Severe infections remain common in a real-world rheumatoid arthritis cohort: A simple clinical model to predict infection risk.

OBJECTIVE

This study aimed to investigate the incidence of severe infections in patients of a dedicated rheumatoid arthritis (RA) clinic, identify the associated risk factors, and derive an infection risk screening tool.

METHODS

Between January and July 2019, 263 eligible patients with a diagnosis of RA were recruited retrospectively and consecutively from an RA clinic of an Australian tertiary hospital. The primary outcome was severe infection (requiring hospital admission) between January 2018 and July 2019. We collected data from medical records and pathology results. We used validated scores, such as the disease activity score of 28 joints (DAS28) and the Charlson comorbidity index, to assess the disease activity and comorbidity burden. Multivariable logistic regression was used for statistical analysis.

RESULTS

A total of 45 severe infection episodes occurred in 34 (13%) patients, corresponding to 10.8 infections per 100 patient-years. Respiratory (53%) and urinary (13%) tract infections were the most common. In the multivariable analysis, significant risk factors included low lymphocyte count (odds ratio [OR], 4.08; 95% confidence interval [CI], 1.16-14.29), severe infection in the past 3 years (OR, 3.58; 95% CI, 1.28-9.97), Charlson comorbidity index >2 (OR, 2.69; 95% CI, 1.03-7.00), and higher DAS28 (OR, 1.35/0.5-unit increment; 95% CI, 1.10-1.67). A model incorporating these factors and age had an area under receiver operating characteristic curve of 0.82.

CONCLUSION

To the best of our knowledge, this was one of the first Australian studies to evaluate severe infection rates in a real-world RA cohort. The rates remained high and comparable with those of the older studies. Lymphopenia, disease activity, comorbidity burden, and previous severe infection were the independent risk factors for infection. A model comprising easily assessable clinical and biological parameters has an excellent predictive potential for severe infection. Once validated, it may be developed into a screening tool to help clinicians rapidly identify the high-risk patients and inform the tailored clinical decision making.