PDF(Pubmed)
Abstract：
Tacrolimus is a major immunosuppressor against post-transplant rejection in kidney transplant recipients. However, the narrow therapeutic index of tacrolimus and considerable variability among individuals are challenges for therapeutic outcomes. The aim of this study was to compare different machine learning and deep learning algorithms and establish individualized dose prediction models by using the best performing algorithm. Therefore, among the 10 commonly used algorithms we compared, the TabNet algorithm outperformed other algorithms with the highest R2 (0.824), the lowest prediction error [mean absolute error (MAE) 0.468, mean square error (MSE) 0.558, and root mean square error (RMSE) 0.745], and good performance of overestimated (5.29%) or underestimated dose percentage (8.52%). In the final prediction model, the last tacrolimus daily dose, the last tacrolimus therapeutic drug monitoring value, time after transplantation, hematocrit, serum creatinine, aspartate aminotransferase, weight, CYP3A5, body mass index, and uric acid were the most influential variables on tacrolimus daily dose. Our study provides a reference for the application of deep learning technique in tacrolimus dose estimation, and the TabNet model with desirable predictive performance is expected to be expanded and applied in future clinical practice.
摘要：
他克莫司是针对肾移植受者移植后排斥的主要免疫抑制剂。然而,他克莫司狭窄的治疗指数和个体间相当大的差异是治疗结果的挑战.本研究的目的是比较不同的机器学习和深度学习算法，并通过使用性能最佳的算法建立个性化的剂量预测模型。因此,在我们比较的10种常用算法中，TabNet算法优于其他具有最高R2(0.824)的算法，最低预测误差[平均绝对误差（MAE）0.468，均方误差（MSE）0.558和均方根误差（RMSE）0.745]，和良好的性能高估（5.29％）或低估的剂量百分比（8.52％）。在最终的预测模型中，最后一次他克莫司日剂量,最后他克莫司治疗药物监测值,移植后的时间，血细胞比容,血清肌酐,天冬氨酸转氨酶,体重,CYP3A5,体重指数,和尿酸是对他克莫司日剂量影响最大的变量。本研究为深度学习技术在他克莫司剂量估算中的应用提供了参考,具有理想预测性能的TabNet模型有望在未来的临床实践中得到扩展和应用。
