BACKGROUND Management of tacrolimus trough levels influences morbidity and mortality after lung transplantation. Several studies have explored pharmacokinetic and artificial intelligence models to monitor tacrolimus levels. However, many models depend on a wide range of variables, some of which, like genetic polymorphisms, are not commonly tested for in regular clinical practice. This study aimed to verify the efficacy of a novel approach simply utilizing time series data of tacrolimus dosing, with the objective of accurately predicting trough levels in the variety of clinical settings. METHODS Data encompassing 36 clinical variables for each patient were gathered, and a multivariate long short-term memory algorithm was applied to forecast subsequent tacrolimus trough levels based on the selected clinical variables. The tool was developed using a dataset of 87,112 data points from　117 patients and its efficacy was confirmed using six additional cases. RESULTS Shapley Additive exPlanations revealed a significant correlation between trough levels and prior dose-concentration data. By using simple trend learning of dose, administration route, and previous trough levels of tacrolimus, we could predict values within 30% of the actual values for 88.5% of time points, which facilitated the creation of a tool for simulating tacrolimus trough levels in response to dosage adjustments. The tool exhibited the potential for rectifying clinical misjudgments in a simulation cohort. CONCLUSIONS Utilizing our time series forecasting tool, precise prediction of trough levels is attainable independently of other clinical variables, through the analysis of historical tacrolimus dose-concentration trends alone.

中文翻译：

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用于个性化他克莫司给药的长短期记忆算法：一种简单有效的肺移植后时间序列预测方法。


背景 他克莫司谷水平的管理会影响肺移植后的发病率和死亡率。几项研究探索了药代动力学和人工智能模型来监测他克莫司水平。然而，许多模型依赖于广泛的变量，其中一些变量，如遗传多态性，在常规临床实践中通常不会进行测试。本研究旨在验证一种仅利用他克莫司给药时间序列数据的新方法的有效性，目的是准确预测各种临床环境中的谷水平。方法 收集每位患者包含 36 个临床变量的数据，并应用多变量长短期记忆算法根据选定的临床变量预测后续他克莫司谷水平。该工具是使用来自 117 名患者的 87,112 个数据点的数据集开发的，其疗效通过另外 6 个病例得到证实。结果 Shapley Additive 解释揭示了谷水平与先前剂量浓度数据之间的显着相关性。通过使用他克莫司剂量、给药途径和先前谷水平的简单趋势学习，我们可以预测 88.5% 时间点的实际值在 30% 以内的值，这有助于创建模拟他克莫司谷水平以响应剂量调整的工具。该工具显示出纠正模拟队列中临床误判的潜力。结论利用我们的时间序列预测工具，仅通过分析历史他克莫司剂量浓度趋势，就可以独立于其他临床变量实现谷水平的精确预测。