Development and validation of a machine learning model for classification of next glucose measurement in hospitalized patients. (February 2022)

• Record Type: Journal Article
• Authors: Zale, Andrew D.; Abusamaan, Mohammed S.; McGready, John; Mathioudakis, Nestoras
• Is Part Of: EClinicalMedicine. Volume 44(2022)
• Publication Date: 2022-02
• Subjects: BG blood glucose -- EMR electronic medical record -- NPO nil per os -- ICU intensive care unit -- BMI body mass index -- T1DM type 1 diabetes mellitus -- T2DM type 2 diabetes mellitus -- AUC area under receiver operating curve -- PPV positive predictive value -- NPV negative predictive value -- PLR positive likelihood ratio -- NLR negative likelihood ratio -- CGM continuous glucose monitor -- ICD International Classification of Diseases; Medicine -- Research -- Periodicals; Medical policy -- Periodicals; Clinical Medicine; Health Policy; Public Health; Medical policy; Medicine -- Research; Periodical; Electronic journals; Periodicals; 613
• DOI: 10.1016/j.eclinm.2022.101290
• Languages: English
• ISSNs: 2589-5370

Abstract

Summary: Background: Inpatient glucose management can be challenging due to evolving factors that influence a patient's blood glucose (BG) throughout hospital admission. The purpose of our study was to predict the category of a patient's next BG measurement based on electronic medical record (EMR) data. Methods: EMR data from 184, 361 admissions containing 4, 538, 418 BG measurements from five hospitals in the Johns Hopkins Health System were collected from patients who were discharged between January 1, 2015 and May 31, 2019. Index BGs used for prediction included the 5th to penultimate BG measurements ( N = 2, 740, 539). The outcome was category of next BG measurement: hypoglycemic (BG ≤ 70 mg/dl), controlled (BG 71–180 mg/dl), or hyperglycemic (BG > 180 mg/dl). A random forest algorithm that included a broad range of clinical covariates predicted the outcome and was validated internally and externally. Findings: In our internal validation test set, 72·8%, 25·7%, and 1·5% of BG measurements occurring after the index BG were controlled, hyperglycemic, and hypoglycemic respectively. The sensitivity/specificity for prediction of controlled, hyperglycemic, and hypoglycemic were 0·77/0·81, 0·77/0·89, and 0·73/0·91, respectively. On external validation in four hospitals, the ranges of sensitivity/specificity for prediction of controlled, hyperglycemic, and hypoglycemic were 0·64–0·70/0·80–0·87, 0·75–0·80/0·82–0·84, and 0·76–0·78/0·87–0·90, respectively. Interpretation: A machine learning algorithm using EMR data can accurately predict the category of a hospitalized patient's next BG measurement. Further studies should determine the effectiveness of integration of this model into the EMR in reducing rates of hypoglycemia and hyperglycemia. Funding: This study was supported by grant K23DK111986 from the National Institute for Diabetes and Digestive and Kidney Diseases (Drs Mathioudakis, Abusamaan, and McGready).