Prediction of ESRD in IgA Nephropathy Patients from an Asian Cohort: A Random Forest Model
Prediction of ESRD in IgA Nephropathy Patients from an Asian Cohort: A Random Forest Model
复制标题
亚洲队列中 IgA 肾病患者的 ESRD 预测：随机森林模型
DOI：
10.1159/000495818
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发表时间：
2018-01-01
影响因子：
2.8
通讯作者：
Liu, Hong
中科院分区：
文献类型：
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作者：
Liu, Yexin;Zhang, Yan;Liu, Hong
背景/目的：亚洲免疫球蛋白A肾病（IgAN）患者发生终末期肾病（ESRD）的风险增加。亚洲IgAN患者ESRD预测的计算机辅助系统尚未得到很好的研究。方法：回顾性分析2009年1月至2013年11月湘雅第二医院肾内科经活检证实的IgAN患者。在肾活检后1个月内获得人口学和临床病理数据。采用随机森林（RF）模型预测IgAN患者的ESRD状态。所有病例最初都进行了培训和验证，利用了出袋（OOB）误差。根据RF模型中的基尼杂质指数选择模型中使用的预测因子，并通过logistic回归分析进行验证。采用受试者工作特征曲线下面积（AUC）和F-measure对射频模型进行评价。结果：本研究共纳入262例IgAN患者，中位随访时间4.66年。首先获得了预测因子在RF模型中的重要性排序，显示了一些最重要的预测因子。Logistic回归也显示这些因素与ESRD状态有统计学相关性。我们首先使用性别、年龄、高血压、血清肌酐、24小时蛋白尿和IgAN临床决策支持系统（CDSS, www.IgAN.net）建议的组织学分级来训练初始RF模型。该6个预测因子模型的f值为0.8，AUC为92.57%。通过加入Oxford-MEST分数，该模型的AUC（96.1%）和F-measure（0.823）均优于初始模型。合并C3染色后，AUC为97.29%，F-measure增加到0.83。增加肾小球滤过率（eGFR）可使AUC提高到95.45%。我们还观察到，添加尿素氮（BUN）、尿酸、血红蛋白和白蛋白后，模型的性能有所改善。结论：除了CDSS中的预测因子外，使用RF模型预测中国IgAN患者ESRD的Oxford-MEST评分、C3染色和eGFR提供了额外的信息。
Background/Aims: There is an increasing risk of end-stage renal disease (ESRD) among Asian people with immunoglobulin A nephropathy (IgAN). A computer-aided system for ESRD prediction in Asian IgAN patients has not been well studied. Methods: We retrospectively reviewed biopsy-proven IgAN patients treated at the Department of Nephrology of the Second Xiangya Hospital from January 2009 to November 2013. Demographic and clinicopathological data were obtained within 1 month of renal biopsy. A random forest (RF) model was employed to predict the ESRD status in IgAN patients. All cases were initially trained and validated, taking advantage of the out-of-bagging(OOB) error. Predictors used in the model were selected according to the Gini impurity index in the RF model and verified by logistic regression analysis. The area under the receiver operating characteristic(ROC) curve (AUC) and F-measure were used to evaluate the RF model. Results: A total of 262 IgAN patients were enrolled in this study with a median follow-up time of 4.66 years. The importance rankings of predictors of ESRD in the RF model were first obtained, indicating some of the most important predictors. Logistic regression also showed that these factors were statistically associated with ESRD status. We first trained an initial RF model using gender, age, hypertension, serum creatinine, 24-hour proteinuria and histological grading suggested by the Clinical Decision Support System for IgAN (CDSS, www.IgAN.net). This 6-predictor model achieved a F-measure of 0.8 and an AUC of 92.57%. By adding Oxford-MEST scores, this model outperformed the initial model with an improved AUC (96.1%) and F-measure (0.823). When C3 staining was incorporated, the AUC was 97.29% and F-measure increased to 0.83. Adding the estimated glomerular filtration rate (eGFR) improved the AUC to 95.45%. We also observed improved performance of the model with additional inputs of blood urea nitrogen (BUN), uric acid, hemoglobin and albumin. Conclusion: In addition to the predictors in the CDSS, Oxford-MEST scores, C3 staining and eGFR conveyed additional information for ESRD prediction in Chinese IgAN patients using a RF model.