• Autor(a) principal: Francisco João Figueiredo Santos Cunha
• Data de Publicação: 2021
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: https://hdl.handle.net/10216/134478
• topic: Medicina clínica; Clinical medicine

Classification of asthma phenotypes has a potentially relevant impact on the clinical management of the disease. Methods for statistical classification without a priori assumptions (data-driven approaches) may contribute to develop a better comprehension of trait heterogeneity in disease phenotyping. This study aimed to summarize and characterize asthma phenotypes derived by data-driven methods. We performed a systematic review using three scientific databases, following PRISMA criteria. We included studies reporting adult asthma phenotypes derived by data-driven methods using easily accessible variables in clinical practice. Two independent reviewers assessed studies. The methodological quality of included primary studies was assessed using ROBINS-I tool. We retrieved 7186 results and included 68 studies of which 65% (n=44) used data from specialized centers and 53% (n=36) evaluated the consistency of phenotypes. The most frequent data-driven method was hierarchical cluster analysis (n=19). Three major asthma-related domains of easily measurable clinical variables used for phenotyping were identified: personal (n=49), functional (n=48), and clinical (n=47). The identified asthma phenotypes varied according to the sample's characteristics, variables included in the model and data availability. Overall, the most frequent phenotypes were related to atopy, gender, and severe disease. This review shows a large variability of asthma phenotypes derived from data-driven methods. Further research should include more population-based samples and assess longitudinal consistency of data-driven phenotypes.