Avoidable Serum Potassium Testing in the Cardiac ICU: Development and Testing of a Machine-Learning Model

Area of Science:

• Pediatric cardiac surgery
• Clinical informatics
• Machine learning in healthcare

Background:

• Serum potassium monitoring is crucial for pediatric patients post-cardiac surgery.
• Frequent blood draws for potassium can lead to complications like anemia and infection.
• Optimizing blood draw frequency is essential for patient safety and resource management.

Purpose of the Study:

• To develop and validate a machine learning model to identify potentially avoidable serum potassium blood draws in pediatric cardiac surgery patients.
• To assess the accuracy and potential impact of such a model in a real-world clinical setting.

Main Methods:

• Retrospective cohort study at a tertiary-care center.
• Utilized machine learning (random forest classifiers) to predict normal potassium levels.
• Included variables such as serum chemistry, fluid balance, and renal function markers.
• Models were developed and validated on a large dataset of pediatric patients.

Main Results:

• The study included 7,269 admissions (6,196 patients) from January 2010 to December 2018.
• Machine learning models achieved a median positive predictive value of 0.900 for predicting normal potassium.
• A median of 27.2% of serum potassium samples were predicted as normal and potentially avoidable.
• The models demonstrated a low rate of incorrectly predicting abnormal potassium as normal.

Conclusions:

• Machine learning models can accurately predict potentially avoidable serum potassium blood tests in critically ill pediatric patients.
• Implementing such models can lead to significant reductions in unnecessary blood draws, decreasing costs and patient risks.
• This technology offers a promising approach to optimize laboratory testing in intensive care settings.