Deep Learning Based Pharmacokinetic Model for Vancomycin

摘要：(30行) 万古霉素是住院环境中最常用的抗菌药物之一。国家指导方针 推荐用贝叶斯模型监测万古霉素的治疗药物浓度，尤其是对 耐甲氧西林金黄色葡萄球菌(MRSA)，以最大限度地减少药物毒性，同时保持其疗效。 现有的贝叶斯模型，尽管被声称为患者特定的药代动力学(PK)模型，但使用简单 患者特征，并在有限的患者群体中研究基于总体的PK参数( 贝叶斯优先)。日益可用的真实世界电子健康记录(EHR)提供了广泛的 患者特定的数据，包括万古霉素剂量和血清水平的数据。然而，有限的灵活性， 贝叶斯模型结构禁止充分利用这些丰富的数据。深度学习模型，如递归 与贝叶斯模型相比，神经网络(RNN)对EHR中万古霉素的预测尤其具有吸引力 和其他传统的机器学习模型，因为深度学习模型使患者能够更灵活- 具体投入，具有较高的潜力。因此，它们提供了对多样化的 人口。我们的万古霉素深度学习模型(PK-RNN-V)的性能优于公开提供的贝叶斯模型 模型，但可以在各个方面进行改进。在目标1中，我们将改进PK-RNN-V模型体系结构和 添加更多特定于患者的数据和更精细的时间步长。我们将构建两室PK-RNN模型来 增加不稳定状态患者的预测能力。我们将使用Med-Bert增强PK-RNN-V以 改进分类数据的嵌入。我们还将开发多轨迹常微分方程式 同时预测血肌酐和万古霉素水平的模型。在目标2中，我们将从 不同的来源验证我们的PK-RNN-V模型，并改进数据提取流程和预处理以 协调来自医疗保健系统的数据。我们将使用休斯顿卫理公会医院和纪念馆的电子病历 赫尔曼医院系统/德克萨斯州休斯敦的德克萨斯大学健康科学中心，德克萨斯州大学 亚利桑那州凤凰城，亚利桑那州，亚利桑那州，和公开可用的MIMIC-IV数据库(马萨诸塞州波士顿)。这些数据库包含 数据来自超过121,007名接受过至少一剂万古霉素静脉注射的患者。在目标3中，我们 将添加基于PK-RNN-V模型预测的剂量建议作为一项功能，并验证我们的模型 在具有挑战万古霉素PK的特定亚组中预测PK水平。这个项目将提供大量的 模型改进，直接导致万古霉素在医院的优化使用，在患者中增加 通过将不良事件降至最低来确保安全，并降低医疗成本，这与NIH的研究使命相一致。

Abstract: (30 lines) Vancomycin is one of the most commonly used antimicrobial drugs in inpatient settings. National guidelines recommend Bayesian models to monitor the therapeutic drug concentration of vancomycin, especially for methicillin-resistant Staphylococcus aureus (MRSA), to minimize drug toxicity while maintaining its efficacy. Existing Bayesian models, despite being claimed as patient-specific pharmacokinetic (PK) models, use simple patient features and are studied in limited patient populations for the population-based PK parameters (the Bayesian prior). Increasingly available real-world electronic health records (EHR) provide a wide range of patient-specific data, including data on vancomycin dosage and serum levels. However, the limited flexibility of the Bayesian model structure prohibits the full use of these rich data. Deep-learning models, such as recurrent neural network (RNN), are particularly attractive for PK of vancomycin in EHR, compared to Bayesian models and other traditional machine learning models, because deep-learning models enable more flexible patient- specific inputs and possess a higher latent capacity. Thus, they deliver better predictions for a diverse population. Our deep-learning model for vancomycin (PK-RNN-V) outperforms publicly available Bayesian models but can be improved on various aspects. In Aim 1, we will improve PK-RNN-V model architectures and add more patient-specific data and a finer timestep. We will construct two-compartment PK-RNN models to increase predictive power in patients with unsteady states. We will augment PK-RNN-V with Med-BERT to improve the embedding of categorical data. We will also develop multi-track ordinary differential equations models for simultaneous prediction of serum creatinine and vancomycin levels. In Aim 2, we will use EHR from different sources to validate our PK-RNN-V model and improve the data-extraction flow and pre-processing to harmonize data from healthcare systems. We will use EHR from Houston Methodist Hospital and Memorial Hermann Hospital System/The University of Texas Health Science Center in Houston, TX, the University of Arizona in Phoenix, AZ, and the publicly available MIMIC-IV database (Boston, MA). These databases contain data from more than 121,007 patients who received at least one dose of intravenous vancomycin. In Aim 3, we will add dosing recommendations based on PK-RNN-V model predictions as a feature and validate our model in specific subgroups with challenging vancomycin PK to predict PK levels. This project will deliver substantial model improvements, leading directly to the optimization of vancomycin use in hospitals, increased in patient safety by minimizing adverse events, and reduced healthcare costs, which align with NIH’s research mission.