Deep Learning Based Pharmacokinetic Model for Vancomycin

基于深度学习的万古霉素药代动力学模型

• 批准号: 10804308
• 负责人: Masayuki Nigo
• 金额: $ 52.77万
• 依托单位: METHODIST HOSPITAL RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-21 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10804308
• 关键词: Adverse event; Architecture; Arizona; Bayesian Modeling; Boston; Clinical; Complex; Creatinine; Critical Illness; Data; Data Set; Data Sources; Databases; Differential Equation; Dose; Drug Kinetics; Drug toxicity; Electronic Health Record; Future; Goals; Guidelines; Health Care Costs; Health Sciences; Healthcare Systems; Hemodialysis; Hospitals; Human; Inpatients; Intensive Care Units; Intravenous; Laboratories; Methodist Church; Mission; Modeling; Monitor; Neural Network Simulation; Output; Patients; Pediatric Intensive Care Units; Peripheral; Pharmaceutical Preparations; Population Heterogeneity; Publishing; Recommendation; Renal Replacement Therapy; Research; Sampling; Serum; Source; Structure; Subgroup; System; Techniques; Testing; Texas; Therapeutic; Time; Toxic effect; United States; United States National Institutes of Health; Universities; Update; Validation; Vancomycin; antimicrobial; antimicrobial drug; data harmonization; deep learning; deep learning model; demographics; dosage; electronic health data; flexibility; improved; machine learning model; methicillin resistant Staphylococcus aureus; patient population; patient safety; patient subsets; pharmacokinetic model; population based; predictive modeling; pregnant; prototype; recurrent neural network; success; trend

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.

摘要：（30行） 万古霉素是住院环境中最常用的抗菌药物之一 推荐贝叶斯模型万古霉素的治疗药物浓度，尤其是 耐甲氧西林金黄色葡萄球菌（MRSA），以最大程度地降低药品税，在维持的同时是ETS的功效。 现有的贝叶斯模型，尽管被称为患者特异性药代动力学（PK）模型，但使用简单 患者特征，并在有限的基于人群的PK参数的有限的皮质种群中进行研究（该参数 贝叶斯先验）。 特定于患者的数据，包括万古霉素剂量和血清水平的数据。 贝叶斯模型结构禁止全面使用丰富的数据。 与贝叶斯模型相比 以及其他传统的机器学习模型，因为深度学习模型可以使更灵活的患者 - 特定的输入并具有更高的潜在能力。 人口。 模型但可以在AIM 1中改进。 添加更多特定于患者的数据和更精细的时间段。 提高不稳定状态的患者的预测能力。 改进分类数据的嵌入。 同时预测血清肌酸和万古霉素水平的模型。 不同的来源来验证我们的PK-RNN-V模型并改善数据摘要流量和处理 协调医疗保健系统的数据。 赫尔曼医院系统/休斯敦德克萨斯大学健康科学中心，大学关闭 亚利桑那州的亚利桑那州，亚利桑那州和公开可用的模仿数据库（马萨诸塞州波士顿） 来自至少一剂静脉内万古霉素的汤姆121,007名患者的数据。 将根据PK-RNN-V模型预测添加剂量建议作为功能并验证我们的模型 在具有万古霉素PK的特定亚组中，可以预测该项目的大量。 模型改进，直接导致医院中万古霉素使用的优化，患者有所增加 通过最大程度地减少不良事件的安全性，并降低医疗保健成本，这与NIH的研究任务保持一致。