Risk assessment tool for anemia of chronic disease in systemic lupus erythematosus: a prediction model.

Abstract

This study aims to develop a predictive model for estimating the likelihood of anemia of chronic disease (ACD) in patients with systemic lupus erythematosus (SLE) and to elucidate the relationship between various factors and ACD METHODS: Individuals diagnosed with SLE for at least one year were enrolled and categorized into two groups: those with ACD and those without anemia symptoms. Patients were randomly assigned to training and test sets at an 8:2 ratio. The least absolute shrinkage and selection operator (LASSO) method was used to select predictors, followed by logistic regression for modeling. Model performance was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) for both training and test sets. The study included a total of 216 patients, with 172 in the training set and 44 in the test set. LASSO identified 6 variables for constructing the predictive model, resulting in an area under the curve (AUC) of 0.833 (95% CI, 0.773-0.892) in the training set and 0.861 (95% CI, 0.750-0.972) in the test set. Calibration curves indicated consistency between expected and observed probabilities. DCA indicated that the model yielded a net benefit with threshold probabilities ranging from 20% to 90% in the training set and from 10% to 80% in the test set. This study presents a predictive model for assessing the risk of ACD in SLE patients. The model effectively captures the underlying mechanism of ACD in SLE and empowers clinicians to make well-informed treatment adjustments. Key Points • Development of a New Predictive Model: This study introduces a new predictive model to evaluate the likelihood of anemia of chronic disease (ACD) in patients with systemic lupus erythematosus (SLE). The model utilizes routine laboratory parameters to identify high-risk individuals, addressing a significant gap in current clinical practice. • Reflection of Potential Mechanisms for ACD Development: By incorporating the factors needed to construct the predictive model, this study also sheds light on the potential mechanisms of ACD development in SLE patients.