Risk factors analysis and prediction model building of infection in patients with autoimmune rheumatic disease

Abstract

Abstract Background Patients with autoimmune rheumatic diseases (AIRDs) are at increased risk of infection, and accurate assessment of infection risk can provide information for clinical decision making. This study is to identify the risk factors associated with infection in patients with AIRDs and develop a risk prediction model. Methods The clinical data of AIRDs inpatients was collected and retrospectively analyzed from January 2020 to December 2022. Univariate and multivariate Logistic regression analyses were employed to determine the independent risk factors of comorbid infection in AIRDs patients. A clinical prediction model was constructed and subsequently evaluated using the receiver operating characteristic (ROC) curve. Results A total of 281 cases of infection were observed in patients with AIRDs, with a positive sputum culture rate of 36.0%. Among these cases, 128 strains of pathogens were identified, including 72 strains of bacteria and 56 strains of fungi. Additionally, parasite eggs were detected in the stool samples of 2 patients. IgG and glucocorticoid therapy were independent factors influencing the occurrence of infection in patients with AIRDs. The prediction model incorporating IgG demonstrated an area under the receiver operating characteristic curve of 0.751 (95% CI: 0.552–0.951). IgG (≤ 12g/L) can serve as a valuable tool for evaluating the susceptibility to infection in AIRDs patients. Conclusion IgG reduction(≤ 12g/L) can serve as a predictive indicator for infection in AIRDs patients, which can assist clinical decision-making by proposing preventive strategies early to reduce infections.