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

Niranjani Prasad, Aishwarya Mandyam, Corey Chivers, Michael Draugelis, C. William Hanson, Barbara E. Engelhardt, Krzysztof Laudanski

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Article number661
JournalJournal of Personalized Medicine
Volume12
Issue number5
DOIs
StatePublished - May 2022

All Science Journal Classification (ASJC) codes

  • Medicine (miscellaneous)

Keywords

  • artificial intelligence
  • decision support systems
  • electrolytes
  • electronic health records
  • machine learning
  • MIMIC-IV
  • reinforcement learning
  • retrospective studies

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