Improved Glomerular Filtration Rate Estimation by an Artificial Neural Network

BackgroundAccurate evaluation of glomerular filtration rates (GFRs) is of critical importance in clinical practice. A previous study showed that models based on artificial neural networks (ANNs) could achieve a better performance than traditional equations. However, large-sample cross-sectional surveys have not resolved questions about ANN performance. MethodsA total of 1,180 patients that had chronic kidney disease (CKD) were enrolled in the development data set, the internal validation data set and the external validation data set. Additional 222 patients that were admitted to two independent institutions were externally validated. Several ANNs were constructed and finally a Back Propagation network optimized by a genetic algorithm (GABP network) was chosen as a superior model, which included six input variables; i.e., serum creatinine, serum urea nitrogen, age, height, weight and gender, and estimated GFR as the one output variable. Performance was then compared with the Cockcroft-Gault equation, the MDRD equations and the CKD-EPI equation. ResultsIn the external validation data set, Bland-Altman analysis demonstrated that the precision of the six-variable GABP network was the highest among all of the estimation models; i.e., 46.7 ml/min/1.73 m2 vs. a range from 71.3 to 101.7 ml/min/1.73 m2, allowing improvement in accuracy (15% accuracy, 49.0%; 30% accuracy, 75.1%; 50% accuracy, 90.5% [PP ConclusionsA new ANN model (the six-variable GABP network) for CKD patients was developed that could provide a simple, more accurate and reliable means for the estimation of GFR and stage of CKD than traditional equations. Further validations are needed to assess the ability of the ANN model in diverse populations.背景：准确评估肾小球滤过率（glomerular filtration rates, GFR）在临床实践中具有至关重要的意义。既往研究表明，基于人工神经网络（artificial neural networks, ANNs）的模型性能优于传统计算公式。然而，大样本横断面研究尚未明确人工神经网络的实际应用表现。方法：本研究共纳入1180例慢性肾脏病（chronic kidney disease, CKD）患者，分别纳入开发数据集、内部验证数据集与外部验证数据集。另纳入来自2家独立医疗机构的222例患者用于外部验证。我们构建了多款人工神经网络模型，最终选择经遗传算法优化的反向传播网络（genetic algorithm optimized Back Propagation network, GABP网络）作为最优模型，该模型包含6个输入变量：血清肌酐、血清尿素氮、年龄、身高、体重与性别，输出变量为估算肾小球滤过率。随后将该模型与科克伦-高尔特（Cockcroft-Gault）公式、MDRD公式及CKD-EPI公式进行性能对比。结果：在外部验证数据集内，布兰德-奥特曼（Bland-Altman）分析显示，六变量GABP网络的估算精度在所有评估模型中最高——其偏差为46.7 ml/min/1.73 m²，其余模型的偏差范围为71.3至101.7 ml/min/1.73 m²，同时提升了准确度：15%准确度为49.0%；30%准确度为75.1%；50%准确度为90.5%[PP 结论：本研究开发了一款针对慢性肾脏病患者的新型人工神经网络模型（六变量GABP网络），相较于传统计算公式，该模型可为肾小球滤过率估算及慢性肾脏病分期提供更简便、准确且可靠的手段。未来仍需开展进一步验证研究，以评估该人工神经网络模型在不同人群中的应用能力。