The clinical impact of hypoglycemia in hospitalized patients

Borzı̀, Vito; Fontanella, Andrea

doi:10.4081/itjm.2015.549

Cited by 6 publications

(3 citation statements)

References 66 publications

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“…Inpatient hypoglycaemia has been implicated in the development of adverse clinical outcomes, including increased mortality . One retrospective study in Spain showed that hypoglycaemia (blood glucose <3.9 mmol/l) in 154 510 people with diabetes during their hospitalization was associated with an increased likelihood of in‐hospital mortality [odds ratio (OR) 1.12, 95% CI 1.09–1.15] .…”

Section: Impact Of Inpatient Hypoglycaemiamentioning

confidence: 99%

Importance of inpatient hypoglycaemia: impact, prediction and prevention

et al. 2019

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Hypoglycaemia is a key barrier to achieving euglycaemic control in people who are hospitalized. Inpatient hypoglycaemia has been linked to adverse clinical outcomes, including mortality and longer stay in hospital. A number of studies have applied mathematical tools and statistical models to predict inpatient hypoglycaemia and identify factors that may result in hypoglycaemic events. Several different approaches have been tested to prevent inpatient hypoglycaemia. These can be categorized as human intervention, computerized methods or application of medical devices. In this review we provide an overview of the epidemiology of inpatient hypoglycaemia and its impact on patients and hospitals. We also discuss the existing methodology used to predict inpatient hypoglycaemia and the limited number of trials performed to prevent inpatient hypoglycaemia. The review highlights the urgent need for evidence‐based methods to reduce inpatient hypoglycaemia.

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Section: Impact Of Inpatient Hypoglycaemiamentioning

confidence: 99%

Importance of inpatient hypoglycaemia: impact, prediction and prevention

et al. 2019

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“…Although only 7% experience a severe (Level 2) hypoglycemic episode, this rises to 26.9% of all patients with Type 1 diabetes with 185 people over the course of 1 week requiring injectable rescue treatment for their hypoglycemia. Inpatient hypoglycemia has been implicated in the development of adverse clinical and economic outcomes, including increased mortality [5][6][7], adverse cardiovascular outcomes [8,9] and increased duration of hospital stay [6,10,11].…”

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confidence: 99%

Predicting the risk of inpatient hypoglycemia with machine learning using electronic health records

Ruan¹,

Bellot²,

Moysova³

et al. 2020

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Objective We analyzed data from inpatients with diabetes admitted to a large university hospital to predict the risk of hypoglycemia through the use of machine learning algorithms. Research Design and Methods Four years of data was extracted from a hospital electronic health record system. This included laboratory and point-of-care blood glucose (BG) values to identify biochemical and clinically significant hypoglycaemic episodes (BG < 3.9 and < 2.9mmol/L respectively). We used patient demographics, administered medications, vital signs, laboratory results and procedures performed during the hospital stays to inform the model. Two iterations of the dataset included the doses of insulin administered and the past history of inpatient hypoglycaemia. Eighteen different prediction models were compared using the area under curve of the receiver operating characteristics (AUC_ROC) through a ten-fold cross validation. Results We analyzed data obtained from 17,658 inpatients with diabetes who underwent 32,758 admissions between July 2014 and August 2018. The predictive factors from the logistic regression model included people undergoing procedures, weight, type of diabetes, oxygen saturation level, use of medications (insulin, sulfonylurea, metformin) and albumin levels. The machine learning model with the best performance was the XGBoost model (AUC_ROC 0.96. This outperformed the logistic regression model which had an AUC_ROC of 0.75 for the estimation of the risk of clinically significant hypoglycaemia. Conclusions Advanced machine learning models are superior to logistic regression models in predicting the risk of hypoglycemia in inpatients with diabetes. Trials of such models should be conducted in real time to evaluate their utility to reduce inpatient hypoglycaemia.

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“…Although only 7% experience a severe (level 2) hypoglycemic episode, this rises to 26.9% of all patients with type 1 diabetes with 185 people over the course of 1 week requiring injectable rescue treatment for their hypoglycemia. Inpatient hypoglycemia has been implicated in the development of adverse clinical and economic outcomes, including increased mortality (5)(6)(7), adverse cardiovascular outcomes (8,9), and increased duration of hospital stay (6,10,11).…”

mentioning

confidence: 99%

Predicting the Risk of Inpatient Hypoglycemia With Machine Learning Using Electronic Health Records

et al. 2020

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We analyzed data from inpatients with diabetes admitted to a large university hospital to predict the risk of hypoglycemia through the use of machine learning algorithms. RESEARCH DESIGN AND METHODSFour years of data were extracted from a hospital electronic health record system. This included laboratory and point-of-care blood glucose (BG) values to identify biochemical and clinically significant hypoglycemic episodes (BG £3.9 and £2.9 mmol/L, respectively). We used patient demographics, administered medications, vital signs, laboratory results, and procedures performed during the hospital stays to inform the model. Two iterations of the data set included the doses of insulin administered and the past history of inpatient hypoglycemia. Eighteen different prediction models were compared using the area under the receiver operating characteristic curve (AUROC) through a 10-fold cross validation. RESULTSWe analyzed data obtained from 17,658 inpatients with diabetes who underwent 32,758 admissions between July 2014 and August 2018. The predictive factors from the logistic regression model included people undergoing procedures, weight, type of diabetes, oxygen saturation level, use of medications (insulin, sulfonylurea, and metformin), and albumin levels. The machine learning model with the best performance was the XGBoost model (AUROC 0.96). This outperformed the logistic regression model, which had an AUROC of 0.75 for the estimation of the risk of clinically significant hypoglycemia. CONCLUSIONSAdvanced machine learning models are superior to logistic regression models in predicting the risk of hypoglycemia in inpatients with diabetes. Trials of such models should be conducted in real time to evaluate their utility to reduce inpatient hypoglycemia.Hypoglycemia is a common and serious complication affecting people with diabetes (1). It is an inappropriately low blood glucose (BG) that results in significant morbidity in people with type 1 diabetes and in many people with type 2 diabetes (2). A BG level of #3.9 mmol/L is defined as level 1 hypoglycemia. A BG level of 2.9 mmol/L and lower is defined as level 2 hypoglycemia requiring immediate action, as at that level, neurogenic and neuroglycopenic symptoms begin to occur (3). Hypoglycemia can lead

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The clinical impact of hypoglycemia in hospitalized patients

Cited by 6 publications

References 66 publications

Importance of inpatient hypoglycaemia: impact, prediction and prevention

Importance of inpatient hypoglycaemia: impact, prediction and prevention

Predicting the risk of inpatient hypoglycemia with machine learning using electronic health records

Predicting the Risk of Inpatient Hypoglycemia With Machine Learning Using Electronic Health Records

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