# AI driven acute renal replacement therapy - (AID-ART)

> **NIH NIH R01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2024 · $584,060

## Abstract

Abstract
Intradialytic hypotension (IDH) occurs in one-third of critically ill patients with acute kidney injury and treated
with kidney replacement therapy in the intensive care unit (ICU). Occurrence of IDH is associated with
increased resource utilization such as fluid and vasopressor administration, discontinuation of kidney
replacement therapy, decreased recovery of kidney function, dependence on kidney replacement therapy and
death. IDH is often unrecognized until it is well established, by which time patients are refractory to treatment
or have already developed organ injury. Thus, if one could accurately predict who and when patients develop
IDH, then effective preemptive treatments could be administered to reduce risk of IDH and improve outcomes.
Our preliminary work showed that advanced high-frequency data modeling and waveform analysis identified
patients at risk for hypotension within 2 minutes of monitoring in the ICU, and if monitored for 5 minutes,
differentiated between patients who would develop hypotension or remain stable over the next 48 hours. In this
proposal entitled “Artificial Intelligence Driven Acute Renal Replacement Therapy (AID-ART)”, we propose to
apply predictive analytics using linked electronic health record and high-frequency monitor data to critically ill
patients with acute kidney injury and undergoing intermittent and continuous kidney replacement therapies at
the University of Pittsburgh Medical Center and the Mayo Clinic ICUs. We will examine the accuracy of various
machine learning models to predict IDH risk-evaluating model performance, usability, alert frequency, lead time
and number needed to alert, and hospital mortality and dependence on kidney replacement therapy (Aim 1a);
predict response to a range of clinical interventions for IDH and subsequent clinical outcomes (Aim 1b); and
perform cross validation across the two healthcare systems (Aim 1c). We will construct reinforcement learning
systems to develop a rule-driven intervention for IDH alerts and measurement-driven responses to avoid and
respond to IDH based on principles of functional hemodynamic monitoring (Aim 2a). We will also develop a
reinforcement learning algorithm to learn an optimal intervention strategy based on the probability of events
rather than in reaction to IDH events (Aim 2b). We will silently deploy and evaluate the ability of this artificial
intelligence (AI) algorithm to forecast IDH risk and recommend interventions in real-time across the two
healthcare systems. We will then assess the validity of recommended interventions using an expert clinician
adjudication panel (Aim 3a); and will compare the AI recommended interventions with that of actual
interventions performed by bedside clinicians (Aim 3b). This proposal will be the harbinger of a future
multicenter randomized clinical trial to examine personalized risk prediction and AI-augmented management of
IDH among critically ill patients with acute kidney injury and underg...

## Key facts

- **NIH application ID:** 10830320
- **Project number:** 5R01DK131586-04
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Gilles Clermont
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $584,060
- **Award type:** 5
- **Project period:** 2021-09-30 → 2026-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10830320

## Citation

> US National Institutes of Health, RePORTER application 10830320, AI driven acute renal replacement therapy - (AID-ART) (5R01DK131586-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10830320. Licensed CC0.

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