Machine learning model to estimate probability of remission in patients with idiopathic membranous nephropathy

列线图 医学 接收机工作特性 肾脏疾病 膜性肾病 内科学 蛋白尿
作者
Lijin Duo,Lei Chen,Yongdi Zuo,Jiulin Guo,Manrong He,Hongsen Zhao,Yingxi Kang,Wanxin Tang
出处
期刊:International Immunopharmacology [Elsevier BV]
卷期号:125: 111126-111126 被引量:5
标识
DOI:10.1016/j.intimp.2023.111126
摘要

Idiopathic membranous nephropathy (IMN) is a type of nephrotic syndrome and the leading cause of chronic kidney disease. As far as we know, no predictive model for assessing the prognosis of IMN is currently available. This study aims to establish a nomogram to predict remission probability in patients with IMN and assists clinicians to make treatment decisions.A total of 266 patients with histopathology-proven IMN were included in this study. Least absolute shrinkage and selection operator regression was utilized to identify the most important variables. Subsequently, multivariate Cox regression analysis was conducted to construct a nomogram, and bootstrap resampling was employed for internal validation. Receiver operating characteristic and calibration curves and decision curve analysis (DCA) were utilized to assess the performance and clinical utility of the developed model.A prognostic nomogram was established, which incorporated creatinine, glomerular_basement_membrane_thickening, gender, IgG_deposition, low-density lipoprotein cholesterol, and fibrinogen. The areas under the curves of the 3-, 12-, 24-month were 0.751, 0.725, and 0.830 in the training set, and 0.729, 0.730, and 0.948 in the validation set respectively. These results and calibration curves demonstrated the good discrimination and calibration of the nomogram in the training and validation sets. Additionally, DCA indicated that the nomogram was useful for remission prediction in clinical settings.The nomogram was useful for clinicians to evaluate the prognosis of patients with IMN in early stage.
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