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A nomogram for predicting the adverse pregnancy outcomes of systemic lupus erythematosus: a single-center study
Wei Kong1, Xin Zhang1, Linyu Geng1
1Department of Rheumatology and Immunology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, No. 321 Zhongshan Road, Nanjing, Jiangsu, 210008, China.
Objectives:
As systemic lupus erythematosus (SLE) primarily impacts women of childbearing age, a considerable number of patients have fertility needs. However, the risk of experiencing adverse pregnancy outcomes (APOs) was higher in these patients. Our study aimed to construct a predictive model to assess the risks for APOs of SLE.
Method:
We retrospectively analyzed the data of pregnant SLE patients hospitalized at Nanjing Drum Tower Hospital from August 2010 to April 2023. The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was used to explore the risk factors for APOs, and a nomogram was established. Afterward, the efficacy of the nomogram was evaluated by analyzing the areas under the curves (AUCs) of Receiver Operating Characteristic (ROC), calibration curves, and Decision Curve Analysis (DCA).
Results:
Our study involved 259 pregnant patients with a median age of 29.00 years, and identified 129 cases of APOs, including preterm birth, low birth weight, congenital anomalies, stillbirth/miscarriage, and fetal distress. Through LASSO regression analysis, nine optimal features were selected as risk factors, including age, lupus nephritis, antepartum body mass index, antinuclear antibody, anti-U1RNP/Sm antibody, anti-ribosomal P protein antibody, platelet, albumin levels, SLEDAI scores, diabetes mellitus, rash, and the use of aspirin therapy. These factors were integrated into a predictive nomogram model, which showed good predictive accuracy, with AUC values of 0.870 and 0.830 in training and validation groups, respectively. The calibration curves and DCA also confirmed the good performance of the model.
Conclusions:
We developed a tool to predict APOs in SLE patients, offering personalized risk assessments and clinical decision support. As the data used to build the predictive model was obtained from a single center, the tool is currently best suited for application within our center. Further validation in diverse populations is needed to expand its generalizability. Key Points • Our study revealed the independent predictors for APOs of SLE through LASSO regression analysis. • We developed a nomogram to predict APOs in SLE based on the results of LASSO regression analysis. • The predictive model may aid clinical decision-making, enabling timely interventions to reduce the incidence of APOs.
