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

> **NIH NIH R01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2022 · $278,880

## Abstract

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 multi...

## Key facts

- **NIH application ID:** 10594086
- **Project number:** 3R01DK121730-02S1
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Azra Bihorac
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $278,880
- **Award type:** 3
- **Project period:** 2021-06-01 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10594086

## Citation

> US National Institutes of Health, RePORTER application 10594086, (MEnD-AKI) Multicenter Implementation of an Electronic Decision Support System for Drug-associated AKI (3R01DK121730-02S1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10594086. Licensed CC0.

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