Development of Biomarker Models to Predict Outcomes in Lupus Nephritis.
Development of Biomarker Models to Predict Outcomes in Lupus Nephritis.
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DOI：
10.1002/art.39623
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发表时间：
2016-08
影响因子：
13.3
通讯作者：
Oates, Jim C.
中科院分区：
文献类型：
--
作者：
Wolf, Bethany J.;Spainhour, John C.;Arthur, John M.;Janech, Michael G.;Petri, Michelle;Oates, Jim C.
美国风湿病学会狼疮性肾炎治疗指南建议，当6个月内对治疗没有反应时，应改变诱导治疗。反应未定义，在等待该终点时可能发生肾纤维化。因此，需要一种决策支持工具来更好地定义反应，以指导临床医生开始治疗患者。本研究旨在确定具有足够预测能力的生物标志物模型，以开发此类工具。对140例尚未开始诱导治疗的经活检证实的狼疮性肾炎患者的尿液样本进行了一组尿液生物标志物分析。为每个单独的生物标志物生成单变量受试者工作特征（ROC）曲线，并与来自使用随机森林算法开发的机器学习模型的ROC曲线下面积值进行比较。除了临床标志物之外，还将用新标志物开发的结果的生物标志物模型与仅用传统临床标志物开发的生物标志物模型进行比较。用组合的传统和新型生物标志物组开发的模型证明了具有临床意义的预测能力。最能预测反应的标志物是趋化因子、细胞因子和细胞损伤标志物。这是第一项证明低丰度生物标志物面板和机器学习算法预测狼疮肾炎结果的能力的研究。这是研究开发具有临床意义的决策支持工具的关键第一步。
The American College of Rheumatology guidelines for the treatment of lupus nephritis recommend change in induction therapy when response to therapy has not occurred within 6 months. Response is not defined, and renal fibrosis can occur while waiting for this end point. Therefore, a decision support tool to better define response is needed to guide clinicians when starting patients on therapy. This study was undertaken to identify biomarker models with sufficient predictive power to develop such a tool. Urine samples from 140 patients with biopsy-proven lupus nephritis who had not yet started induction therapy were analyzed for a panel of urinary biomarkers. Univariate receiver operating characteristic (ROC) curves were generated for each individual biomarker and compared to the ROC area under the curve values from machine learning models developed using random forest algorithms. Biomarker models of outcome developed with novel markers in addition to clinical markers were compared to those developed with traditional clinical markers alone. Models developed with the combined traditional and novel biomarker panels demonstrated clinically meaningful predictive power. Markers most predictive of response were chemokines, cytokines, and markers of cellular damage. This is the first study to demonstrate the power of low-abundance biomarker panels and machine learning algorithms for predicting lupus nephritis outcomes. This is a critical first step in research to develop clinically meaningful decision support tools.
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影响因子：
4.7
作者：
Hahn, Bevra H.;McMahon, Maureen A.;Wilkinson, Alan;Wallace, W. Dean;Daikh, David I.;Fitzgerald, John D.;Karpouzas, George A.;Merrill, Joan T.;Wallace, Daniel J.;Yazdany, Jinoos;Ramsey-Goldman, Rosalind;Singh, Karandeep;Khalighi, Mazdak;Choi, Soo-In;Gogia, Maneesh;Kafaja, Suzanne;Kamgar, Mohammad;Lau, Christine;Martin, William J.;Parikh, Sefali;Peng, Justin;Rastogi, Anjay;Chen, Weiling;Grossman, Jennifer M.
通讯作者：
Grossman, Jennifer M.
DOI：
10.2215/cjn.04810908
发表时间：
2009-05-01
影响因子：
9.8
作者：
Han, Won K.;Wagener, Gebhard;Lee, H. Thomas
通讯作者：
Lee, H. Thomas
影响因子：
4.4
作者：
Campbell, S;Burkly, LC;Putterman, C
通讯作者：
Putterman, C
影响因子：
3.8
作者：
Taylor TP;Janech MG;Slate EH;Lewis EC;Arthur JM;Oates JC
通讯作者：
Oates JC
影响因子：
--
作者：
Rovin, Brad H.;Furie, Richard;Appel, Gerald
通讯作者：
Appel, Gerald