Development and Validation of a Machine Learning Model to Predict Near-Term Risk of Iatrogenic Hypoglycemia in Hospitalized Patients

Key Points Question Can the risk of iatrogenic hypoglycemia resulting from insulin or insulin secretagogues be predicted continuously throughout hospitalization without the use of continuous glucose monitors? Findings In this cohort study of 54 978 admissions in a large US health care system, a stochastic gradient boosting machine learning model using 43 static and time-varying clinical predictors available in the electronic medical record accurately predicted the risk of iatrogenic hypoglycemia in a prediction horizon of 24 hours from the time of each point-of-care and serum glucose measurement throughout a patient’s hospital admission. Meaning These findings suggest that translating this machine learning prediction model into a real-time informatics alert embedded in the electronic medical record has the potential to reduce the incidence of iatrogenic hypoglycemia, a serious adverse event.