Asthma clustering methods: a literature-informed application to the children's health study data

Abstract

Objective: The heterogeneity of asthma has inspired widespread application of statistical clustering algorithms to a variety of datasets for identification of potentially clinically meaningful phenotypes. There has not been a standardized data analysis approach for asthma clustering, which can affect reproducibility and clinical translation of results. Our objective was to identify common and effective data analysis practices in the asthma clustering literature and apply them to data from a Southern California population-based cohort of schoolchildren with asthma.

Methods: As of January 1, 2020, we reviewed key statistical elements of 77 asthma clustering studies. Guided by the literature, we used 12 input variables and three clustering methods (hierarchical clustering, k-medoids, and latent class analysis) to identify clusters in 598 schoolchildren with asthma from the Southern California Children's Health Study (CHS).

Results: Clusters of children identified by latent class analysis were characterized by exhaled nitric oxide, FEV₁/FVC, FEV₁ percent predicted, asthma control and allergy score; and were predictive of control at two year follow up. Clusters from the other two methods were less clinically remarkable, primarily differentiated by sex and race/ethnicity and less predictive of asthma control over time.

Conclusion: Upon review of the asthma phenotyping literature, common approaches of data clustering emerged. When applying these elements to the Children's Health Study data, latent class analysis clusters-represented by exhaled nitric oxide and spirometry measures-had clinical relevance over time.

Figure 1.

Article selection for the literature review, displayed using a PRISMA flow diagram

Figure 2.

Characterization of the clusters resulting from each of three clustering methods (HCLUST: hierarchal clustering, k-medoids, and LCA: latent class analysis), using heatmaps to represent the cluster-specific mean values* of input variables.^† Darker values indicate larger means. Input variables (rows) are ordered by p-values for differences across clusters. Clusters (columns) are labeled with the number of participants in that cluster. * Complementary numerical summaries are presented in Table E4. † Input variables: Male is a binary indicator for sex (male). For simple visual presentation, here we use a binary indicator of Hispanic rather than the nominal 3-level race/ethnicity variable (Hispanic, non-Hispanic White, Other) input to the clustering algorithms. BMI Category is the BMI percentile category, SHS is secondhand smoke, and % pred FEV1 is percent predicted FEV₁.

Figure 3.

Representation of the four clusters identified in the Children’s Health Study (CHS) by Latent Class Analysis (LCA), using a flow chart with decision points informed by a simplified classification (decision) tree. This simple flow chart did not exactly reproduce the clusters, but did yield an 81.7% classification accuracy within the 30% holdout test dataset.* * Modification of this flow chart to use decision points based on the clinically relevant values of FEV₁/FVC >= 85, FeNO <25, and FEV₁ ≤80% predicted yielded a classification accuracy of 73.3% in the test dataset.