Background: The increasing incidence of cutaneous melanoma (CM) is a significant public health issue. However, few studies have focused on how comorbidity patterns may influence the outcomes of CM patients. This study aimed to identify comorbidity patterns among CM patients and assess their impact on survival rates. Methods: This retrospective population-based cohort study included all CM patients recorded in the regional Veneto Cancer Registry in 2019 and 2021. Comorbidity data (ICD-9-CM coding) were obtained from hospital discharge records and included 17 primary disease categories. Patients with at least two documented conditions were clustered via latent class analysis (LCA), with the optimal number of clusters determined via the Akaike information criterion (AIC). Results: This population-based retrospective cohort study included 2,114 CM patients. Coexisting medical conditions were documented in 1,048 (49.6%) patients; multiple conditions were documented in 19.9% of the study cohort. Among these patients, the LCA identified three patterns: (1) cardio-endocrine-respiratory (20.96%); (2) pregnancy-psychosocial (29.97%); and (3) injury-multiorgan-multifactorial disorders (49.08%). Patients in the injury-multiorgan-multifactorial class had the highest mortality risk (HR = 3.08, 95% CI: 2.25–4.22). Conclusions: Comorbidity class has a significant effect on the survival of CM patients. The incorporation of the comorbidity profile into clinical care strategies can improve prognostic accuracy and enhance patient management.
Impact of comorbidities on survival in melanoma patients
Buja, Alessandra;Belloni Fortina, Anna;Mocellin, Simone
2025
Abstract
Background: The increasing incidence of cutaneous melanoma (CM) is a significant public health issue. However, few studies have focused on how comorbidity patterns may influence the outcomes of CM patients. This study aimed to identify comorbidity patterns among CM patients and assess their impact on survival rates. Methods: This retrospective population-based cohort study included all CM patients recorded in the regional Veneto Cancer Registry in 2019 and 2021. Comorbidity data (ICD-9-CM coding) were obtained from hospital discharge records and included 17 primary disease categories. Patients with at least two documented conditions were clustered via latent class analysis (LCA), with the optimal number of clusters determined via the Akaike information criterion (AIC). Results: This population-based retrospective cohort study included 2,114 CM patients. Coexisting medical conditions were documented in 1,048 (49.6%) patients; multiple conditions were documented in 19.9% of the study cohort. Among these patients, the LCA identified three patterns: (1) cardio-endocrine-respiratory (20.96%); (2) pregnancy-psychosocial (29.97%); and (3) injury-multiorgan-multifactorial disorders (49.08%). Patients in the injury-multiorgan-multifactorial class had the highest mortality risk (HR = 3.08, 95% CI: 2.25–4.22). Conclusions: Comorbidity class has a significant effect on the survival of CM patients. The incorporation of the comorbidity profile into clinical care strategies can improve prognostic accuracy and enhance patient management.| File | Dimensione | Formato | |
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