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. 2018 Jun 5;131(11):1275-1281.
doi: 10.4103/0366-6999.232809.

A Predictive Model for Estimation Risk of Proliferative Lupus Nephritis

Dong-Ni Chen  1 , Li Fan  1 , Yu-Xi Wu  1 , Qian Zhou  2 , Wei Chen  1 , Xue-Qing Yu  3
Affiliations

A Predictive Model for Estimation Risk of Proliferative Lupus Nephritis

Dong-Ni Chen et al. Chin Med J (Engl). .

Abstract
in English, Chinese

Background: Lupus nephritis (LN) is classified by renal biopsy into proliferative and nonproliferative forms, with distinct prognoses, but renal biopsy is not available for every LN patient. The present study aimed to establish an alternate tool by building a predictive model to evaluate the probability of proliferative LN.

Methods: In this retrospective cohort with biopsy-proven LN, 382 patients in development cohort, 193 in internal validation cohort, and 164 newly diagnosed patients in external validation cohort were selected. Logistic regression model was established, and the concordance statistics (C-statistics), Akaike information criterion (AIC), integrated discrimination improvement, Hosmer-Lemeshow test, and net reclassification improvement were calculated to evaluate the performance and validation of models.

Results: The prevalence of proliferative LN was 77.7% in the whole cohort. A model, including age, gender, systolic blood pressure, hemoglobin, proteinuria, hematuria, and serum C3, performed well on good-of-fit and discrimination in the development chohort to predict the risk of proliferative LN (291 for AIC and 0.84 for C-statistics). In the internal and external validation cohorts, this model showed good capability for discrimination and calibration (0.84 and 0.82 for C-statistics, and 0.99 and 0.75 for P values, respectively).

Conclusion: This study developed and validated a model including demographic and clinical indices to evaluate the probability of presenting proliferative LN to guide therapeutic decisions and outcomes.

评价增殖型狼疮性肾炎风险的预测模型研究摘要背景：狼疮性肾炎通过肾活检可分为增殖型狼疮肾炎和非增殖型狼疮肾炎，两者预后截然不同。然而并不是每位患者都能够进行肾活检。本研究旨在通过建立一个评价增殖型狼疮肾炎风险的预测模型，为不能进行肾活检的狼疮患者提供评估肾脏损伤的方法。 方法：本研究数据来自肾活检证实的狼疮肾炎回顾性队列，随机选取382例患者作为建模队列，193例患者作为内部验证队列，164例新诊断的狼疮肾炎患者作为外部验证队列。构建Logistic模型，并计算C统计量、AIC检验模型拟合优度，计算IDI、NRI检验模型再分类与预测能力。并在外部验证队列中验证最佳模型。 结果：本队列增殖型狼疮肾炎患病率为77.7%。一个包含年龄、性别、收缩压、血红蛋白、蛋白尿半定量、血尿半定量及血清C3水平的模型获得最佳拟合优度与区分度（AIC为291，C统计量为0.84）。经过内部验证和外部验证，该模型的区分度及准确性良好（C统计量分别为0.84，0.82；P值分别为0.99，0.75）。 结论：我们成功构建了一个模型能通过肾活检前临床及人口学指标评价狼疮肾炎患者为增殖型狼疮肾炎的风险。.

Keywords: Biopsy; Lupus Nephritis; Nomogram; Predictive Value of Tests; Risk Factors.

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Conflict of interest statement

There are no conflicts of interest

Figures

Figure 1
Figure 1
Calibration plot for recommended model 5 by bootstrap method. By bootstrap method repeating 500 times, solid line stands for actual performance and dot line for reference. Plus marks on the top and button indicate the distribution of proliferative lupus nephritis.
Figure 2
Figure 2
Nomogram graph of predictive model. To provide a quantitative method to better stratify patients with different classes, a nomogram of lupus nephritis was constructed integrating significant independent factors identified in the multivariate analysis.
See this image and copyright information in PMC

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