Classification of patients with osteoarthritis through clusters of comorbidities using 633,330 individuals from Spain

Abstract Objectives To explore clustering of comorbidities among patients with a new diagnosis of osteoarthritis (OA) and estimate the 10-year mortality risk for each identified cluster. Methods This is a population-based cohort study of individuals with first incident diagnosis of OA of the hip, knee, ankle/foot, wrist/hand, or ‘unspecified’ site between 2006 and 2020, using SIDIAP (a primary care database representative from Catalonia, Spain). At the time of OA diagnosis, conditions associated with OA in the literature that were found in ≥ 1% of the individuals (n = 35) were fitted into two cluster algorithms, K-means and latent class analysis (LCA). Models were assessed using a range of internal and external criteria evaluation procedures. Mortality risk of the obtained clusters was assessed by survival analysis using Cox proportional hazards. Results We identified 633 330 patients with a diagnosis of OA. Our proposed best solution used LCA to identify four clusters: ‘Low-morbidity (relatively low number of comorbidities), ‘Back/neck pain plus mental health’, ‘Metabolic syndrome’ and ‘Multimorbidity’ (higher prevalence of all study comorbidities). Compared with the ‘Low-morbidity, the ‘Multimorbidity’ cluster had the highest risk of 10-year mortality (adjusted HR: 2.19 [95%CI: 2.15–2.23]), followed by ‘Metabolic syndrome’ (adjusted HR: 1.24 [95%CI: 1.22–1.27]]) and ‘Back/neck pain plus mental health’ (adjusted HR: 1.12 [95%CI: 1.09–1.15]). Conclusion Patients with a new diagnosis of OA can be clustered into groups based on their comorbidity profile, with significant differences in 10-year mortality risk. Further research is required to understand the interplay between OA and particular comorbidity groups, and the clinical significance of such results.