Latent Class Analysis to Identify Novel Phenotypes in Exacerbations of COPD: A Retrospective, Multicenter Cohort Study

Abstract

This study aimed to identify novel phenotypes in patients with exacerbations of chronic obstructive pulmonary disease (ECOPD) to enable precise management, as current phenotypic classifications show limited utility in predicting patient prognosis. By analyzing data from a robust, retrospective multicenter registry (n = 13,449) and leveraging 133 biomarkers with penalized Cox models, we developed a six-phenotype latent class analysis model. Phenotype 1 is distinguished by elevated direct bilirubin (Dbil) and lactate dehydrogenase (LDH). Phenotype 2 features a higher percentage of lymphocytes (LYMPH_pct) and lower percentage of neutrophils (NEUT_pct). Phenotype 3 is marked by increased generalized cardiovascular disease (gCVD) and reduced NEUT_pct. Phenotype 4 is related to higher NEUT_pct and lower LYMPH_pct. Phenotype 5 is associated with a higher prevalence of gCVD and surgical trauma history. Phenotype 6 stands out for its higher rates of respiratory failure and elevated pulse at admission. Compared with Phenotype 1, patients in Phenotype 6 have a significantly higher risk of all-cause mortality in both the development and validation sets, with adjusted hazard ratios of 2.06 (95% CI: 1.38–3.08) and 2.51 (95% CI: 1.43–4.04), respectively. These findings reveal novel ECOPD subgroups with significant prognostic differences, providing a crucial framework for implementing precision health management and improving patient outcomes.

Keywords

ECOPD / latent class analysis / multicenter study / phenotypes / personalized medicine

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