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 3rd; Barbara E Engelhardt; Krzysztof Laudanski

doi:10.3390/jpm12050661

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

J Pers Med. 2022 Apr 20;12(5):661. doi: 10.3390/jpm12050661.

Authors

Niranjani Prasad¹, Aishwarya Mandyam^{1

2}, Corey Chivers³, Michael Draugelis³, C William Hanson 3rd^{3

4}, Barbara E Engelhardt^{1

2}, Krzysztof Laudanski^{4

5

6}

Affiliations

¹ Department of Computer Science, Princeton University, Princeton, NJ 08540, USA.
² Gladstone Institutes, San Francisco, CA 94158, USA.
³ University of Pennsylvania Health System, Philadelphia, PA 19104, USA.
⁴ Department of Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, PA 19104, USA.
⁵ Penn Medicine Predictive Healthcare, University of Pennsylvania Health System, Philadelphia, PA 19104, USA.
⁶ Leonard Davis Institute of Healthcare Economics, University of Pennsylvania, Philadelphia, PA 19104, USA.

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.

Keywords: MIMIC-IV; artificial intelligence; decision support systems; electrolytes; electronic health records; machine learning; reinforcement learning; retrospective studies.