基于机器学习的老年原发性膜性肾病预后预测模型构建
Development of A Prognostic Prediction Model for Primary Membranous Nephropathy in the Elderly Based on Machine Learning
摘要
Abstract
Objective Elderly patients with primary membranous nephropathy(PMN)exhibit significant prognostic heterogeneity and poor tolerance to immunotherapy.However,there is a lack of early prog-nostic prediction tools specifically for this population.This study aimed to develop a prognostic prediction model applicable to elderly PMN patients.Methods This study retrospectively included elderly patients with PMN con-firmed by renal biopsy.The primary endpoint was a adverse composite outcome including end-stage renal disease(ESRD),a≥50%decline in estimated glomerular filtration rate(eGFR),or all-cause death.Patients were randomly divided into a training cohort and a validation cohort at a ratio of 7∶3.Key prognostic features were i-dentified using least absolute shrinkage and selection operator(LASSO)regression combined with random survival forest,and a predictive model was constructed based on penalized Cox regression.Model performance was evaluated using the concordance index(C-index),time-dependent area under the receiver operating characteristic curve(AUROC),calibration curves,and decision curve analysis.The SurvSHAP(t)method was employed for interpretability analysis of the model.Results A total of 309 elderly patients with PMN were included in this study,with a median age of 65.00 years(IQR,62.00-68.00)and a male predominance 61.2%(189/309).During a median follow-up of 47.00 months(IQR,25.00-89.00),38.2%(118/309)reached the endpoint event.The final model included nine key features,including eGFR,total protein(TP),glomerular capsu-lar adhesion,urine glucose,segmental glomerulosclerosis proportion,fibrinogen,urea,age,and activated partial thromboplastin time(APTT).In the validation cohort,the model demonstrated good discrimination,with a C-index of 0.731(95%CI:0.652-0.797).The time-dependent AUROCs for predicting adverse outcomes at 3,5,and 10 years were 0.758(95%CI:0.614-0.901),0.781(95%CI:0.646-0.916),and 0.866(95%CI:0.740-0.993),respectively.Calibration curves demonstrated a high degree of concordance between predicted probabilities and actual event rates.Decision curve analysis confirmed the net clinical benefit of the model.SurvSHAP(t)analy-sis showed that eGFR,TP,glomerular capsular adhesion,urine glucose,and the proportion of segmental glomerular sclerosis were the top five variables contributing to the model.Conclusions This prognostic model effectively pre-dicts the risk of adverse outcomes in elderly patients with PMN in the internal validation cohort,offering a potential scientific basis for individualized risk stratification and treatment decision-making in this population.关键词
原发性膜性肾病/老年/预后/机器学习/可解释性预测模型Key words
primary membranous nephropathy/elderly/prognosis/machine learning/explainable prediction model分类
医药卫生引用本文复制引用
许玉珠,刘淑琴,王丁丁,陈崴,王欣..基于机器学习的老年原发性膜性肾病预后预测模型构建[J].协和医学杂志,2026,17(2):370-381,12.基金项目
国家自然科学基金(82170737,82370707) （82170737,82370707）
国家重点研发计划(2025YFC2511800) （2025YFC2511800）
四大慢病重大专项(2023ZD0509300) （2023ZD0509300）
广东省基础与应用基础研究重大项目(2023B0303000013) （2023B0303000013）
广东省自然科学基金(2023A1515010539) （2023A1515010539）
2024年度农业和社会发展科技专题-重点研发计划(2024B03J1337) National Natural Science Foundation of China(82170737,82370707) （2024B03J1337）
National Key Research and Development Program of China(2025YFC2511800) （2025YFC2511800）
Noncommunicable Chronic Diseases-National Science and Technology Major Project(2023ZD0509300) （2023ZD0509300）
Guangdong Major Project of Basic Research(2023B0303000013) （2023B0303000013）
Natural Science Foundation of Guangdong Province(2023A1515010539) （2023A1515010539）
2024 Guangzhou Science and Technology Fund for Agriculture and Social Development Special Topic(2024B03J1337) （2024B03J1337）
