Background Osteoarthritis (OA) patients are more likely to have other comorbidities (Swain, Sarmanova et al. 2020). Improving the understanding of comorbidity profiles of OA patients may lead to improvement in their clinical care.

Objectives To identify sub-groups in patients diagnosed with hip OA using patterns of comorbidity.

Methods Routinely-collected data of individuals ≥18 years with an incident diagnosis of hip OA (baseline/time of diagnosis), with at least 1 year of follow-up in SIDIAP (Information System for Research in Primary Care, a primary case database from Spain) were collected from January 1^st 2006 to June 31^st 2020. Those with soft-tissue disorders or other bone/cartilage diseases at the same joint in the year prior/after baseline were excluded. Comorbidities associated with OA in the literature and present in ≥1% of the study population were included. Clusters of comorbidities were identified at baseline using latent class analysis (LCA), a soft clustering method that classifies individuals according to the distribution of their measured items. The number of clusters or sub-groups within the study population was decided by comparing goodness of fit parameters (CAIC, BIC, ABIC) and log-likelihood changes of models from 2 to 8 clusters. The selected model was externally evaluated by a survival analysis assessing 10 years mortality within each cluster, where the weight of the posterior probability was used as a probability of sampling weight.

Results We identified 94,720 individuals with an incident diagnosis of hip OA, 56.3% women and 43.7% men, with a mean age (SD) of 67.2 (13.1) years. We selected the LCA model with 5 clusters that could be described as: healthier (lower prevalence of all comorbidities than average in the cohort), multimorbidity (higher prevalence of all comorbidities, multiple comorbidities), back/neck pain plus mental health (B/N-mental), cardiovascular disease (CVD), and metabolic syndrome (MetS) (Figure 1). Cox regression (HR [95CI%]) showed higher mortality risk for multimorbidity (3.76 [3.70-3.83]), CVD (1.56 [1.53-1.59]) and MetS (4.56 [4.35-4.78]), compared to healthy. No difference was observed for B/N-mental cluster.

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Figure 1.

Distribution of comorbidities within each cluster using latent class analysis. Clusters were described as Healthier, Multimorbidity, B/N-mental, CVD and MetS. Black horizontal lines represent the prevalence of the comorbidity before the clusterization. Abbreviations: Healthier, lower prevalence of all comorbidities; Multimorbidity, higher prevalence of all comorbidities; B/N-mental, back/neck pain plus mental health disorders; CVD, cardiovascular disease; Met, metabolic syndrome; Bhp, benign prostate hypertrophy; Chd, chronic heart disease; Chf, chronic heart failure; Ckd, chronic kidney disease; Copd, chronic obstructive pulmonary disease; Gbs, gall bladder stone; Gerd, gastroesophageal reflux disease; Ibd, inflammatory bowel disease; Ovd, other vessel diseases; Substance, substance abuse.

Conclusion Clustering of co-morbidities in hip OA patients at the time of diagnosis has the potential to detect sub-groups of hip OA patients who might require additional care.

References [1]Swain, S., A. Sarmanova, C. Coupland, M. Doherty and W. Zhang (2020). “Comorbidities in Osteoarthritis: A Systematic Review and Meta-Analysis of Observational Studies.” Arthritis Care Res (Hoboken) 72(7): 991-1000.

Acknowledgements We thank the Patient Research Participants (PRP) members Jenny Cockshull, Stevie Vanhegan, and Irene Pitsillidou for their involvement since the beginning of the project. We would like to thank the FOREUM for financially supporting the research.

Disclosure of Interests None declared