Improved Glomerular Filtration Rate Estimation by an Artificial Neural Network

Background
Accurate 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.
Methods
A 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.
Results
In 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% [P<0.001 for all]) and CKD stage classification (misclassification rate of CKD stage, 32.4% vs. a range from 47.3% to 53.3% [P<0.001 for all]). Furthermore, in the additional external validation data set, precision and accuracy were improved by the six-variable GABP network.
Conclusions
A 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，但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，分别用于构建开发数据集、内部验证数据集与外部验证数据集；另纳入222例来自两家独立医疗机构的患者开展额外外部验证。本研究构建了多种人工神经网络模型，最终选取经遗传算法优化的反向传播（Back Propagation, BP）网络（GABP网络）作为最优模型，该模型包含6项输入变量：血清肌酐、血清尿素氮、年龄、身高、体重与性别，输出变量为估算的肾小球滤过率。随后将该模型与Cockcroft-Gault公式、MDRD公式以及CKD-EPI公式的性能进行对比。
结果
针对外部验证数据集的Bland-Altman分析显示，六变量GABP网络的精密度在所有估算模型中最高，其精密度为46.7 ml/min/1.73 m²，其余模型的精密度区间为71.3~101.7 ml/min/1.73 m²；同时该模型的准确性得到显著提升：15%准确性达49.0%，30%准确性达75.1%，50%准确性达90.5%（所有对比P<0.001），且慢性肾脏病分期的错分率为32.4%，显著低于其余模型的47.3%~53.3%区间（所有对比P<0.001）。此外，在额外外部验证数据集当中，六变量GABP网络的精密度与准确性均得到提升。
结论
本研究针对慢性肾脏病患者开发了一款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与慢性肾脏病分期提供更简便、精准且可靠的手段。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。