A real-time continuous glucose monitoring-based algorithm to trigger hypotreatments to prevent/mitigate hypoglycemic events
Abstract
Background: The standard treatment for hypoglycemia recommended by the American Diabetes Association (ADA) suggests patients with diabetes to take small amounts of carbohydrates, the so-called hypotreatments (HTs), as soon as blood glucose concentration goes below 70 mg/dL. However, prevention, or at least mitigation, of hypoglycemic events could be achieved by triggering HTs ahead of time thanks to the use of the predictive capabilities of suitable real-time algorithms fed by continuous glucose monitoring (CGM) sensor data. Materials and Methods: The algorithm proposed in this article to trigger HTs for preventing forthcoming hypoglycemic events is based on the computation of the “dynamic risk”, there is a nonlinear function combining current glycemia with its rate-of-change, both provided by CGM. A comparison of performance of the proposed algorithm against the ADA guidelines is made, in silico , on datasets of 100 virtual patients undergoing a single-meal experiment, with induced postmeal hypoglycemia, generated by the UVA/Padova type 1 diabetes simulator. Results: On noise-free CGM data, the proposed algorithm reduces the time spent in hypoglycemia, on median [25th–75th percentiles] from 36 [29–43] to 0 [0–11] min ( P < 0.0001), with a concomitant decrease of the post-treatment rebound (PTR) in glucose concentration, on median [25th–75th percentiles] from 136 [121–148] to 121 [116–127] mg/dL ( P < 0.0001). On noisy CGM data, there is still a reduction of both time spent in hypoglycemia from 41 [28–49] min to 25 [0–41] min ( P < 0.0001) and PTR from 174 [146–189] mg/dL to 137 [123–151] mg/dL ( P < 0.0001). Conclusions: The potentiality of the new algorithm in generating preventive HTs, which can allow significant reduction of hypoglycemia without concomitant increase of hyperglycemia, suggests its further development and test in silico, for example, simulating both insulin pump and multiple-daily-injection therapies.
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If provided sufficiently in advance, predictive alerts allow the patient to take suitable countermeasures, such as preventive hypo-treatments or correction boluses, to mitigate or even prevent critical episodes.
“If provided sufficiently in advance, predictive alerts allow the patient to take suitable countermeasures, e.g., preventive hypo-treatments or correction boluses, to mitigate, or even prevent, such critical episodes [50].”
From Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors (2020) · cites this paper as [50]
Cites
The UVA/PADOVA Type 1 Diabetes Simulator: New Features
Man, C. D., Micheletto, F., Lv, D., Breton, M., Kovatchev, B., & Cobelli, C. (2014) · Journal of Diabetes Science and Technology · doi:10.1177/1932296813514502 · Cited by 791 · see citing works
Continuous Glucose Monitoring: A Review of Successes, Challenges, and Opportunities
Rodbard, D. (2016) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2015.0417 · Cited by 743 · see citing works
6. Glycemic Targets: <i>Standards of Medical Care in Diabetes—2019</i>
American Diabetes Association (2019) · Diabetes Care · doi:10.2337/dc19-s006 · Cited by 655 · see citing works
A review of personalized blood glucose prediction strategies for T1DM patients
Oviedo, S., Vehí, J., Calm, R., & Armengol, J. (2017) · International Journal for Numerical Methods in Biomedical Engineering · doi:10.1002/cnm.2833 · Cited by 279 · see citing works
Symmetrization of the Blood Glucose Measurement Scale and Its Applications
Kovatchev, B. P., Cox, D. J., Gonder-Frederick, L. A., & Clarke, W. L. (1997) · Diabetes Care · doi:10.2337/diacare.20.11.1655 · Cited by 258 · see citing works
Hypoglycemia in Type 1 Diabetes Mellitus
Cryer, P. E. (2010) · Endocrinology and Metabolism Clinics of North America · doi:10.1016/j.ecl.2010.05.003 · Cited by 205 · see citing works
Zecchin, C., Facchinetti, A., Sparacino, G., Nicolao, G. D., & Cobelli, C. (2012) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2012.2188893 · Cited by 181 · see citing works
Self-citationModeling the glucose sensor error
Facchinetti, A., Del Favero, S., Sparacino, G., Castle, J. R., Ward, W. K., & Cobelli, C. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2284023 · Cited by 130 · see citing works
Self-citationLy, T. T., Breton, M. D., Keith-Hynes, P., DeSalvo, D. J., Clinton, P. K., Benassi, K., Mize, B., Cherñavvsky, D. R., Place, J., Wilson, D. M., et al. (2014) · Diabetes Care · doi:10.2337/dc14-0147 · Cited by 123 · see citing works
The Search for an Optimized Treatment of Hypoglycemia
Slama, G. (1990) · Archives of Internal Medicine · doi:10.1001/archinte.150.3.589 · Cited by 120 · see citing works
Vettoretti, M., Facchinetti, A., Sparacino, G., & Cobelli, C. (2017) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2017.2746340 · Cited by 110 · see citing works
Self-citationZecchin, C., Facchinetti, A., Sparacino, G., & Cobelli, C. (2013) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2013.09.016 · Cited by 105 · see citing works
Self-citationAlanine and terbutaline in treatment of hypoglycemia in IDDM.
Wiethop, B. V., & Cryer, P. E. (1993) · Diabetes Care · doi:10.2337/diacare.16.8.1131 · Cited by 99 · see citing works
Breton, M. D., Patek, S. D., Lv, D., Schertz, E. M., Robic, J., Pinnata, J., Kollar, L. L., Barnett, C. L., WAKEMAN, C. A., Oliveri, M. C., et al. (2018) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2018.0079 · Cited by 98 · see citing works
Continuous glucose monitoring: Current use in diabetes management and possible future applications
Vettoretti, M., Cappon, G., Acciaroli, G., Facchinetti, A., & Sparacino, G. (2018) · Journal of Diabetes Science and Technology · doi:10.1177/1932296818774078 · Cited by 96 · see citing works
Self-citationFacchinetti, A., Del Favero, S., Sparacino, G., & Cobelli, C. (2015) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-014-1226-y · Cited by 80 · see citing works
Self-citationContreras, I., Oviedo, S., Vettoretti, M., VISENTIN, R., & Vehı́, J. (2017) · PLoS ONE · doi:10.1371/journal.pone.0187754 · Cited by 80 · see citing works
Self-citationHypoglycemia Prediction with Subject-Specific Recursive Time-Series Models
Eren-Oruklu, M., Çınar, A., & Quinn, L. T. (2010) · Journal of Diabetes Science and Technology · doi:10.1177/193229681000400104 · Cited by 70 · see citing works
Boscari, F., Galasso, S., Facchinetti, A., Marescotti, M. C., Vallone, V., Amato, A. M. L., Avogaro, A., & Bruttomesso, D. (2017) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2017.10.023 · Cited by 64 · see citing works
Self-citationZhao, C., Dassau, E., Jovanovič, L., Zisser, H., Doyle, F. J., & Seborg, D. E. (2012) · Journal of Diabetes Science and Technology · doi:10.1177/193229681200600317 · Cited by 60 · see citing works
Facchinetti, A., Sparacino, G., & Cobelli, C. (2011) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2011.2161083 · Cited by 60 · see citing works
Self-citationMessori, M., Kropff, J., Favero, S. D., Place, J., VISENTIN, R., Calore, R., Toffanin, C., Palma, F. D., Lanzola, G., Farret, A., et al. (2017) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2016.0463 · Cited by 59 · see citing works
Head-to-head comparison of the accuracy of Abbott FreeStyle Libre and Dexcom G5 mobile
Boscari, F., Galasso, S., Acciaroli, G., Facchinetti, A., Marescotti, M. C., Avogaro, A., & Bruttomesso, D. (2018) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2018.01.003 · Cited by 59 · see citing works
Self-citationVettoretti, M., & Facchinetti, A. (2019) · BioMedical Engineering OnLine · doi:10.1186/s12938-019-0658-x · Cited by 52 · see citing works
Self-citationClayton, D., Woo, V. C., & Yale, J. (2013) · Canadian Journal of Diabetes · doi:10.1016/j.jcjd.2013.01.022 · Cited by 50 · see citing works
Model-fusion-based online glucose concentration predictions in people with type 1 diabetes
Yu, X., Turksoy, K., Rashid, M. M., Feng, J., Hobbs, N., Hajizadeh, I., Samadi, S., Sevil, M., Lazaro, C., Maloney, Z., et al. (2017) · Control Engineering Practice · doi:10.1016/j.conengprac.2017.10.013 · Cited by 39 · see citing works
A Dynamic Risk Measure from Continuous Glucose Monitoring Data
Guerra, S., Sparacino, G., Facchinetti, A., Schiavon, M., Man, C. D., & Cobelli, C. (2011) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2011.0006 · Cited by 37 · see citing works
Self-citationA Nonlinear Blind Identification Approach to Modeling of Diabetic Patients
Novara, C., Pour, N. M., Vincent, T. L., & Grassi, G. (2015) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2015.2462734 · Cited by 34 · see citing works
Hypoglycemia Detection and Carbohydrate Suggestion in an Artificial Pancreas
Turksoy, K., Kilkus, J. M., Hajizadeh, I., Samadi, S., Feng, J., Sevil, M., Lazaro, C., Hobbs, N., Littlejohn, E., & Çınar, A. (2016) · Journal of Diabetes Science and Technology · doi:10.1177/1932296816658666 · Cited by 27 · see citing works
Short-term prediction of blood glucose concentration using interval probabilistic models
Efendic, H., Kirchsteiger, H., Freckmann, G., & del Re, L. (2014) · Mediterranean Conference on Control and Automation (MED) · doi:10.1109/med.2014.6961587 · Cited by 13 · see citing works
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Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors
Vettoretti, M., Cappon, G., Facchinetti, A., & Sparacino, G. (2020) · Sensors · doi:10.3390/s20143870 · Cited by 176 · see citing works
Self-citationPrendin, F., Pavan, J., Cappon, G., Favero, S. D., Sparacino, G., & Facchinetti, A. (2023) · Scientific Reports · doi:10.1038/s41598-023-44155-x · Cited by 168 · see citing works
Self-citationPrendin, F., Favero, S. D., Vettoretti, M., Sparacino, G., & Facchinetti, A. (2021) · Sensors · doi:10.3390/s21051647 · Cited by 77 · see citing works
Self-citationCappon, G., Vettoretti, M., Sparacino, G., Favero, S. D., & Facchinetti, A. (2023) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2023.3286856 · Cited by 57 · see citing works
Self-citationT1DiabetesGranada: a longitudinal multi-modal dataset of type 1 diabetes mellitus
Rodriguez-León, C., Avilés-Pérez, M. D., Baños, O., Quesada‐Charneco, M., Lopez-Ibarra, P. J., Villalonga, C., & Muñoz‐Torres, M. (2023) · Scientific Data · doi:10.1038/s41597-023-02737-4 · Cited by 35 · see citing works
Vettoretti, M., Battocchio, C., Sparacino, G., & Facchinetti, A. (2019) · Sensors · doi:10.3390/s19235320 · Cited by 31 · see citing works
Self-citationLinear Model Identification for Personalized Prediction and Control in Diabetes
Faccioli, S., Facchinetti, A., Sparacino, G., Pillonetto, G., & Favero, S. D. (2021) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2021.3101589 · Cited by 26 · see citing works
Self-citationIbrahim, M. A., Ba-Essa, E. M., Ahmed, A., Ahmad, E., Annabi, F. A., Chaabane, H., Tuccinardi, D., Davies, M. J., Domenico, F. D., Eckel, R. H., et al. (2025) · Diabetes/Metabolism Research and Reviews · doi:10.1002/dmrr.70057 · Cited by 15 · see citing works
Camerlingo, N., Vettoretti, M., Favero, S. D., Facchinetti, A., & Sparacino, G. (2020) · Journal of Diabetes Science and Technology · doi:10.1177/1932296820952123 · Cited by 13 · see citing works
Self-citationCamerlingo, N., Siviero, I., Vettoretti, M., Sparacino, G., Favero, S. D., & Facchinetti, A. (2023) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2023.1280233 · Cited by 12 · see citing works
Self-citationPellizzari, E., Prendin, F., Cappon, G., Sparacino, G., & Facchinetti, A. (2023) · Journal of Diabetes Science and Technology · doi:10.1177/19322968231221768 · Cited by 11 · see citing works
Self-citationFaccioli, S., Prendin, F., Facchinetti, A., Sparacino, G., & Favero, S. D. (2022) · Journal of Diabetes Science and Technology · doi:10.1177/19322968221093665 · Cited by 11 · see citing works
Self-citationÇankaya, N., & Aydoğdu, Ö. (2020) · IEEE Access · doi:10.1109/access.2020.3015454 · Cited by 9 · see citing works
Camerlingo, N., Vettoretti, M., Favero, S. D., Facchinetti, A., Choudhary, P., & Sparacino, G. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.106862 · Cited by 7 · see citing works
Self-citationIdi, E., Manzoni, E., Facchinetti, A., Sparacino, G., & Favero, S. D. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3465893 · Cited by 6 · see citing works
Self-citationPavan, J., Noaro, G., Facchinetti, A., Salvagnin, D., Sparacino, G., & Favero, S. D. (2024) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108179 · Cited by 4 · see citing works
Self-citationA New Decision Support System for Type 1 Diabetes Management
Cappon, G., Noaro, G., Camerlingo, N., Cossu, L., Sparacino, G., & Facchinetti, A. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9629797 · Cited by 3 · see citing works
Self-citationVettoretti, M., Drecogna, M., Favero, S. D., Facchinetti, A., & Sparacino, G. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107700 · Cited by 2 · see citing works
Self-citationA Correction Insulin Bolus Delivery Strategy for Decision Support Systems in Type 1 Diabetes
Cappon, G., Pighin, E., Prendin, F., Sparacino, G., & Facchinetti, A. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9630052 · Cited by 2 · see citing works
Self-citationAL-Khafaji, R. D. M., Pusppanathan, J., Moolla, A., & Phang, F. A. (2025) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-031-97578-3_8 · Cited by 1 · see citing works
Roversi, C., Camerlingo, N., Vettoretti, M., Facchinetti, A., Choudhary, P., Sparacino, G., & Favero, S. D. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107943 · Cited by 1 · see citing works
Self-citationStrollo, F., Fresco, A., Grussu, M., Mambro, A., & Gentile, S. (2023) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2023.110509
- Authors
- Camerlingo, N., Vettoretti, M., Del Favero, S., Cappon, G., Sparacino, G., & Facchinetti, A.
- Affiliations
- University of Padua (IT)
- Department of Information Engineering, University of Padova, Padova, Italy.
- Corresponding author
- Facchinetti, Andrea
- Venue
- Diabetes Technology & Therapeutics
- Publisher
- SAGE Publications
- Published
- 2019-10-21
- Volume
- 21
- Issue
- 11
- Pages
- 644-655
- ISSN
- 1520-9156 (ISSN-L)
- 1557-8593
- OpenAlex
- W2963798798
- Cited by
- 22
- References
- 30
- Citations by year
- 2025: 2 · 2024: 2 · 2023: 8 · 2022: 2 · 2021: 4 · 2020: 3 · 2019: 1
- FWCI
- 2.03
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Diabetes Technology & Therapeutics (publishedVersion)
- PubMed (publishedVersion)
- Padua Research Archive (University of Padova) (submittedVersion)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Treatment and Management
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Keywords
- hypoglycemia prevention, continuous glucose monitoring, real-time algorithms, dynamic risk, type 1 diabetes, postprandial hypoglycemia, UVA/Padova simulator, sensor noise robustness, insulin pump, multiple daily insulin injections
- MeSH terms
- Algorithms, Blood Glucose, Computer Simulation, Diabetes Mellitus, Type 1, Dietary Carbohydrates, Humans, Hypoglycemia, Hypoglycemic Agents, Insulin, Retrospective Studies, Blood Glucose Self-Monitoring, Reproducibility of Results, Biosensing Techniques, Postprandial Period
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