Yao Shu-Hui, Tsai Hsiang-Te, Lin Wen-Lin, Chen Yu-Chieh, Chou Chiahung, Lin Hsiang-Wen
College of Pharmacy, China Medical University, Taichung, Taiwan.
Department of Pharmacy, China Medical University Beigan Hospital, Yunlin, Taiwan.
BMC Pediatr. 2019 Dec 27;19(1):517. doi: 10.1186/s12887-019-1895-7.
Given its narrow therapeutic range, digoxin's pharmacokinetic parameters in infants are difficult to predict due to variation in birth weight and gestational age, especially for critically ill newborns. There is limited evidence to support the safety and dosage requirements of digoxin, let alone to predict its concentrations in infants. This study aimed to compare the concentrations of digoxin predicted by traditional regression modeling and artificial neural network (ANN) modeling for newborn infants given digoxin for clinically significant patent ductus arteriosus (PDA).
A retrospective chart review was conducted to obtain data on digoxin use for clinically significant PDA in a neonatal intensive care unit. Newborn infants who were given digoxin and had digoxin concentration(s) within the acceptable range were identified as subjects in the training model and validation datasets, accordingly. Their demographics, disease, and medication information, which were potentially associated with heart failure, were used for model training and analysis of digoxin concentration prediction. The models were generated using backward standard multivariable linear regressions (MLRs) and a standard backpropagation algorithm of ANN, respectively. The common goodness-of-fit estimates, receiver operating characteristic curves, and classification of sensitivity and specificity of the toxic concentrations in the validation dataset obtained from MLR or ANN models were compared to identify the final better predictive model.
Given the weakness of correlations between actual observed digoxin concentrations and pre-specified variables in newborn infants, the performance of all ANN models was better than that of MLR models for digoxin concentration prediction. In particular, the nine-parameter ANN model has better forecasting accuracy and differentiation ability for toxic concentrations.
The nine-parameter ANN model is the best alternative than the other models to predict serum digoxin concentrations whenever therapeutic drug monitoring is not available. Further cross-validations using diverse samples from different hospitals for newborn infants are needed.
地高辛的治疗窗较窄，由于出生体重和胎龄的差异，尤其是危重新生儿，其在婴儿体内的药代动力学参数难以预测。支持地高辛安全性和剂量要求的证据有限，更不用说预测其在婴儿体内的浓度了。本研究旨在比较传统回归模型和人工神经网络（ANN）模型预测地高辛用于临床上有意义的动脉导管未闭（PDA）的新生儿体内地高辛浓度的情况。
进行回顾性病历审查，以获取新生儿重症监护病房中地高辛用于临床上有意义的PDA的数据。相应地，将给予地高辛且地高辛浓度在可接受范围内的新生儿确定为训练模型和验证数据集中的受试者。他们的人口统计学、疾病和用药信息，这些可能与心力衰竭相关，用于模型训练和地高辛浓度预测分析。分别使用向后标准多变量线性回归（MLR）和ANN的标准反向传播算法生成模型。比较从MLR或ANN模型获得的验证数据集中常见的拟合优度估计、受试者工作特征曲线以及毒性浓度的敏感性和特异性分类，以确定最终更好的预测模型。
鉴于新生儿实际观察到的地高辛浓度与预先指定变量之间的相关性较弱，所有ANN模型在预测地高辛浓度方面的性能均优于MLR模型。特别是，九参数ANN模型对毒性浓度具有更好的预测准确性和区分能力。
在无法进行治疗药物监测时，九参数ANN模型是预测血清地高辛浓度的最佳选择，优于其他模型。需要使用来自不同医院的新生儿不同样本进行进一步的交叉验证。
