(MEnD-AKI) Multicenter Implementation of an Electronic Decision Support System for Drug-associated AKI

(MEnD-AKI) 药物相关 AKI 电子决策支持系统的多中心实施

• 批准号: 10594086
• 负责人: Azra Bihorac
• 金额: $ 27.89万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10594086
• 关键词: Acute Renal Failure with Renal Papillary Necrosis; Budgets; Cessation of life; Clinical; Clinical Data; Clinical Decision Support Systems; Consensus; Coupled; Data; Data Element; Data Set; Databases; Decision Support Systems; Dependence; Development; Dialysis procedure; Early identification; Florida; Goals; Health; Hospital Costs; Hospitals; Intervention; Knowledge; Laboratories; Medical History; Medical center; Methods; Mission; Modeling; Patient Care; Patient-Focused Outcomes; Patients; Performance; Persons; Pharmaceutical Preparations; Pharmacists; Phenotype; Physicians; Predictive Analytics; Preparation; Process; Readiness; Research; Risk; Standardization; Testing; Text; Time; United States National Institutes of Health; Universities; Validation; cost effectiveness; data integration; deep learning; demographics; effectiveness evaluation; experience; health care availability; health data; high risk; improved; improved outcome; model development; multimodality; nephrotoxicity; parent project; risk prediction model; social health determinants; social integration; tool

Project Summary Acute Kidney Injury (AKI) in the US has increased by 38% over the last eight years, with drugs as a major contributor to AKI in hospitalized patients. Drug-associated AKI (D-AKI) results in severe consequences with approximately 40% of patients experiencing in-hospital death or dialysis dependence. We have determined that many patients often continue to receive nephrotoxic drugs until AKI becomes severe. The goal of the parent project is to assess the effectiveness of a clinical decision support system (CDSS) augmented with real-time predictive analytics to support a pharmacist-led intervention to reduce the progression and complications of D-AKI. Specifically, we aim to 1) optimize the clinical performance of risk-alerts generated by a CDSS; 2) test whether an advanced CDSS coupled with a pharmacist-led intervention improves outcomes for patients with D-AKI; and 3) determine physician acceptance and cost-effectiveness of our intervention. This requires harmonization and cross validation of electronic AKI phenotypes and interpretable deep learning AKI transition model using data from two different EMR platforms, EPIC used by the University of Florida Health (UFH) and Cerner used by the University of Pittsburgh Medical Center (UPMC). While data integration, harmonization, and standardization processes are being developed for demographics and medical history, medications, laboratory results, and vital signs, it is lacking AI/ML ready datasets with social determinants of health (SDOH) exposome data and clinical notes that may carry important information about patient heath status and access to health care that may improve performance of the models. The proposed supplement project will develop integration, standardization, and processing tools and pipeline to create multimodal AI/ML ready datasets with SDOH and text data with aims: Aim 1: Preparation of AI/ML ready SDOH data. We will develop and assess tools for a) extracting, cleaning, imputing, preprocessing and representing data for various exposures contributing to a person’s SDOH exposome, b) integration of SDOH data to databases of University of Florida (UF) and University of Pittsburgh (UPitt) for them to be used in D-AKI risk model development and validation. Aim 2: Preparation of multimodal AI/ML ready data that includes unstructured text data. We will develop and assess tools for a) extracting, cleaning, preprocessing and representing unstructured text data b) integration of clinical data and unstructured text data to prepare multimodal AI/ML ready datasets at UF. The proposed supplement project will extend the aims of the parent project by including development of (1) a tool for integration of SDOH (2) a tool for extraction and integration of unstructured text data (3) tools for cleaning, imputing, preprocessing data and generalizable methods for improving the AI/ML-readiness of datasets through consensus-driven AI datasheets. The completion of these aims will provide multimodal AI/ML ready data with additional data elements, that require effort and budget not covered by parent project, to improve efficiency and performance of the D-AKI risk prediction models and other AI applications.

项目概要 过去八年，美国的急性肾损伤 (AKI) 增加了 38%，其中药物是主要病因 导致住院患者 AKI 的因素 药物相关 AKI (D-AKI) 会导致严重后果 我们已经确定，大约 40% 的患者在院内死亡或依赖透析。 许多患者经常继续接受肾毒性药物直至 AKI 变得严重。 母项目是评估临床决策支持系统（CDSS）的有效性 实时预测分析，支持药剂师主导的干预措施，以减少病情进展和 具体来说，我们的目标是 1) 优化由 D-AKI 生成的风险警报的临床表现。 CDSS；2) 测试高级 CDSS 与药剂师主导的干预措施相结合是否可以改善结果 对于 D-AKI 患者；3) 确定医生对我们干预措施的接受度和成本效益。 需要电子 AKI 表型和可解释的深度学习 AKI 的协调和交叉验证 使用来自两个不同 EMR 平台的数据的转换模型，佛罗里达大学健康中心使用的 EPIC (UFH) 和匹兹堡大学医学中心 (UPMC) 使用的 Cerner 在数据集成时， 正在为人口统计和病史制定协调和标准化流程， 药物、实验室结果和生命体征，但缺乏具有社会决定因素的 AI/ML 数据集 健康 (SDOH) 暴露数据和临床记录，可能携带有关患者健康的重要信息 可能提高模型性能的状况和获得医疗保健的机会。 项目将开发集成、标准化、处理工具和管道，以创建多模式人工智能/机器学习 准备好包含 SDOH 的数据集和文本数据，目标是： 目标 1：准备 AI/ML 准备好的 SDOH 数据。 开发和评估工具 a) 提取、清理、插补、预处理和表示各种数据 影响个人 SDOH 暴露组的暴露，b) 将 SDOH 数据整合到大学数据库中 佛罗里达大学 (UF) 和匹兹堡大学 (UPitt) 将其用于 D-AKI 风险模型开发和 目标 2：准备包括非结构化文本数据的多模式 AI/ML 数据。 将开发和评估用于 a) 提取、清理、预处理和表示非结构化文本数据的工具 b) 整合临床数据和非结构化文本数据，以在佛罗里达大学准备多模式 AI/ML 数据集。 拟议的补充项目将通过包括 (1) 工具的开发来扩展父项目的目标 用于集成 SDOH (2) 用于提取和集成非结构化文本数据的工具 (3) 用于清理的工具， 用于提高数据集的 AI/ML 就绪性的插补、预处理数据和通用方法 通过共识驱动的人工智能数据表，这些目标的完成将为多模式人工智能/机器学习做好准备。 具有额外数​​据元素的数据，需要父项目未涵盖的工作量和预算，以 提高 D-AKI 风险预测模型和其他人工智能应用程序的效率和性能。