Cluster and CART analyses identify large subgroups of adults with cystic fibrosis at low risk of 10-year death

Abstract

Our goal was to identify subgroups of adults with cystic fibrosis (CF) at low risk of death within 10\u2005years. Factor analysis for mixed data followed by Ward s cluster analysis was conducted using 25 variables from 1572 French CF adults in 2005. Rates of death by subgroups were analysed over 10\u2005years. An algorithm was developed using CART (classification and regression tree) analysis to provide rules for the identification of subgroups of CF adults with low rates of death within 10\u2005years. This algorithm was validated in 1376 Canadian CF adults. Seven subgroups were identified by cluster analysis in French CF adults, including two subgroups with low (∼5%) rates of death at 10\u2005years: one subgroup (22% of patients) was composed of patients with nonclassic CF, the other subgroup (17% of patients) was composed of patients with classic CF but low rates of Pseudomonas aeruginosa infection and diabetes. An algorithm based on CART analysis of data in 2005 allowed us to identify most French adults with low rates of death. When tested using data from Canadian CF adults in 2005, the algorithm identified 287 out of 1376 (21%) patients at low risk (10-year death: 7.7%). Large subgroups of CF adults share low risk of 10-year mortality. Large subgroups of adults with cystic fibrosis have very low rates of death at 10\u2005years http://ow.ly/PbS830n2Tkm