Prediction of Next Glucose Measurement in Hospitalized Patients by Comparing Various Regression Methods: Retrospective Cohort Study

Abstract

Background Continuous glucose monitors have shown great promise in improving outpatient blood glucose (BG) control; however, continuous glucose monitors are not routinely used in hospitals, and glucose management is driven by point-of-care (finger stick) and serum glucose measurements in most patients. Objective This study aimed to evaluate times series approaches for prediction of inpatient BG using only point-of-care and serum glucose observations. Methods Our data set included electronic health record data from 184,320 admissions, from patients who received at least one unit of subcutaneous insulin, had at least 4 BG measurements, and were discharged between January 1, 2015, and May 31, 2019, from 5 Johns Hopkins Health System hospitals. A total of 2,436,228 BG observations were included after excluding measurements obtained in quick succession, from patients who received intravenous insulin, or from critically ill patients. After exclusion criteria, 2.85% (3253/113,976), 32.5% (37,045/113,976), and 1.06% (1207/113,976) of admissions had a coded diagnosis of type 1, type 2, and other diabetes, respectively. The outcome of interest was the predicted value of the next BG measurement (mg/dL). Multiple time series predictors were created and analyzed by comparing those predictors and the index BG measurement (sample-and-hold technique) with next BG measurement. The population was classified by glycemic variability based on the coefficient of variation. To compare the performance of different time series predictors among one another, R2, root mean squared error, and Clarke Error Grid were calculated and compared with the next BG measurement. All these time series predictors were then used together in Cubist, linear, random forest, partial least squares, and k-nearest neighbor methods. Results The median number of BG measurements from 113,976 admissions was 12 (IQR 5-24). The R2 values for the sample-and-hold, 2-hour, 4-hour, 16-hour, and 24-hour moving average were 0.529, 0.504, 0.481, 0.467, and 0.459, respectively. The R2 values for 4-hour moving average based on glycemic variability were 0.680, 0.480, 0.290, and 0.205 for low, medium, high, and very high glucose variability, respectively. The proportion of BG predictions in zone A of the Clarke Error Grid analysis was 61%, 59%, 27%, and 53% for 4-hour moving average, 24-hour moving average, 3 observation rolling regression, and recursive regression predictors, respectively. In a fully adjusted Cubist, linear, random forest, partial least squares, and k-nearest neighbor model, the R2 values were 0.563, 0.526, 0.538, and 0.472, respectively. Conclusions When analyzing time series predictors independently, increasing variability in a patient’s BG decreased predictive accuracy. Similarly, inclusion of older BG measurements decreased predictive accuracy. These relationships become weaker as glucose variability increases. Machine learning techniques marginally augmented the performance of time series predictors for predicting a patient’s next BG measurement. Further studies should determine the potential of using time series analyses for prediction of inpatient dysglycemia.

Medications that Regulate Gastrointestinal Transit Influence Inpatient Blood Glucose

“…We used a mixed effects model, which can handle repeated measures by accounting for the correlation between encounters of the same patient. Our model had comparable performance to a linear model that used a 24 hour moving average of inpatient BG measurements collected from EHRs to predict the next BG; R2 was 0.45 using all observations, and performance varied based on glycemic variability category (very high glycemic variability R2=0.14 to low glycemic variability R2=0.65) 38 . The focus of our study was to identify potentially undiscovered medication predictors of BG, rather development of a high-performing clinical model.…”

“…The linear regression method employed did not use time series data. However, a study using linear, cubist, random forest and K-nearest neighbors models to predict the next BG using previous BGs over various moving average and rolling regression windows found no difference in performance (R2 CI's overlapped) 38 . Future directions include inclusion of more patient encounters to the model and assessment of its generalizability.…”

“…Mean and last input BG were positively correlated with output BG, and retained significance across lookback windows. Studies show that prior BG is important in predicting future BG 38 . Additionally, history of T2DM was strongly positively correlated with output BG across all three lookback windows.…”