Machine learning validation of medication regimen complexity for critical care pharmacist resource prediction

重症监护药剂师资源预测的药物治疗方案复杂性的机器学习验证

• 批准号: 10448856
• 负责人: Andrea Sikora
• 金额: $ 15.15万
• 依托单位: UNIVERSITY OF GEORGIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-08 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10448856
• 关键词: Machine; learning; validation; medication; regimen

Intensive care unit (ICU) patients are at heightened risk of adverse drug events (ADEs) and poor outcomes. Critical care pharmacists (CCPs) prevent ADEs, improve patient outcomes, and reduce healthcare costs through performing medication interventions. However, CCPs are an underused healthcare resource due to lack of health information technology (IT)-based predictive tools to allocate the care they provide to ICU patients. Currently, there are no validated health IT tools for CCPs available to optimize patient-centered care. The central hypothesis of this R21 Health Information Technology to Improve Health Care Quality and Outcomes Award, based on preliminary data, is that data-driven methods applied to the MRC-ICU Scoring Tool will out-perform predictions of a rules-based model in predicting CCP interventions that can improve patient outcomes and may serve as the foundation for development of novel health IT tools that optimize the patient-centered care provided by CCPs. The MRC-ICU Scoring Tool is the first tool designed to measure medication regimen complexity in ICU patients. To be scaled-up, this tool requires thorough validation and IT based automation. The objective of this work is to apply machine learning (ML) methodology to multi-center data to create prediction tools for integration into visualization dashboards that answer vital questions including (1) what are the predicted number of CCP interventions per patient; (2) what is the risk of real-time modifiable outcomes (e.g., fluid overload); (3) what are the predicted outcomes (e.g., mortality, length of stay). The long- term goal of the proposed work is to establish validated prediction models that can be embedded into dashboards in the electronic health record (EHR) to help guide CCP resource deployment. The rationale for this work is that it will establish the MRC-ICU Scoring Tool as a means of synthesizing patient data for integration across EHR systems. The central hypothesis will be tested using large, multi-center data of ICU patients via these specific aims: (1) Apply ML-based prediction methods to develop a new model of medication regimen complexity as a metric for predicting CCP interventions and patient outcomes; (2) Compare the performance of different models to predict CCP interventions and patient outcomes; (3) Design a web-based dashboard (ICView) to visualize medication regimen complexity-based predictions. The health IT product will result in a Web-based dashboard (ICView) that houses a real-time, automated MRC-ICU Scoring Tool in addition to prediction models for CCP interventions that can improve patient outcomes. This innovative approach applies state-of-the-art ML methodology to the novel MRC-ICU Scoring Tool. The proposed work is significant because any advances in the understanding of how CCPs improve outcomes would have a profound public health impact due to their established role on the interprofessional healthcare team. The health IT products provide the necessary foundation for a future R18 application for a multi-center, prospective trial to evaluate MRC-ICU based CCP resource allocation strategies.

重症监护病房(ICU)患者发生不良药物事件(ADE)和预后不良的风险较高。 重症监护药剂师(CCP)可预防不良反应、改善患者预后并降低医疗成本 通过实施药物干预。然而，CCP是一种未得到充分利用的医疗资源，原因是 缺乏基于医疗信息技术(IT)的预测工具来分配其向ICU提供的护理 病人。目前，还没有经过验证的适用于CCP的医疗IT工具来优化以患者为中心的护理。 这一R21健康信息技术提高医疗质量和 结果奖，基于初步数据，是将数据驱动的方法应用于MRC-ICU评分 在预测可改进的CCP干预方面，该工具的性能将超过基于规则的模型的预测 患者的结果，并可作为开发新的医疗IT工具的基础，以优化 由CCP提供的以患者为中心的护理。MRC-ICU评分工具是第一个设计用于测量 ICU患者用药方案的复杂性。要向上扩展，此工具需要彻底的验证和IT 基于自动化。这项工作的目标是将机器学习方法应用于多中心 用于创建用于集成到可视化仪表板中的预测工具的数据，这些仪表板可回答以下重要问题 (1)预计每个患者接受CCP干预的次数是多少；(2)实时可修改的风险是什么 结果(例如，液体超载)；(3)预测的结果(例如，死亡率、住院时间)。长的- 拟议工作的长期目标是建立经过验证的预测模型，该模型可以嵌入 电子健康记录(EHR)中的仪表板，帮助指导CCP资源部署。其基本原理是 这项工作是，它将建立MRC-ICU评分工具，作为合成患者数据的一种手段 跨EHR系统集成。中心假设将使用ICU的大型多中心数据进行验证 患者通过这些特定的目标：(1)应用基于ML的预测方法来开发新的用药模式 作为预测CCP干预和患者结果的指标的方案复杂性；(2)比较 预测CCP干预和患者预后的不同模型的性能；(3)设计基于Web的 Dashboard(ICView)，用于可视化基于复杂性的用药方案预测。健康IT产品将 最终形成一个基于Web的仪表板(ICView)，其中包含一个实时、自动化的MRC-ICU评分工具 除了可以改善患者预后的CCP干预预测模型。这是一项创新 方法将最先进的ML方法应用于新颖的MRC-ICU评分工具。建议的工作是 因为在理解CCP如何改善结果方面的任何进展都将产生 由于他们在跨专业医疗团队中的既定角色，对公共卫生产生了深远的影响。健康 IT产品为未来的R18应用程序提供了必要的基础，以便进行多中心、前瞻性试验 评估基于MRC-ICU的CCP资源分配策略。