Cunha Francisco, Amaral Rita, Jacinto Tiago, Sousa-Pinto Bernardo, Fonseca João A
Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal.
Center for Health Technology and Services Research (CINTESIS), Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal.
Diagnostics (Basel). 2021 Apr 2;11(4):644. doi: 10.3390/diagnostics11040644.
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 developing 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 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (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 the ROBINS-I tool. We retrieved 7446 results and included 68 studies of which 65% ( = 44) used data from specialized centers and 53% ( = 36) evaluated the consistency of phenotypes. The most frequent data-driven method was hierarchical cluster analysis ( = 19). Three major asthma-related domains of easily measurable clinical variables used for phenotyping were identified: personal ( = 49), functional ( = 48) and clinical ( = 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.
哮喘表型的分类对该疾病的临床管理具有潜在的相关影响。无先验假设的统计分类方法（数据驱动方法）可能有助于更好地理解疾病表型中的特征异质性。本研究旨在总结和描述通过数据驱动方法得出的哮喘表型。我们按照系统评价和Meta分析的首选报告项目（PRISMA）标准，使用三个科学数据库进行了系统评价。我们纳入了报告通过数据驱动方法得出的成人哮喘表型的研究，这些方法使用临床实践中易于获取的变量。两名独立评审员对研究进行了评估。使用ROBINS-I工具评估纳入的原始研究的方法学质量。我们检索到7446条结果，纳入了68项研究，其中65%（n = 44）使用了来自专业中心的数据，53%（n = 36）评估了表型的一致性。最常用的数据驱动方法是层次聚类分析（n = 19）。确定了用于表型分析的易于测量的临床变量的三个主要哮喘相关领域：个人因素（n = 49）、功能因素（n = 48）和临床因素（n = 47）。所确定的哮喘表型因样本特征、模型中包含的变量和数据可用性而异。总体而言，最常见的表型与特应性、性别和严重疾病有关。本综述显示，通过数据驱动方法得出的哮喘表型存在很大差异。进一步的研究应纳入更多基于人群的样本，并评估数据驱动表型的纵向一致性。
