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  • Author: Corrine E. Munoz-Plaza x
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Erin E. Hahn, Michael K. Gould, Corrine E. Munoz-Plaza, Janet S. Lee, Carla Parry and Ernest Shen

Background: Patients with colorectal cancer (CRC) commonly present at an older age with multiple comorbid conditions and complex care needs at the time of diagnosis. Clusters of comorbid conditions, or profiles, have not been systematically identified in this patient population. This study aimed to identify clinically distinct comorbidity profiles in a large sample of patients with CRC from an integrated healthcare system, and to examine the effect of comorbidity profiles on treatment and survival. Methods: In this retrospective cohort study, we used latent class analysis (LCA) to identify comorbidity profiles in a sample of 7,803 patients with CRC diagnosed between 2008 and 2013. We identified treatment received from electronic health records and used Cox proportional hazards analysis to examine the effect of comorbidity class on survival. We also examined the effect of comorbidity profile on receipt of guideline-recommended treatment. Results: Median age at diagnosis was 66 years, 52% of patients were male, and 48% were nonwhite. A plurality had stage 0–I disease (42%), with 22% stage II, 22% stage III, and 14% stage IV. More than half (59%) had ≤1 comorbid condition, whereas 19% had ≥4 comorbidities. LCA identified 4 distinct comorbidity classes. Classes were distinguished by the presence or absence of vascular and/or respiratory disease and diabetes with complications, as well as progressively greater Charlson comorbidity index scores. Comorbidity class was independently associated with treatment selection (P<.001) and survival (P<.001). Conclusions: Patients with CRC can be described by 4 distinct comorbidity profiles that are independent predictors of treatment and survival. These results provide insight into how comorbidities cluster within patients with CRC. This work represents a shift away from simple counting of comorbid conditions and toward a more nuanced understanding of how comorbidities cluster within groups of patients with CRC.