Background Rheumatoid arthritis patients are clinically complex, and the interplay of their prognostically important conditions leads to morbidity as well as mortality. Separate patterns of comorbidity are identified in patients with different rheumatic diseases. These patterns include the type of comorbid variables reported and their associations with age and disease duration. The available comorbidity tools face some tough challenges particularly most of them are non-specific and were developed before the era of biologic therapy, early inflammatory arthritis as well as Treat to Target concepts.
Objectives 1. identify comorbidities with greatest impact on Rheumatoid Arthritis (RA) patients' health status. 2. develop and validate a prospectively applicable comorbidity index for classifying RA patients according to their comorbid conditions which might alter their risk of hospitalization and mortality.
Methods This was a retrospective multicenter cohort analysis of RA patients in a rheumatology clinical registry assessing the effect of different comorbidities measured at patients' visits over 10-years period, on predicting future death and hospitalization. A weighted index that takes into account the number and seriousness of comorbid disease was developed in a cohort of 2029 patients with early RA monitored over 10-years. Logistic and Cox Regression analyses were implemented to estimate the risk of mortality. Regression coefficients were used to develop the morbidity index score. ROC curve for the invented index was used to evaluate the discriminating ability of the index and identify different cutoff values that can delineate patients at different stages for risk of death. Disease activity parameters were considered.
Results Comorbidities (18 conditions) were strongly associated with the 10-year death risk, and composed the RA-comorbidity index. These include Cardiovascular (7 comorbidities), infection, osteoporotic fractures, falls risk, Depression/anxiety, functional status (HAQ >2), diabetes mellitus, steroid therapy >5mg, DAS-28 >3.2), renal/liver/lung disease and tumors. A comorbidity index weighted according to the regression coefficient of the variables extracted through the logistic regression analysis was developed. The score ranges from 0 to 40. A cut off point of 20 was associated with a sensitivity of 90.2% and a specificity of 89.3%. Validation using ROC curve revealed AUC of 97%.
Conclusions The RA-comorbidity index is a valid method for estimating risk of death in RA patients. It enables the clinicians to include comorbidities assessment and management in their standard practice. It can be used to predict resource utilization, identify targets for reducing high costs, by prospectively identifying RA patients at high risk.
Acknowledgement Omar El Miedany: data entery and analysis
Disclosure of Interest None declared