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

项目摘要 在过去的八年里，美国的急性肾损伤（阿基）增加了38%，其中药物是主要原因。 住院患者中阿基的贡献者。药物相关阿基（D-AKI）导致严重后果， 约40%的患者发生院内死亡或透析依赖。我们已经确定 许多患者经常继续接受肾毒性药物，直到阿基变得严重。的目标 父项目是评估临床决策支持系统（CDSS）的有效性， 实时预测分析，以支持药剂师主导的干预，以减少进展， D-AKI的并发症具体而言，我们的目标是：1）优化风险警报的临床性能， CDSS; 2）测试高级CDSS与药剂师主导的干预措施相结合是否会改善结局 D-AKI患者; 3）确定医生对我们干预的接受程度和成本效益。这 需要协调和交叉验证电子阿基表型和可解释的深度学习阿基 转换模型使用来自两个不同EMR平台的数据，佛罗里达大学卫生部使用的EPIC (UFH)和匹兹堡大学医学中心（UPMC）使用的Cerner。在数据集成的同时， 人口统计学和病史的协调和标准化过程正在开发中， 药物，实验室结果和生命体征，它缺乏AI/ML就绪的数据集，这些数据集具有以下社会决定因素： 健康（SDOH）麻烦数据和临床记录，可能包含有关患者健康的重要信息 状态和获得医疗保健，可能会提高模型的性能。拟议补编 该项目将开发集成，标准化和处理工具和管道，以创建多模式AI/ML 准备好SDOH数据集和目标文本数据：目标1：准备AI/ML准备好的SDOH数据。我们将 开发和评估工具，用于a）提取、清理、估算、预处理和表示各种 导致个人SDOH暴露的暴露，B）将SDOH数据集成到大学数据库中 佛罗里达大学（UF）和匹兹堡大学（UPitt）的研究人员将其用于D-AKI风险模型开发， 验证。目标2：准备包含非结构化文本数据的多模态AI/ML就绪数据。我们 将开发和评估工具，用于a）提取、清理、预处理和表示非结构化文本数据 B）整合临床数据和非结构化文本数据，以在UF准备多模态AI/ML就绪数据集。的 拟议的补充项目将扩展母项目的目标，包括开发（1）一个工具 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， 估算、预处理数据和提高数据集AI/ML就绪性的可推广方法 通过共识驱动的AI文档。这些目标的完成将为多模式AI/ML提供准备 包含额外数据元素的数据，需要父项目未涵盖的工作和预算， 提高D-AKI风险预测模型和其他AI应用的效率和性能。

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.