596-P: Machine Learning Prediction of Blood Glucose in Hospitalized Patients

Abstract

Background: Inpatient glucose management can be challenging due to various evolving factors that influence a patient's blood glucose (BG). Providers could benefit from a clinical decision support tool that predicts the trajectory of a patient's BG reading. The purpose of our study was to predict the category of a patient's next BG reading based on electronic medical record (EMR) data. Methods: EMR data from 184,361 admissions, containing 4,538,418 BG readings from five hospitals in the Johns Hopkins Health System were collected over a 4.5 year period. The outcome was category of next BG reading: hypoglycemic (BG <=70 mg/dl), controlled (BG 71-180 mg/dl), or hyperglycemic (BG >180 mg/dl). A LogitBoost machine learning algorithm that included a broad range of clinical predictors was used to predict the outcome and validated internally (within one hospital) and externally (between different hospitals). Results: Our machine learning model achieved 86.2% (95% CI: 86.1%-86.2%) accuracy on internal validation and 80.4%-83.2% on external validation. On internal validation, the positive likelihood ratio (+LR) for a prediction of controlled, hyperglycemic, and hypoglycemic were 2.4, 12.3, and 47.4 respectively; the negative likelihood ratio (-LR) for a prediction of controlled, hyperglycemic, and hypoglycemic were 0.09, 0.38, and 0.99 on internal validation. From a safety standpoint, only 0.23% of hyperglycemic observations were predicted to be hypoglycemic on internal validation. On external validation, the +LR for prediction of controlled, hyperglycemic, and hypoglycemic were 2.2-2.8, 6.3-8.5, and 23.1-62.7; the -LR for a prediction of controlled, hyperglycemic, and hypoglycemic were 0.13-0.16, 0.32-0.42, and 0.98-0.99. Conclusions: A machine learning algorithm accurately predicts the category of a patient's next BG reading. Further studies should determine the success of implementing this model into the EMR to decrease the rates of hypoglycemia and hyperglycemia in hospitalized patients. Disclosure A. D. Zale: None. M. S. Abusamaan: None. N. Mathioudakis: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (K23DK111986)