Comorbidity Profiles and Their Effect on Treatment Selection and Survival among Patients with Lung Cancer.

RATIONALE: Prior work has shown that the comorbidity burden is high among patients with lung cancer, but patterns of comorbid conditions have not been systematically identified.
OBJECTIVES: We aimed to identify distinct comorbidity profiles in a large sample of patients with lung cancer and to examine the effect of comorbidity profiles on treatment and survival.
METHODS: In this retrospective cohort study, we used latent class analysis to identify comorbidity profiles (or classes) in a population-based sample of 6,662 patients with bronchogenic carcinoma diagnosed between 2008 and 2013. We included specific comorbid conditions from the Charlson comorbidity index. We used Cox proportional hazards analysis to examine the effect of comorbidity class on survival.
RESULTS: The mean age of the patients was 70 years, and 50% were female, 34% were nonwhite, and 17% were never-smokers. Most patients had stage III (21%) or IV (53%) disease. Over half (51%) had at least one comorbid condition, whereas 18% had at least four comorbidities. Latent class analysis identified five distinct comorbidity classes. Classes were defined by progressively greater Charlson comorbidity index scores and were further distinguished by the presence or absence of specific types of vascular disease and diabetes. Comorbidity class was independently associated with treatment selection (P < 0.001) and survival (P < 0.0001), especially among patients with stages 0-II disease (P < 0.0001).
CONCLUSIONS: Patients with lung cancer can be described by distinct comorbidity profiles that are independent predictors of treatment and survival. These profiles provide a more nuanced understanding of how comorbidities cluster within patients with lung cancer and how they can be applied for descriptive purposes or in research.