Development and validation of a machine learning model for classification of next glucose measurement in hospitalized patients

Zale, Andrew D.; Abusamaan, Mohammed S.; McGready, John; Mathioudakis, Nestoras

doi:10.1016/j.eclinm.2022.101290

Cited by 9 publications

(12 citation statements)

References 36 publications

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“…Previously published glucose prediction in hospitalized patients has focused on predicting future hypoglycemia over the prediction horizon of 24 hours [ 24 , 28 ] and a patient’s admission [ 32 ]. Recent studies have predicted future hyperglycemia and hypoglycemia as categorical outcomes [ 35 , 36 ]. Although several recent studies have used machine learning to predict the next category of glucose (ie, hypoglycemic, controlled, or hyperglycemic), there are no studies that have tried to predict the next glucose value as a continuous outcome using electronic health record data alone.…”

Section: Discussionmentioning

confidence: 99%

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

Zale¹,

Abusamaan²,

McGready³

et al. 2023

JMIR Form Res

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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.

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Section: Discussionmentioning

confidence: 99%

“…As previously reported, the characteristics of patients included from the 5 hospitals differed with respect to age (median 59-74 years), sex (45.7%-51.2% male), and race (53.3%-64.5% White). The percentage of patients who were prescribed insulin at home in each of the 5 hospitals ranged from 7.6% to 14.5% [ 35 ].…”

Section: Methodsmentioning

confidence: 99%

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

Zale¹,

Abusamaan²,

McGready³

et al. 2023

JMIR Form Res

Self Cite

View full text Add to dashboard Cite

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“…[ 28 ] For example, choosing more restrictive hypoglycemia cutoffs (< 54 mg/dL vs. < 70 mg/dL) or narrowing the prediction horizon (hypoglycemia with next glucose measurement vs. at any time during admission) will decrease the prevalence of the outcome, thereby affecting the sensitivity and specificity of the model (i.e., it can be harder to accurately predict a more rare outcome). [ 29 ]…”

Section: Phases Of Machine Learning Researchmentioning

confidence: 99%

“…Risk of hypoglycemia is inherently dynamic within a patient’s hospital course and being able to incorporate new data with a shorter prediction horizon allows for more clinically meaningful predictions. Zale et al published this year a random forest model employing 59 predictors that classified a patient as hypoglycemic, controlled, and hyperglycemic using a large dataset with over 100,000 patients from five hospitals [ 29 ]. Unlike earlier glycemic prediction models, this model predicted the glycemic outcome for the next glucose observation at the time of each index glucose measurement, with the interquartile range for time to next glucose measurement of 1.63 to 4.37 h. Four of those hospitals were used for external validation and achieved sensitivities for controlled, hyperglycemic, and hypoglycemic of 0.64–0.70, 0.75–0.80, and 0.76–0.78, respectively; specificities for controlled, hyperglycemic, and hypoglycemic ranged from 0.80 to 0.87, 0.82 to 0.84, and 0.87 to 0.90, respectively, across the external validation hospitals.…”

Section: Machine Learning Models For Inpatient Glucose Predictionmentioning

confidence: 99%

Machine Learning Models for Inpatient Glucose Prediction

Zale

Mathioudakis

2022

Curr Diab Rep

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Purpose of Review Glucose management in the hospital is difficult due to non-static factors such as antihyperglycemic and steroid doses, renal function, infection, surgical status, and diet. Given these complex and dynamic factors, machine learning approaches can be leveraged for prediction of glucose trends in the hospital to mitigate and prevent suboptimal hypoglycemic and hyperglycemic outcomes. Our aim was to review the clinical evidence for the role of machine learning–based models in predicting hospitalized patients’ glucose trajectory. Recent Findings The published literature on machine learning algorithms has varied in terms of population studied, outcomes of interest, and validation methods. There have been tools developed that utilize data from both continuous glucose monitors and large electronic health records (EHRs). With increasing sample sizes, inclusion of a greater number of predictor variables, and use of more advanced machine learning algorithms, there has been a trend in recent years towards increasing predictive accuracy for glycemic outcomes in the hospital setting. While current models predicting glucose trajectory offer promising results, they have not been tested prospectively in the clinical setting. Summary Accurate machine learning algorithms have been developed and validated for prediction of hypoglycemia and hyperglycemia in the hospital. Further work is needed in implementation/integration of machine learning models into EHR systems, with prospective studies to evaluate effectiveness and safety of such clinical decision support on glycemic and other clinical outcomes.

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“…Challenges of the approach include confounding by medical interventions, incomplete observations, and selection bias. While several machine learning algorithms have shown high predictive accuracy in the hospital setting, [1][2][3][4][5][6][7][8] most models have not yet been deployed in the EHR and evaluated prospectively with respect to clinical outcomes. The use of machine learning in healthcare is a dynamic process and algorithms need regular updates as new drugs are brought to market.…”

Section: Machine Learning Glycemic Predictions For the Hospital Using...mentioning

confidence: 99%

Hospital Diabetes Meeting 2022

Huang

Yeung

Nguyen

et al. 2022

J Diabetes Sci Technol

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The annual Virtual Hospital Diabetes Meeting was hosted by Diabetes Technology Society on April 1 and April 2, 2022. This meeting brought together experts in diabetes technology to discuss various new developments in the field of managing diabetes in hospitalized patients. Meeting topics included (1) digital health and the hospital, (2) blood glucose targets, (3) software for inpatient diabetes, (4) surgery, (5) transitions, (6) coronavirus disease and diabetes in the hospital, (7) drugs for diabetes, (8) continuous glucose monitoring, (9) quality improvement, (10) diabetes care and educatinon, and (11) uniting people, process, and technology to achieve optimal glycemic management. This meeting covered new technology that will enable better care of people with diabetes if they are hospitalized.

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Development and validation of a machine learning model for classification of next glucose measurement in hospitalized patients

Cited by 9 publications

References 36 publications

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

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

Machine Learning Models for Inpatient Glucose Prediction

Hospital Diabetes Meeting 2022

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