Prediction models of treatment response in lupus nephritis.
Prediction models of treatment response in lupus nephritis.
复制标题
狼疮性肾炎治疗反应的预测模型。
DOI：
10.1016/j.kint.2021.11.014
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发表时间：
2022-03
影响因子：
19.6
通讯作者：
Rovin BH
中科院分区：
文献类型：
--
作者：
Ayoub I;Wolf BJ;Geng L;Song H;Khatiwada A;Tsao BP;Oates JC;Rovin BH
为了建立狼疮性肾炎一年治疗反应的预测模型，采用机器学习的方法将传统的临床数据和新的尿液生物标志物相结合。当代狼疮性肾炎生物标记物是通过无偏见的PubMed搜索确定的。在排名前50%的生物标志物中，有13种新的尿蛋白被选为狼疮性肾炎发作时的检测对象。这些新的标记物和传统的临床数据被结合到各种机器学习算法中，以开发一年蛋白尿的预测模型和估计的肾小球滤过率(EGFR)。模型在来自四个不同子队列的246人上进行了培训，并在30名狼疮性肾炎患者的独立队列上进行了验证。每种结果都考虑了七个模型。其中四分之三的模型显示了良好的预测价值，接收器工作特性曲线下的面积超过0.7。总体而言，EGFR治疗反应模型的预测效果最好。此外，表现最好的模型既包含传统的临床数据，也包含新的尿液生物标记物，包括细胞因子、趋化因子和肾脏损害标记物。因此，我们的研究提供了进一步的证据，表明机器学习方法可以使用一组传统的和新的生物标记物来预测狼疮性肾炎一年的结果。然而，在机器学习能够被常规用于临床实践以指导治疗之前，需要进一步验证机器学习作为临床决策辅助手段来改善结果的有效性。
In order to develop prediction models of one-year treatment response in lupus nephritis, an approach using machine learning to combine traditional clinical data and novel urine biomarkers was undertaken. Contemporary lupus nephritis biomarkers were identified through an unbiased PubMed search. Thirteen novel urine proteins contributed to the top 50% of ranked biomarkers and were selected for measurement at the time of lupus nephritis flare. These novel markers along with traditional clinical data were incorporated into a variety of machine learning algorithms to develop prediction models of one-year proteinuria and estimated glomerular filtration rate (eGFR). Models were trained on 246 individuals from four different sub-cohorts and validated on an independent cohort of 30 patients with lupus nephritis. Seven models were considered for each outcome. Three quarters of these models demonstrated good predictive value with areas under the receiver operating characteristic curve over 0.7. Overall, prediction performance was the best for models of eGFR response to treatment. Furthermore, the best performing models contained both traditional clinical data and novel urine biomarkers, including cytokines, chemokines, and markers of kidney damage. Thus, our study provides further evidence that a machine learning approach can predict lupus nephritis outcomes at one year using a set of traditional and novel biomarkers. However, further validation of the utility of machine learning as a clinical decision aid to improve outcomes will be necessary before it can be routinely used in clinical practice to guide therapy.
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影响因子：
13.3
作者：
Wolf, Bethany J.;Spainhour, John C.;Arthur, John M.;Janech, Michael G.;Petri, Michelle;Oates, Jim C.
通讯作者：
Oates, Jim C.
影响因子：
5
作者：
Ayoub, Isabelle;Nelson, Jessica;Rovin, Brad H.
通讯作者：
Rovin, Brad H.
影响因子：
2.4
作者：
Filzmoser, Peter;Liebmann, Bettina;Varmuza, Kurt
通讯作者：
Varmuza, Kurt
DOI：
10.1152/ajprenal.00512.2017
发表时间：
2018-12-01
期刊：
American journal of physiology. Renal physiology
影响因子：
--
作者：
Wright RD;Beresford MW
通讯作者：
Beresford MW
影响因子：
--
作者：
Rovin, Brad H.;Furie, Richard;Appel, Gerald
通讯作者：
Appel, Gerald