医学
狼疮性肾炎
鉴定(生物学)
计算生物学
模式识别(心理学)
人工智能
生物信息学
疾病
病理
计算机科学
植物
生物
作者
Zhipeng Wang,Xiang Wang,Yiqin Wang,Jianwen Yu,Xin Wang,Hongjian Ye,Haishan Wu,Ruihan Tang,Xi Xia,Wei Chen
摘要
ABSTRACT Background and hypothesis Membranous lupus nephritis (MLN) traditionally includes class V (alone), and may be associated with other classes (III or IV). The clinical, therapeutic, and prognostic relevance of the classification remains controversial. Methods A retrospective cohort of 412 MLN patients from the First Affiliated Hospital of Sun-Yat Sen University was followed for a median of 65.68 (interquartile range 23.13–131.70) months. The primary outcomes were adverse renal events including all-cause death and ESRD. Phenotypes were identified and validated using unsupervised clustering analysis (K-means), principal component analysis and decision tree analysis. Results Distinct clinical and pathological differences were noted between the traditional class IV + V and classes V + III and V, while class V + III and class V exhibited high similarities in clinical features and prognosis (P = 0.074). K-means clustering revealed high-risk (n = 180) and low-risk (n = 232) groups, with significant differences in adverse renal outcomes (9.2% vs 4.1%, P < 0.001). To recognize the high-risk profile of MLN patients, a decision tree based on Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) score, hemoglobin, serum creatinine, traditional classification, and activity index of renal biopsy accurately clustered patients in the development (95.8% accuracy) and validation (87.1% accuracy) cohorts. Conclusions Two novel phenotypic clusters, more predictive than traditional classifications, enhance high-risk profile identification and prognostic accuracy.
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