A Classification Model to Predict the Rate of Decline of Kidney Function.
A Classification Model to Predict the Rate of Decline of Kidney Function.
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DOI：
10.3389/fmed.2017.00097
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发表时间：
2017
影响因子：
3.9
通讯作者：
Lipkowitz MS
中科院分区：
文献类型：
--
作者：
Subasi E;Subasi MM;Hammer PL;Roboz J;Anbalagan V;Lipkowitz MS
非裔美国人肾脏疾病和高血压研究 (AASK) 是一项随机双盲治疗试验，其动机是非裔美国人人群中高血压相关肾脏疾病的高发病率以及有效疗法的稀缺。本研究描述了一种基于模式的分类方法，使用表面增强激光解吸电离/飞行时间蛋白质组数据来预测肾功能下降率，这些数据来自按肾小球滤过率变化率分类的快速和慢速进展者。通过应用数据逻辑分析 (LAD) 方法，构建了由 5,751 个血清蛋白质组特征中的 7 个组成的准确分类模型。经过10倍交叉验证，该模型的准确度为80.6±0.11%，敏感性为78.4±0.17%，特异性为78.5±0.16%。 LAD 判别式用于识别不同风险组的患者。分配给 116 名 AASK 患者的 LAD 风险评分生成了一条 AUC 0.899 (CI 0.845–0.953) 的受试者工作曲线，并且优于蛋白尿分配的风险评分，蛋白尿是慢性肾病进展的最佳预测因子之一。
The African American Study of Kidney Disease and Hypertension (AASK), a randomized double-blinded treatment trial, was motivated by the high rate of hypertension-related renal disease in the African-American population and the scarcity of effective therapies. This study describes a pattern-based classification approach to predict the rate of decline of kidney function using surface-enhanced laser desorption ionization/time of flight proteomic data from rapid and slow progressors classified by rate of change in glomerular filtration rate. An accurate classification model consisting of 7 out of 5,751 serum proteomic features is constructed by applying the logical analysis of data (LAD) methodology. On cross-validation by 10-folding, the model was shown to have an accuracy of 80.6 ± 0.11%, sensitivity of 78.4 ± 0.17%, and specificity of 78.5 ± 0.16%. The LAD discriminant is used to identify the patients in different risk groups. The LAD risk scores assigned to 116 AASK patients generated a receiver operating curves curve with AUC 0.899 (CI 0.845–0.953) and outperforms the risk scores assigned by proteinuria, one of the best predictors of chronic kidney disease progression.