Dataset Information

Prediction of Nocturnal Hypoglycemia in Adults with Type 1 Diabetes under Multiple Daily Injections Using Continuous Glucose Monitoring and Physical Activity Monitor.

ABSTRACT: (1) Background: nocturnal hypoglycemia (NH) is one of the most challenging side effects of multiple doses of insulin (MDI) therapy in type 1 diabetes (T1D). This work aimed to investigate the feasibility of a machine-learning-based prediction model to anticipate NH in T1D patients on MDI. (2) Methods: ten T1D adults were studied during 12 weeks. Information regarding T1D management, continuous glucose monitoring (CGM), and from a physical activity tracker were obtained under free-living conditions at home. Supervised machine-learning algorithms were applied to the data, and prediction models were created to forecast the occurrence of NH. Individualized prediction models were generated using multilayer perceptron (MLP) and a support vector machine (SVM). (3) Results: population outcomes indicated that more than 70% of the NH may be avoided with the proposed methodology. The predictions performed by the SVM achieved the best population outcomes, with a sensitivity and specificity of 78.75% and 82.15%, respectively. (4) Conclusions: our study supports the feasibility of using ML techniques to address the prediction of nocturnal hypoglycemia in the daily life of patients with T1D on MDI, using CGM and a physical activity tracker.

SUBMITTER: Bertachi A

PROVIDER: S-EPMC7147466 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Prediction of Nocturnal Hypoglycemia in Adults with Type 1 Diabetes under Multiple Daily Injections Using Continuous Glucose Monitoring and Physical Activity Monitor.

Bertachi Arthur A Viñals Clara C Biagi Lyvia L Contreras Ivan I Vehí Josep J Conget Ignacio I Giménez Marga M

Sensors (Basel, Switzerland) 20200319 6

(1) Background: nocturnal hypoglycemia (NH) is one of the most challenging side effects of multiple doses of insulin (MDI) therapy in type 1 diabetes (T1D). This work aimed to investigate the feasibility of a machine-learning-based prediction model to anticipate NH in T1D patients on MDI. (2) Methods: ten T1D adults were studied during 12 weeks. Information regarding T1D management, continuous glucose monitoring (CGM), and from a physical activity tracker were obtained under free-living conditio ...[more]

PMID: 32204318

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Project description:Tight glycemic control can potentially reduce morbidity and mortality in the intensive care unit but increases the risk of hypoglycemia. The most effective means to avoid hypoglycemia is to obtain frequent blood glucose samples, but this increases the burden to nursing staff. The objective of this study was to assess the ability of a real-time continuous glucose monitor to reduce hypoglycemia (blood glucose <60 mg/dL [3.3 mmol/L]) during standard care or tight glycemic control effected with a proportional integral derivative insulin titration algorithm.Real-time continuous glucose monitor profiles obtained from an ongoing prospective randomized trial of tight glycemic control were retrospectively analyzed to determine whether the continuous glucose measure had prevented instances of hypoglycemia.Cardiac intensive care unit.Children 3 yrs of age or younger undergoing cardiac surgery were studied.Intravenous insulin infusion and rescue glucose guided by the real-time continuous glucose monitor and the proportional integral derivative algorithm in the tight glycemic control arm (n = 155; target glucose 80-110 mg/dL [4.4-6.1 mmol/L]) and the real-time continuous glucose monitor in the standard care arm (n = 156).No reduction in hypoglycemia was observed with real-time continuous glucose monitor alarms set at 60 mg/dL (3.3 mmol/L) (zero of 19 occurrences of blood glucose <60 mg/dL [3.3 mmol/L] detected); 18 of 40 subsequent incidences of hypoglycemia were detected after the alarm threshold was increased to 70 mg/dL (3.9 mmol/L). In the tight glycemic control arm, eight incidences were reduced in duration and an additional eight events were prevented with intravenous glucose. In the standard care arm, three of nine occurrences of hypoglycemia were detected with the duration reduced in all cases. On average, one to two false hypoglycemia alarms were observed in each patient.The real-time continuous glucose monitor in combination with proportional integral derivative control can reduce hypoglycemia during tight glycemic control. The real-time continuous glucose monitor can also reduce hypoglycemia during standard care. However, false alarms increase the overall nursing workload.

Dataset Information

Prediction of Nocturnal Hypoglycemia in Adults with Type 1 Diabetes under Multiple Daily Injections Using Continuous Glucose Monitoring and Physical Activity Monitor.

Publications

Prediction of Nocturnal Hypoglycemia in Adults with Type 1 Diabetes under Multiple Daily Injections Using Continuous Glucose Monitoring and Physical Activity Monitor.

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