Can Artificial Intelligence Assist in Delivering Continuous Renal Replacement Therapy?

Copyright © 2022 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved..

Continuous renal replacement therapy (CRRT) is widely utilized to support critically ill patients with acute kidney injury. Artificial intelligence (AI) has the potential to enhance CRRT delivery, but evidence is limited. We reviewed existing literature on the utilization of AI in CRRT with the objective of identifying current gaps in evidence and research considerations. We conducted a scoping review focusing on the development or use of AI-based tools in patients receiving CRRT. Ten papers were identified; 6 of 10 (60%) published in 2021, and 6 of 10 (60%) focused on machine learning models to augment CRRT delivery. All innovations were in the design/early validation phase of development. Primary research interests focused on early indicators of CRRT need, prognostication of mortality and kidney recovery, and identification of risk factors for mortality. Secondary research priorities included dynamic CRRT monitoring, predicting CRRT-related complications, and automated data pooling for point-of-care analysis. Literature gaps included prospective validation and implementation, biases ascertainment, and evaluation of AI-generated health care disparities. Research on AI applications to enhance CRRT delivery has grown exponentially in the last years, but the field remains premature. There is a need to evaluate how these applications could enhance bedside decision-making capacity and assist structure and processes of CRRT delivery.

Medienart:

E-Artikel

Erscheinungsjahr:

2022

Erschienen:

2022

Enthalten in:

Zur Gesamtaufnahme - volume:29

Enthalten in:

Advances in chronic kidney disease - 29(2022), 5 vom: 15. Sept., Seite 439-449

Sprache:

Englisch

Beteiligte Personen:

Hammouda, Nada [VerfasserIn]
Neyra, Javier A [VerfasserIn]

Links:

Volltext

Themen:

Artificial intelligence
CRRT
Continuous renal replacement therapy
Information technology
Journal Article
Machine learning
Review

Anmerkungen:

Date Completed 19.10.2022

Date Revised 16.12.2023

published: Print

Citation Status MEDLINE

doi:

10.1053/j.ackd.2022.08.001

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM347679218