Investigation of Nutritional Factors and Malnutrition Risk Prediction Model in Hospitalized Patients with Systemic Lupus Erythematosus in China.

Abstract

Introduction: Nutritional status is a critical indicator of overall health and immune function, significantly influencing treatment outcomes. Despite its importance, the nutritional status of patients with systemic lupus erythematosus (SLE) often receives insufficient attention. This study aims to evaluate the nutritional status of patients with SLE, identify factors associated with malnutrition, and develop a risk prediction model for malnutrition in this population.

Methods: We collected clinical data from a convenience sample of SLE patients at a general hospital in Ningxia Province, China, between January and December 2022. Univariate and multivariate logistic regression analyses were performed to determine the independent risk factors for malnutrition. A risk prediction model was constructed and evaluated using the receiver operating characteristic (ROC) curve.

Results: This study included 420 patients with SLE (mean age: 41.43 years, 91.7% women), of whom 46.2% were malnourished based on their serum albumin levels. Multivariate logistic regression analysis identified monthly income (OR=0.192, P<0.05), sleep quality (OR=2.559, P<0.05), kidney involvement (OR=4.269, P<0.05), disease activity (OR=2.743, P<0.05), leukocyte count (OR=1.576, P<0.05), lymphocyte count (OR=0.393, P<0.05), hemoglobin (OR=0.972, P<0.05), complement C3 (OR=0.802, P<0.05), and complement C4 (OR=0.493, P<0.05) as independent risk factors for malnutrition. The prediction model showed good predictive value with an area under the ROC curve of 0.895 (95% CI: 0.823-0.840), sensitivity of 0.907, and specificity of 0.827. The Hosmer-Lemeshow test indicated a good model fit (χ²=10.779, P=0.215).

Discussion: Malnutrition is a significant concern among SLE patients, influenced by a range of socioeconomic and clinical factors. Our risk prediction model, with its high sensitivity and specificity, provides a robust tool for early identification of malnutrition in this population. Implementing this model in clinical practice can guide healthcare providers in prioritizing at-risk patients, enabling proactive nutritional interventions that could potentially improve clinical outcomes, enhance quality of life, and reduce healthcare costs associated with SLE.