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Objectives Our study purpose was to detect the distribution of anti-nuclear antibody (ANA) IgG subclasses in patients with systemic lupus erythematosus (SLE) and to evaluate their influence on the inflammatory process in SLE. Methods We determined the serum levels of ANA IgG subclasses from 70 SLE patients, 25 patients with other autoimmune diseases (OAD), and 25 healthy controls using ELISA. The serum level of total ANA IgG and the avidity of ANA IgG, dsDNA IgG, and dsDNA IgG subclasses were analysed by ELISA. Results The results indicated that levels of four ANA IgG subclasses (IgG1, IgG2, IgG3 and IgG4) and total IgG were significantly higher in SLE patients than in OAD patients and healthy controls ( p < 0.001). Moreover, the level of each ANA IgG subclass and the prevalence of high-avidity IgG ANAs (HA IgG ANAs) were significantly higher in the active cases than in the inactive cases of SLE and LN. Furthermore, level of ANA IgG subclasses decreased as level of dsDNA IgG subclasses decreased in 30 patients with SLE. In comparison, ANA IgG3 was significantly effective in high-dose prednisone combined with hydroxychloroquine ( p = 0.025). Additionally, it revealed that level of dsDNA IgG had a significant influence on four ANA IgG subclasses, especially on ANA IgG3 (β coefficient = 0.649, p < 0.001). Level of ANA IgG3 was also positively related to the serum level of dsDNA IgG (r = 0.729, p < 0.001) and RAI of HA IgG ANAs (r = 0.504, p < 0.001). However, the level of ANA IgG4 was positively related to the serum level of albumin (r = 0.572, p < 0.001) and RAI of HA IgG ANAs (r = 0.549, p < 0.001). Moreover, the results revealed that cutaneous and renal involvement were mainly associated with the ANA IgG1 and IgG4 subclasses. Although, arthritic involvement was mainly associated with ANA IgG3. Conclusions First, we demonstrated that the ANA IgG subclasses were diagnostic tools in SLE patients. Furthermore, HA IgG ANAs might affect the distribution of ANA IgG3 and IgG4. Moreover, ANA IgG3 might play a particular role in the activity of SLE disease and therapy. Therefore, an altered ANA IgG subclass distribution might be a risk factor influencing the inflammatory process in SLE.

Background Sepsis is a leading cause of death in patients with trauma, and the risk of mortality increases significantly for each hour of delay in treatment. A hypermetabolic baseline and explosive inflammatory immune response mask clinical signs and symptoms of sepsis in trauma patients, making early diagnosis of sepsis more challenging. Machine learning–based predictive modeling has shown great promise in evaluating and predicting sepsis risk in the general intensive care unit (ICU) setting, but there has been no sepsis prediction model specifically developed for trauma patients so far. Objective To develop a machine learning model to predict the risk of sepsis at an hourly scale among ICU-admitted trauma patients. Methods We extracted data from adult trauma patients admitted to the ICU at Beth Israel Deaconess Medical Center between 2008 and 2019. A total of 42 raw variables were collected, including demographics, vital signs, arterial blood gas, and laboratory tests. We further derived a total of 485 features, including measurement pattern features, scoring features, and time-series variables, from the raw variables by feature engineering. The data set was randomly split into 70% for model development with stratified 5-fold cross-validation, 15% for calibration, and 15% for testing. An Extreme Gradient Boosting (XGBoost) model was developed to predict the hourly risk of sepsis at prediction windows of 4, 6, 8, 12, and 24 hours. We evaluated model performance for discrimination and calibration both at time-step and outcome levels. Clinical applicability of the model was evaluated with varying levels of precision, and the potential clinical net benefit was assessed with decision curve analysis (DCA). A Shapley additive explanation algorithm was applied to show the effect of features on the prediction model. In addition, we trained an L2-regularized logistic regression model to compare its performance with XGBoost. Results We included 4603 trauma patients in the study, 1196 (26%) of whom developed sepsis. The XGBoost model achieved an area under the receiver operating characteristics curve (AUROC) ranging from 0.83 to 0.88 at the 4-to-24-hour prediction window in the test set. With a ratio of 9 false alerts for every true alert, it predicted 73% (386/529) of sepsis-positive timesteps and 91% (163/179) of sepsis events in the subsequent 6 hours. The DCA showed our model had a positive net benefit in the threshold probability range of 0 to 0.6. In comparison, the logistic regression model achieved lower performance, with AUROC ranging from 0.76 to 0.84 at the 4-to-24-hour prediction window. Conclusions The machine learning–based model had good discrimination and calibration performance for sepsis prediction in critical trauma patients. Using the model in clinical practice might help to identify patients at risk of sepsis in a time window that enables personalized intervention and early treatment.