Clinical Prediction Tool To Identify Adults With Type 2 Diabetes at Risk for Persistent Adverse Glycemia in Hospital

Kyi, Mervyn; Gorelik, Alexandra; Reid, Jane; Rowan, Lois M.; Wraight, Paul R; Colman, Peter G; Fourlanos, Spiros

doi:10.1016/j.jcjd.2020.06.006

Cited by 11 publications

(7 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As previous research has suggested that self-monitoring BG underpredicts low and high BG indices owing to the sparser data points compared with CGM, there is a compelling need to improve on predictive accuracy using POC BG measurements [23]. Most published machine learning prediction models in the inpatient setting have been developed for binary [24][25][26][27][28][29][30][31][32][33][34] (ie, hypoglycemia vs not) or categorical [35,36] glucose outcomes (ie, controlled, hyperglycemic, and hypoglycemia) rather than a continuous glucose outcome [37] (ie, glucose value) [38]. We have previously published models that seek to predict hypoglycemia by considering BG as a categorical variable [24,35].…”

Section: Objectivesmentioning

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

View full text Add to dashboard Cite

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.

show abstract

Section: Objectivesmentioning

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

View full text Add to dashboard Cite

show abstract

“…Despite these concerns, sulfonylureas are used in up to 20% of hospitalized patients in the USA and UK, but data on prevalence use is limited [45,74,85]. As suggested by two recent risk prediction models, the use of sulfonylureas in the hospital is associated with hypoglycemia [86,87]. The rates of hypoglycemia ranged from ~20 to 30% [45, 85,88].…”

Section: Use Of Sulfonylureasmentioning

confidence: 99%

Safety and Efficacy of Inpatient Diabetes Management with Non-insulin Agents: an Overview of International Practices

et al. 2022

View full text Add to dashboard Cite

Purpose of ReviewThe field of inpatient diabetes has advanced significantly over the last 20 years, leading to the development of personalized treatment approaches. However, outdated guidelines still recommend the use of basal-bolus insulin therapy as the preferred treatment approach, and against the use of non-insulin anti-hyperglycemic agents. Recent Findings Several observational and prospective randomized controlled studies have demonstrated that oral antihyperglycemic agents are widely used in the hospital, including studies of DPP-4 agents and GLP-1 agonists. Summary With advances in the field of inpatient diabetes management, a paradigm shift has occurred, from an approach of recommending "basal-bolus regimens" for all patients to a more precision medicine option for hospitalized non-critically ill patients with type 2 diabetes. Keywords Inpatient hyperglycemia • Hospital diabetesThis article is part of the Topical Collection on Hospital Management of Diabetes

show abstract

“…Upon externally validating their model on the MIMIC-III data, they achieved an AUC of 0.79. Another approach to incorporating both hypoglycemia and hyperglycemia into glucose prediction was presented by Kyi et al [ 43 ] Their model sought to predict persistent dysglycemia (glucose < 72 mg/dL or > 270 mg/dL on two admission days) at the time of admission. Their dataset included 594 patients and ten predictors such as admission dysglycemia, HbA1c ≥ 8.1%, sulfonylurea or insulin use, glucocorticoid use, Charlson Comorbidity Index score, and admission days.…”

Section: Machine Learning Models For Inpatient Glucose Predictionmentioning

confidence: 99%

Machine Learning Models for Inpatient Glucose Prediction

Zale

Mathioudakis

2022

Curr Diab Rep

View full text Add to dashboard Cite

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.

show abstract

Clinical Prediction Tool To Identify Adults With Type 2 Diabetes at Risk for Persistent Adverse Glycemia in Hospital

Cited by 11 publications

References 30 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

Safety and Efficacy of Inpatient Diabetes Management with Non-insulin Agents: an Overview of International Practices

Machine Learning Models for Inpatient Glucose Prediction

Contact Info

Product

Resources

About