Ann Am Thorac Soc. 2022 Jul 13. doi: 10.1513/AnnalsATS.202110-1127OC. Online ahead of print.
ABSTRACT
RATIONALE: Chronic obstructive pulmonary disease (COPD) is a heterogeneous syndrome, with phenotypic manifestations that tend to be distributed along a continuum. Unsupervised machine learning based on broad selection of imaging and clinical phenotypes may be used to identify primary variables that define disease axes and stratify patients with COPD.
OBJECTIVES: To identify primary variables driving COPD heterogeneity using principal component analysis (PCA), and to define disease axes and assess the prognostic value of these axes across three outcomes: progression, exacerbation, and mortality.
METHODS: We included 7331 patients between 39 and 85 years, of which 40.3% are black and 45.8% are female smokers with a mean of 44.6 pack years from the COPDGene Phase 1 cohort (2008-2011) in our analysis. Out of a total of 916 phenotypes, 147 continuous clinical, spirometric, and CT features were selected. For each component (PC), we computed a principal component score (PCS) based on feature weights. We used PCS distributions to define disease axes along which we divided the patients into quartiles. To assess the prognostic value of these axes, we applied logistic regression analyses to estimate 5-year (n=4159) and 10-year (n=1487) odds of progression. Cox regression and Kaplan-Meier analyses were performed to estimate 5-year and 10-year risk of exacerbation (n=6532) and all-cause mortality (n=7331).
RESULTS: The first PC, accounting for 43.7% of variance, was defined by CT measures of air trapping and emphysema. The second PC, accounting for 13.7% of variance, was defined by spirometric and CT measures of vital capacity and lung volume. The third PC, accounting for 7.9% of the variance, was defined by CT measures of lung mass, airway thickening, and body habitus. Stratification of patients across each disease axis revealed up to 3.2-fold [2.4, 4.3] greater odds of 5-year progression, 5.4-fold [4.6, 6.3] greater risk of 5-year exacerbation, and 5.0-fold [4.2, 6.0] greater risk of 10-year mortality between the highest and lowest quartiles.
CONCLUSIONS: Unsupervised learning analysis of the COPDGene cohort reveals CT measurements may bolster patient stratification along the continuum of COPD phenotypes. Each of the disease axes also individually demonstrate prognostic potential, predictive of future FEV1 decline, exacerbation, and mortality.
PMID:35830591 | DOI:10.1513/AnnalsATS.202110-1127OC
