Predicting hypoglycemia in hospitalized patients with diabetes: A derivation and validation study

Elbaz, Meyer; Nashashibi, Jeries; Kushnir, Shiri; Leibovici, Leonard

doi:10.1016/j.diabres.2020.108611

Cited by 11 publications

(13 citation statements)

References 16 publications

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“…Previous research by Elbaz et al demonstrated that stratification by class probability derived from P-values from a logistic regression output could improve model performance in predicting hypoglycemia. 23 The highest performing model was a random forest classification model with 35 random variables in each node. We used the cutpointr package in R to maximize the sum of sensitivity and specificity for each class to determine probability cutoffs.…”

Section: Model Selection and Developmentmentioning

confidence: 99%

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

et al. 2022

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Section: Model Selection and Developmentmentioning

confidence: 99%

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

et al. 2022

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“…After an initial screening, the remaining 56 published studies were screened by full text; 41 were excluded after the screening of the full text (reasons outlined in Figure 1). The remaining 15 studies met our eligible criteria (Chandran et al, 2019; Chow et al, 2018; Claydon-Platt et al, 2014; Elbaz et al, 2021; Ena et al, 2018; K. Han et al, 2018; Heller et al, 2020; Hu et al, 2020; Karter et al, 2017; Lagani et al, 2015; Misra-Hebert et al, 2020; Murata et al, 2004; Ruan et al, 2020; Schroeder et al, 2017; Shah et al, 2019). In addition, we found one eligible study by hand-search (Mathioudakis et al, 2021).…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The prognostic prediction model for hypoglycemia has been developed to distinguish diabetic patients with different levels of hypoglycemia risk (Elbaz et al, 2021; Misra-Hebert et al, 2020; Ruan et al, 2020; Shah et al, 2019). For instance, Elbaz et al used the logistic regression equation to develop a prognostic prediction model for hypoglycemia in hospitalized patients with diabetes and the derived model performed well in the validation cohorts (C-statistic was 0.71–0.72; (Elbaz et al, 2021). However, due to differences in study design, predictors, modeling methods, and other problems in the development process, there are large differences among prediction models for hypoglycemia prognosis, which makes the clinical practicality of prediction models for hypoglycemia prognosis poor.…”

Section: Introductionmentioning

confidence: 99%

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Prediction Models for Prognosis of Hypoglycemia in Patients with Diabetes: A Systematic Review and Meta-Analysis

Zhang

et al. 2022

Biological Research For Nursing

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Objective To systematically summarize the reported prediction models for hypoglycemia in patients with diabetes, compare their performance, and evaluate their applicability in clinical practice. Methods We selected studies according to the PRISMA, appraised studies according to the Prediction model Risk of Bias Assessment Tool (PROBAST), and extracted and synthesized the data according to the CHARMS. The databases of PubMed, Web of Science, Embase, and Cochrane Library were searched from inception to 31 October 2021 using a systematic review approach to capture all eligible studies developing and/or validating a prognostic prediction model for hypoglycemia in patients with diabetes. The risk bias and clinical applicability were assessed using the PROBAST. The meta-analysis of the performance of the prediction models were also conducted. The protocol of this study was recorded in PROSPERO (CRD42022309852). Results Sixteen studies with 22 models met the eligible criteria. The predictors with the high frequency of occurrence among all models were age, HbA1c, history of hypoglycemia, and insulin use. A meta-analysis of C-statistic was performed for 21 prediction models, and the summary C-statistic and its 95% confidence interval and prediction interval were 0.7699 (0.7299–0.8098), 0.7699 (0.5862–0.9536), respectively. Heterogeneity exists between different hypoglycemia prediction models (τ2 was 0.00734≠0). Conclusions The existing predictive models are not recommended for widespread clinical use. A high-quality hypoglycemia screening tool should be developed in future studies.

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“…Since the publication of those two studies in 2018, researchers have employed logistic regression in large datasets to answer different questions about predicting inpatient hypoglycemia. Elbaz et al asked if they could predict glucose ≤ 70 mg/dL in the first week of a patient’s admission [ 44 ]. Their dataset included a training set and two validation sets of 3,605, 2,425, and 3,635 patients, respectively.…”

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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Predicting hypoglycemia in hospitalized patients with diabetes: A derivation and validation study

Cited by 11 publications

References 16 publications

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

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

Prediction Models for Prognosis of Hypoglycemia in Patients with Diabetes: A Systematic Review and Meta-Analysis

Machine Learning Models for Inpatient Glucose Prediction

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