The disease management of long-term breast cancer survivors (BCS) is hampered by the scarce knowledge of multimorbidity patterns. The aim of our study was to identify multimorbidity clusters among long-term BCS and assess their impact on mortality and health services use. We conducted a retrospective study using electronic health records of 6512 BCS from Spain surviving at least 5 years. Hierarchical cluster analysis was used to identify groups of similar patients based on their chronic diagnoses, which were assessed using the Clinical Classifications Software. As a result, multimorbidity clusters were obtained, clinically defined and named according to the comorbidities with higher observed/expected prevalence ratios. Multivariable Cox and negative binomial regression models were fitted to estimate overall mortality risk and probability of contacting health services according to the clusters identified. 83.7% of BCS presented multimorbidity, essential hypertension (34.5%) and obesity and other metabolic disorders (27.4%) being the most prevalent chronic diseases at the beginning of follow-up. Five multimorbidity clusters were identified: C1-unspecific (29.9%), C2-metabolic and neurodegenerative (28.3%), C3-anxiety and fractures (9.7%), C4-musculoskeletal and cardiovascular (9.6%) and C5-thyroid disorders (5.3%). All clusters except C5-thyroid disorders were associated with higher mortality compared to BCS without comorbidities. The risk of mortality in C4 was increased by 64% (adjusted hazard ratio 1.64, 95% confidence interval 1.52-2.07). Stratified analysis showed an increased risk of death among BCS with 5 to 10 years of survival in all clusters. These results help to identify subgroups of long-term BCS with specific needs and mortality risks and to guide BCS clinical practice regarding multimorbidity.
Keywords: breast cancer; cluster analysis; electronic health records; multimorbidity; survival.
© 2021 UICC.