Cargando…

Guiding Efficient, Effective, and Patient-Oriented Electrolyte Replacement in Critical Care: An Artificial Intelligence Reinforcement Learning Approach

Both provider- and protocol-driven electrolyte replacement have been linked to the over-prescription of ubiquitous electrolytes. Here, we describe the development and retrospective validation of a data-driven clinical decision support tool that uses reinforcement learning (RL) algorithms to recommen...

Descripción completa

Detalles Bibliográficos
Autores principales: Prasad, Niranjani, Mandyam, Aishwarya, Chivers, Corey, Draugelis, Michael, Hanson, C. William, Engelhardt, Barbara E., Laudanski, Krzysztof
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9143326/
https://www.ncbi.nlm.nih.gov/pubmed/35629084
http://dx.doi.org/10.3390/jpm12050661
_version_ 1784715778433482752
author Prasad, Niranjani
Mandyam, Aishwarya
Chivers, Corey
Draugelis, Michael
Hanson, C. William
Engelhardt, Barbara E.
Laudanski, Krzysztof
author_facet Prasad, Niranjani
Mandyam, Aishwarya
Chivers, Corey
Draugelis, Michael
Hanson, C. William
Engelhardt, Barbara E.
Laudanski, Krzysztof
author_sort Prasad, Niranjani
collection PubMed
description Both provider- and protocol-driven electrolyte replacement have been linked to the over-prescription of ubiquitous electrolytes. Here, we describe the development and retrospective validation of a data-driven clinical decision support tool that uses reinforcement learning (RL) algorithms to recommend patient-tailored electrolyte replacement policies for ICU patients. We used electronic health records (EHR) data that originated from two institutions (UPHS; MIMIC-IV). The tool uses a set of patient characteristics, such as their physiological and pharmacological state, a pre-defined set of possible repletion actions, and a set of clinical goals to present clinicians with a recommendation for the route and dose of an electrolyte. RL-driven electrolyte repletion substantially reduces the frequency of magnesium and potassium replacements (up to 60%), adjusts the timing of interventions in all three electrolytes considered (potassium, magnesium, and phosphate), and shifts them towards orally administered repletion over intravenous replacement. This shift in recommended treatment limits risk of the potentially harmful effects of over-repletion and implies monetary savings. Overall, the RL-driven electrolyte repletion recommendations reduce excess electrolyte replacements and improve the safety, precision, efficacy, and cost of each electrolyte repletion event, while showing robust performance across patient cohorts and hospital systems.
format Online
Article
Text
id pubmed-9143326
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-91433262022-05-29 Guiding Efficient, Effective, and Patient-Oriented Electrolyte Replacement in Critical Care: An Artificial Intelligence Reinforcement Learning Approach Prasad, Niranjani Mandyam, Aishwarya Chivers, Corey Draugelis, Michael Hanson, C. William Engelhardt, Barbara E. Laudanski, Krzysztof J Pers Med Article Both provider- and protocol-driven electrolyte replacement have been linked to the over-prescription of ubiquitous electrolytes. Here, we describe the development and retrospective validation of a data-driven clinical decision support tool that uses reinforcement learning (RL) algorithms to recommend patient-tailored electrolyte replacement policies for ICU patients. We used electronic health records (EHR) data that originated from two institutions (UPHS; MIMIC-IV). The tool uses a set of patient characteristics, such as their physiological and pharmacological state, a pre-defined set of possible repletion actions, and a set of clinical goals to present clinicians with a recommendation for the route and dose of an electrolyte. RL-driven electrolyte repletion substantially reduces the frequency of magnesium and potassium replacements (up to 60%), adjusts the timing of interventions in all three electrolytes considered (potassium, magnesium, and phosphate), and shifts them towards orally administered repletion over intravenous replacement. This shift in recommended treatment limits risk of the potentially harmful effects of over-repletion and implies monetary savings. Overall, the RL-driven electrolyte repletion recommendations reduce excess electrolyte replacements and improve the safety, precision, efficacy, and cost of each electrolyte repletion event, while showing robust performance across patient cohorts and hospital systems. MDPI 2022-04-20 /pmc/articles/PMC9143326/ /pubmed/35629084 http://dx.doi.org/10.3390/jpm12050661 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Prasad, Niranjani
Mandyam, Aishwarya
Chivers, Corey
Draugelis, Michael
Hanson, C. William
Engelhardt, Barbara E.
Laudanski, Krzysztof
Guiding Efficient, Effective, and Patient-Oriented Electrolyte Replacement in Critical Care: An Artificial Intelligence Reinforcement Learning Approach
title Guiding Efficient, Effective, and Patient-Oriented Electrolyte Replacement in Critical Care: An Artificial Intelligence Reinforcement Learning Approach
title_full Guiding Efficient, Effective, and Patient-Oriented Electrolyte Replacement in Critical Care: An Artificial Intelligence Reinforcement Learning Approach
title_fullStr Guiding Efficient, Effective, and Patient-Oriented Electrolyte Replacement in Critical Care: An Artificial Intelligence Reinforcement Learning Approach
title_full_unstemmed Guiding Efficient, Effective, and Patient-Oriented Electrolyte Replacement in Critical Care: An Artificial Intelligence Reinforcement Learning Approach
title_short Guiding Efficient, Effective, and Patient-Oriented Electrolyte Replacement in Critical Care: An Artificial Intelligence Reinforcement Learning Approach
title_sort guiding efficient, effective, and patient-oriented electrolyte replacement in critical care: an artificial intelligence reinforcement learning approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9143326/
https://www.ncbi.nlm.nih.gov/pubmed/35629084
http://dx.doi.org/10.3390/jpm12050661
work_keys_str_mv AT prasadniranjani guidingefficienteffectiveandpatientorientedelectrolytereplacementincriticalcareanartificialintelligencereinforcementlearningapproach
AT mandyamaishwarya guidingefficienteffectiveandpatientorientedelectrolytereplacementincriticalcareanartificialintelligencereinforcementlearningapproach
AT chiverscorey guidingefficienteffectiveandpatientorientedelectrolytereplacementincriticalcareanartificialintelligencereinforcementlearningapproach
AT draugelismichael guidingefficienteffectiveandpatientorientedelectrolytereplacementincriticalcareanartificialintelligencereinforcementlearningapproach
AT hansoncwilliam guidingefficienteffectiveandpatientorientedelectrolytereplacementincriticalcareanartificialintelligencereinforcementlearningapproach
AT engelhardtbarbarae guidingefficienteffectiveandpatientorientedelectrolytereplacementincriticalcareanartificialintelligencereinforcementlearningapproach
AT laudanskikrzysztof guidingefficienteffectiveandpatientorientedelectrolytereplacementincriticalcareanartificialintelligencereinforcementlearningapproach