目的:本研究旨在构建一个预测模型,用于根据临床评估SLE患者神经精神性系统性红斑狼疮(NPSLE)的发展风险。实验室,和气象数据。
方法:共纳入2232例SLE患者,并将其随机分配到训练集和验证集。通过单变量和最小绝对收缩和选择算子(LASSO)逻辑回归模型筛选了诸如临床和实验室数据以及当地气象数据之类的变量。经过10倍交叉验证,采用多变量logistic回归建立预测模型,并构建列线图以可视化NPSLE的风险。通过受试者工作特征(ROC)曲线和校准曲线分析评估模型的有效性和准确性。通过决策曲线分析评估净临床获益。
结果:包含在预测模型中的变量是抗dsDNA,抗SSA,淋巴细胞计数,血细胞比容,红细胞沉降率,前白蛋白,视黄醇结合蛋白,肌酸激酶同工酶MB,N末端脑钠肽前体,肌酐,间接胆红素,纤维蛋白原,超敏C反应蛋白,CO,轻度污染。列线图显示了广泛的预测谱;训练集的曲线下面积(AUC)为0.895(0.858-0.931),验证集为0.849(0.783-0.916)。
结论:该模型表现出良好的预测性能,并将在NPSLE风险计算中提供临床益处。关键点•临床,实验室,和气象数据被纳入SLE患者神经精神性系统性红斑狼疮(NPSLE)的预测模型.•Anti-dsDNA,抗SSA,LYM,HCT,ESR,hsCRP,IBIL,PA,RBP,CO,Fib,NT-proBNP,Crea,CO,和轻度污染是NPSLE发展的预测因子,可能具有研究潜力。•列线图具有良好的预测性能和临床价值,可用于指导临床诊断和治疗。
OBJECTIVE: This study aimed to construct a predictive model for assessing the risk of development of neuropsychiatric systemic lupus erythematosus (NPSLE) among patients with SLE based on clinical, laboratory, and meteorological data.
METHODS: A total of 2232 SLE patients were included and were randomly assigned into training and validation sets. Variables such as clinical and laboratory data and local meteorological data were screened by univariate and least absolute shrinkage and selection operator (LASSO) logistic regression modelling. After 10-fold cross-validation, the predictive model was built by multivariate logistic regression, and a nomogram was constructed to visualize the risk of NPSLE. The efficacy and accuracy of the model were assessed by receiver operating characteristic (ROC) curve and calibration curve analysis. Net clinical benefit was assessed by decision curve analysis.
RESULTS: Variables that were included in the predictive model were anti-dsDNA, anti-SSA, lymphocyte count, hematocrit, erythrocyte sedimentation rate, pre-albumin, retinol binding protein, creatine kinase isoenzyme MB, Nterminal brain natriuretic peptide precursor, creatinine, indirect bilirubin, fibrinogen, hypersensitive C-reactive protein, CO, and mild contamination. The nomogram showed a broad prediction spectrum; the area under the curve (AUC) was 0.895 (0.858-0.931) for the training set and 0.849 (0.783-0.916) for the validation set.
CONCLUSIONS: The model exhibits good predictive performance and will confer clinical benefit in NPSLE risk calculation. Key Points • Clinical, laboratory, and meteorological data were incorporated into a predictive model for neuropsychiatric systemic lupus erythematosus (NPSLE) in SLE patients. • Anti-dsDNA, anti-SSA, LYM, HCT, ESR, hsCRP, IBIL, PA, RBP, CO, Fib, NT-proBNP, Crea, CO, and mild contamination are predictors of the development of NPSLE and may have potential for research. • The nomogram has good predictive performance and clinical value and can be used to guide clinical diagnosis and treatment.