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IgA 肾病的肾脏结局预测和风险分层。

Prediction and Risk Stratification of Kidney Outcomes in IgA Nephropathy.

机构信息

National Clinical Research Center of Kidney Diseases, Jinling Clinical Medical College of Nanjing Medical University, Nanjing, China; National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China.

IBM Research-China, Beijing, China; Ping An Healthcare Technology, Beijing, China.

出版信息

Am J Kidney Dis. 2019 Sep;74(3):300-309. doi: 10.1053/j.ajkd.2019.02.016. Epub 2019 Apr 25.

Abstract

RATIONALE & OBJECTIVE: Immunoglobulin A nephropathy (IgAN) is common worldwide and has heterogeneous phenotypes. Predicting long-term outcomes and stratifying risk are important for clinical decision making and designing future clinical trials.

STUDY DESIGN

Multicenter retrospective cohort study of 2,047 patients with IgAN.

SETTING & PARTICIPANTS: Derivation and validation cohorts composed of 1,022 Chinese patients with IgAN from a single center and 1,025 patients with IgAN from 18 renal centers, respectively.

PREDICTORS

36 characteristics, including demographic, clinical, and pathologic variables.

OUTCOMES

Combined event of end-stage kidney disease or 50% reduction in estimated glomerular filtration rate within 5 years after diagnostic kidney biopsy.

ANALYTICAL APPROACH

A gradient tree boosting method implemented in the eXtreme Gradient Boosting (XGBoost) system was used to select the 10 most important variables from 36 candidate variables. Stepwise Cox regression analysis was used to derive a simplified scoring scale model (SSM) based on these 10 variables. Model discrimination and calibration were assessed using the C statistic and Hosmer-Lemeshow test. Risk stratification of the SSM was evaluated using Kaplan-Meier analysis.

RESULTS

In the derivation and validation cohorts, 74 and 114 patients reached the outcome, respectively. XGBoost predicted the outcome with a C statistic of 0.84 (95% CI, 0.80-0.88) for the validation cohort. The SSM included 3 variables: urine protein excretion, global sclerosis, and tubular atrophy/interstitial fibrosis. Using Kaplan-Meier analysis, the SSM identified significant risk stratification (P < 0.001).

LIMITATIONS

Retrospective study design, application for other ethnic groups needs to be verified.

CONCLUSIONS

A prediction model using routinely available characteristics and based on the combination of a machine learning algorithm and survival analysis can stratify risk for kidney disease progression in the setting of IgAN. An online calculator, the Nanjing IgAN Risk Stratification System, permits easy implementation of this model.

摘要

背景与目的

免疫球蛋白 A 肾病(IgAN)在全球范围内较为常见,且具有多种表型。预测长期结局并进行风险分层对临床决策和未来临床试验设计具有重要意义。

研究设计

这是一项多中心回顾性队列研究,纳入了 2047 例 IgAN 患者。

研究场所和参与者

分别来自单个中心的 1022 例 IgAN 患者和 18 个肾脏中心的 1025 例 IgAN 患者组成了本研究的推导队列和验证队列。

预测指标

36 项特征,包括人口统计学、临床和病理变量。

主要结局

诊断性肾活检后 5 年内终末期肾病或估算肾小球滤过率下降 50%的复合结局。

分析方法

采用极端梯度提升(XGBoost)系统中的梯度提升树算法从 36 个候选变量中选择 10 个最重要的变量。基于这 10 个变量,采用逐步 Cox 回归分析建立简化评分量表模型(SSM)。采用 C 统计量和 Hosmer-Lemeshow 检验评估模型的判别能力和校准度。采用 Kaplan-Meier 分析评估 SSM 的风险分层。

结果

在推导队列和验证队列中,分别有 74 例和 114 例患者达到了主要结局。XGBoost 对验证队列的预测准确率为 0.84(95%CI,0.80-0.88)。SSM 包括 3 个变量:尿蛋白排泄量、肾小球全球硬化和肾小管萎缩/间质纤维化。Kaplan-Meier 分析表明,SSM 可显著进行风险分层(P<0.001)。

局限性

这是一项回顾性研究设计,该模型在其他种族人群中的适用性尚待验证。

结论

基于机器学习算法和生存分析相结合的方法,使用常规临床特征建立的预测模型可对 IgAN 患者的肾脏疾病进展风险进行分层。该模型的在线计算器,即南京 IgAN 风险分层系统,易于实施。

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