Bayesian Networks: A New Approach to Predict Therapeutic Range Achievement of Initial Cyclosporine Blood Concentration After Pediatric Hematopoietic Stem Cell Transplantation.
Bayesian Networks: A New Approach to Predict Therapeutic Range Achievement of Initial Cyclosporine Blood Concentration After Pediatric Hematopoietic Stem Cell Transplantation.
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DOI：
10.1007/s40268-017-0223-7
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发表时间：
2018-03
期刊：
影响因子：
3
通讯作者：
Bleyzac N
中科院分区：
文献类型：
--
作者：
Leclerc V;Ducher M;Bleyzac N
儿科造血干细胞移植（HSCT）允许治疗许多疾病，包括恶性和非恶性。环孢菌素是一种窄治疗指数药物，是用于预防移植物抗宿主病（GVHD）的主要免疫抑制剂，但如果用药过量，也可能导致严重的不良反应。本研究的目的是使用数学个体预测模型预测儿科HSCT后的初始环孢素残留血药浓度值，从而预测达到治疗范围所需的剂量。从2008年至2016年接受HSCT的155例儿科患者移植后2个月的移植物输注中收集的临床和生物学数据用于生成1000例受试者的合成数据，用于构建贝叶斯网络模型。我们比较了该模型与其他四种方法的特征和对临床或生物学缺失数据的敏感性。树增强的朴素贝叶斯网络显示出最好的特征，没有丢失数据（受试者特征曲线曲线下面积[AUC-ROC]为0.89 ± 0.02），18.9 ± 2.6%的患者分类错误，阳性和阴性预测值分别为85.9 ± 3.4%和74.2 ± 5.1%，并且在从没有到10%缺失数据的合成数据集中发现了这种趋势。可能影响初始残留环孢素浓度是否在治疗范围内的最相关变量是测量前的末次剂量和测量前的平均剂量。我们开发并交叉验证了一个在线贝叶斯网络来预测儿科HSCT后的首次环孢素浓度。该模型允许模拟不同的给药方案，并使最佳给药方案能够在移植后立即达到待发现的治疗范围，最大限度地降低不良反应和GVHD发生的风险。
Pediatric hematopoietic stem cell transplantation (HSCT) allows the treatment of numerous diseases, both malignant and non-malignant. Cyclosporine, a narrow therapeutic index drug, is the major immunosuppressant used to prevent graft-versus-host disease (GVHD), but may also cause severe adverse effects in case of overdosing. The objective of this study is to predict the initial cyclosporine residual blood concentration value after pediatric HSCT, and consequently the dose necessary to reach the therapeutic range, using a mathematical individual predictive model. Clinical and biological data collected from the graft infusion for 2 months after transplantation in 155 pediatric patients undergoing HSCT between 2008 and 2016 were used to generate synthetic data for 1000 subjects which were used to build a Bayesian network model. We compared the characteristics and sensitivity to clinical or biological missing data of this model with four other methods. The tree-augmented Naïve Bayesian network showed the best characteristics, with no missing data (area under the curve of the receiving operator characteristics curve [AUC-ROC] of 0.89 ± 0.02), 18.9 ± 2.6% of patients misclassified, and positive and negative predictive values of 85.9 ± 3.4% and 74.2 ± 5.1%, respectively, and this trend is found in the synthetic dataset from no to 10% missing data. The most relevant variables that could influence whether the initial residual cyclosporine concentration is in the therapeutic range are the last dose before measurement and the mean dose before measurement. We developed and cross-validated an online Bayesian network to predict the first cyclosporine concentration after pediatric HSCT. This model allows simulation of different dosing regimens, and enables the best dosing regimen to reach the therapeutic range immediately after transplantation to be found, minimizing the risk of adverse effects and GVHD occurrence.
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