Glu4: An open-source package for real-time forecasting and alerting post-bariatric hypoglycemia based on continuous glucose monitoring
Background: Post-bariatric hypoglycemia (PBH) is a severe and often overlooked complication of bariatric surgery (BS), characterized by dangerously low blood glucose levels after meals, particularly those high in carbohydrates. Unlike in Type 1 and Type 2 diabetes (T1D, T2D), where decision support...
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KeAi Communications Co., Ltd.
2025-12-01
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| Series: | Clinical eHealth |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2588914125000036 |
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| author | Luca Cossu Francesco Prendin Giacomo Cappon David Herzig Lia Bally Andrea Facchinetti |
| author_facet | Luca Cossu Francesco Prendin Giacomo Cappon David Herzig Lia Bally Andrea Facchinetti |
| author_sort | Luca Cossu |
| collection | DOAJ |
| description | Background: Post-bariatric hypoglycemia (PBH) is a severe and often overlooked complication of bariatric surgery (BS), characterized by dangerously low blood glucose levels after meals, particularly those high in carbohydrates. Unlike in Type 1 and Type 2 diabetes (T1D, T2D), where decision support systems (DSS) and continuous glucose monitoring (CGM) tools aid blood glucose management, no dedicated DSS exists for PBH. This leaves individuals vulnerable to recurrent, unpredictable hypoglycemia, posing significant health risks. To address this gap, we propose Glu4, an open-source software package designed to predict and notify users of impending PBH events using CGM data. Methods: Glu4 employs a two-step approach to predict PBH. A run-to-run algorithm forecasts future glucose levels using past CGM data, identifying potential hypoglycemic events 30 min in advance. An intelligent alarm system alerts users when glucose levels are predicted to drop below a critical threshold, prompting preventive action. A pilot study involving three PBH patients collected real-time glucose data to validate the system’s predictive performance. Results: The pilot study demonstrated that Glu4 reliably predicted impending hypoglycemia in all participants, providing timely alerts 30 min before glucose drops. The system showed a high specificity, with no false alarms being triggered during the monitoring period. The proactive notifications enabled participants to manage their glucose levels more effectively by taking preventive actions such as consuming rescue carbohydrates before the onset of severe hypoglycemia. Conclusions: Glu4 represents a promising tool for managing PBH, leveraging CGM data to deliver accurate, timely alerts that enable proactive intervention. By improving safety and quality of life for individuals with PBH, Glu4 addresses a critical unmet need. Future work will focus on enhancing system capabilities and conducting larger-scale studies to validate its effectiveness and refine its usability for clinical adoption. |
| format | Article |
| id | doaj-art-130ea43966ba4f9da710f40d5555a9c6 |
| institution | Kabale University |
| issn | 2588-9141 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Clinical eHealth |
| spelling | doaj-art-130ea43966ba4f9da710f40d5555a9c62025-01-24T04:45:33ZengKeAi Communications Co., Ltd.Clinical eHealth2588-91412025-12-01816Glu4: An open-source package for real-time forecasting and alerting post-bariatric hypoglycemia based on continuous glucose monitoringLuca Cossu0Francesco Prendin1Giacomo Cappon2David Herzig3Lia Bally4Andrea Facchinetti5Department of Information Engineering (DEI), University of Padova, Via G. Gradenigo 6/B, 35131 Padova, ItalyDepartment of Information Engineering (DEI), University of Padova, Via G. Gradenigo 6/B, 35131 Padova, ItalyDepartment of Information Engineering (DEI), University of Padova, Via G. Gradenigo 6/B, 35131 Padova, ItalyDepartment of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital, University of Bern, Bern, SwitzerlandDepartment of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital, University of Bern, Bern, SwitzerlandDepartment of Information Engineering (DEI), University of Padova, Via G. Gradenigo 6/B, 35131 Padova, Italy; Corresponding author.Background: Post-bariatric hypoglycemia (PBH) is a severe and often overlooked complication of bariatric surgery (BS), characterized by dangerously low blood glucose levels after meals, particularly those high in carbohydrates. Unlike in Type 1 and Type 2 diabetes (T1D, T2D), where decision support systems (DSS) and continuous glucose monitoring (CGM) tools aid blood glucose management, no dedicated DSS exists for PBH. This leaves individuals vulnerable to recurrent, unpredictable hypoglycemia, posing significant health risks. To address this gap, we propose Glu4, an open-source software package designed to predict and notify users of impending PBH events using CGM data. Methods: Glu4 employs a two-step approach to predict PBH. A run-to-run algorithm forecasts future glucose levels using past CGM data, identifying potential hypoglycemic events 30 min in advance. An intelligent alarm system alerts users when glucose levels are predicted to drop below a critical threshold, prompting preventive action. A pilot study involving three PBH patients collected real-time glucose data to validate the system’s predictive performance. Results: The pilot study demonstrated that Glu4 reliably predicted impending hypoglycemia in all participants, providing timely alerts 30 min before glucose drops. The system showed a high specificity, with no false alarms being triggered during the monitoring period. The proactive notifications enabled participants to manage their glucose levels more effectively by taking preventive actions such as consuming rescue carbohydrates before the onset of severe hypoglycemia. Conclusions: Glu4 represents a promising tool for managing PBH, leveraging CGM data to deliver accurate, timely alerts that enable proactive intervention. By improving safety and quality of life for individuals with PBH, Glu4 addresses a critical unmet need. Future work will focus on enhancing system capabilities and conducting larger-scale studies to validate its effectiveness and refine its usability for clinical adoption.http://www.sciencedirect.com/science/article/pii/S2588914125000036Post bariatric hypoglycaemiaContinuous glucose monitoringDecision support systemPredictionWeb interface |
| spellingShingle | Luca Cossu Francesco Prendin Giacomo Cappon David Herzig Lia Bally Andrea Facchinetti Glu4: An open-source package for real-time forecasting and alerting post-bariatric hypoglycemia based on continuous glucose monitoring Clinical eHealth Post bariatric hypoglycaemia Continuous glucose monitoring Decision support system Prediction Web interface |
| title | Glu4: An open-source package for real-time forecasting and alerting post-bariatric hypoglycemia based on continuous glucose monitoring |
| title_full | Glu4: An open-source package for real-time forecasting and alerting post-bariatric hypoglycemia based on continuous glucose monitoring |
| title_fullStr | Glu4: An open-source package for real-time forecasting and alerting post-bariatric hypoglycemia based on continuous glucose monitoring |
| title_full_unstemmed | Glu4: An open-source package for real-time forecasting and alerting post-bariatric hypoglycemia based on continuous glucose monitoring |
| title_short | Glu4: An open-source package for real-time forecasting and alerting post-bariatric hypoglycemia based on continuous glucose monitoring |
| title_sort | glu4 an open source package for real time forecasting and alerting post bariatric hypoglycemia based on continuous glucose monitoring |
| topic | Post bariatric hypoglycaemia Continuous glucose monitoring Decision support system Prediction Web interface |
| url | http://www.sciencedirect.com/science/article/pii/S2588914125000036 |
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