Regulation Catches Up to Reality: Nonadjunctive Use of Continuous Glucose Monitoring Data

Current state of type 1 diabetes treatment in the U.S.: updated data from the T1D Exchange clinic registry

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The UVA/PADOVA Type 1 Diabetes Simulator: New Features

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<i>In Silico</i> Preclinical Trials: A Proof of Concept in Closed-Loop Control of Type 1 Diabetes

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The T1D exchange clinic registry

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Glycemic Variability Is Higher in Type 1 Diabetes Patients with Microvascular Complications Irrespective of Glycemic Control

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Recommendations for Using Real-Time Continuous Glucose Monitoring (rtCGM) Data for Insulin Adjustments in Type 1 Diabetes

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In silico assessment of biomedical products: The conundrum of rare but not so rare events in two case studies

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Improving Efficacy of Inhaled Technosphere Insulin (Afrezza) by Postmeal Dosing: In-silico Clinical Trial with the University of Virginia/Padova Type 1 Diabetes Simulator

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Continuous glucose monitoring: a review of recent studies demonstrating improved glycemic outcomes

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Wearable Continuous Glucose Monitoring Sensors: A Revolution in Diabetes Treatment

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ISPAD Clinical Practice Consensus Guidelines 2018: diabetes technologies

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Continuous glucose monitoring sensors: past, present and future algorithmic challenges

Facchinetti, A. (2016) · Sensors · doi:10.3390/s16122093 · Cited by 176 · see citing works

Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors

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Technology in the management of type 1 diabetes mellitus — current status and future prospects

Tauschmann, M., & Hovorka, R. (2018) · Nature Reviews Endocrinology · doi:10.1038/s41574-018-0044-y · Cited by 161 · see citing works

Calibration of Minimally Invasive Continuous Glucose Monitoring Sensors: State-of-The-Art and Current Perspectives

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A review of implantable biosensors for closed-loop glucose control and other drug delivery applications

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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

Advances in Continuous Glucose Monitoring and Integrated Devices for Management of Diabetes with Insulin-Based Therapy: Improvement in Glycemic Control

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Personalized blood glucose prediction: A hybrid approach using grammatical evolution and physiological models

Contreras, I., Oviedo, S., Vettoretti, M., VISENTIN, R., & Vehı́, J. (2017) · PLoS ONE · doi:10.1371/journal.pone.0187754 · Cited by 80 · see citing works

In silico assessment of biomedical products: The conundrum of rare but not so rare events in two case studies

Viceconti, M., Cobelli, C., Haddad, T. D., Himes, A. K., Kovatchev, B. P., & Palmer, M. (2017) · Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine · doi:10.1177/0954411917702931 · Cited by 63 · see citing works

A Neural-network-based approach to personalize insulin bolus calculation using continuous glucose monitoring

Cappon, G., Vettoretti, M., Marturano, F., Facchinetti, A., & Sparacino, G. (2018) · Journal of Diabetes Science and Technology · doi:10.1177/1932296818759558 · Cited by 62 · see citing works

Machine-Learning Based Model to Improve Insulin Bolus Calculation in Type 1 Diabetes Therapy

Noaro, G., Cappon, G., Vettoretti, M., Sparacino, G., Favero, S. D., & Facchinetti, A. (2021) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2020.3004031 · Cited by 57 · see citing works

Combining continuous glucose monitoring and insulin pumps to automatically tune the basal insulin infusion in diabetes therapy: a review

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Practical Considerations on the Use of Continuous Glucose Monitoring in Pediatrics and Older Adults and Nonadjunctive Use

Forlenza, G. P., Argento, N. B., & Laffel, L. M. (2017) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2017.0034 · Cited by 47 · see citing works

A Review of Safety and Hazards Associated With the Artificial Pancreas

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The Padova Type 2 Diabetes Simulator from Triple-Tracer Single-Meal Studies: In Silico Trials Also Possible in Rare but Not-So-Rare Individuals

VISENTIN, R., Cobelli, C., & Man, C. D. (2020) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2020.0110 · Cited by 39 · see citing works

Reduction of Blood Glucose Measurements to Calibrate Subcutaneous Glucose Sensors: A Bayesian Multiday Framework

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Glycemic variability of acute stroke patients and clinical outcomes: a continuous glucose monitoring study

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Modeling of Diabetes and Its Clinical Impact

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Effect of BGM Accuracy on the Clinical Performance of CGM: An In-Silico Study

Campos-Náñez, E., & Breton, M. D. (2017) · Journal of Diabetes Science and Technology · doi:10.1177/1932296817710476 · Cited by 24 · see citing works

Modeling the Error of the Medtronic Paradigm Veo Enlite Glucose Sensor

Biagi, L., Ramkissoon, C. M., Facchinetti, A., Leal, Y., & Vehı́, J. (2017) · Sensors · doi:10.3390/s17061361 · Cited by 24 · see citing works

Incorporating Long-Acting Insulin Glargine Into the UVA/Padova Type 1 Diabetes Simulator for<i>In Silico</i>Testing of MDI Therapies

Visentin, R., Schiavon, M., Giegerich, C., Klabunde, T., Man, C. D., & Cobelli, C. (2019) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2019.2897851 · Cited by 22 · see citing works

Nonadjunctive Use of Continuous Glucose Monitors for Insulin Dosing: Is It Safe?

Shapiro, A. R. (2017) · Journal of Diabetes Science and Technology · doi:10.1177/1932296816688303 · Cited by 22 · see citing works

Toward Calibration-Free Continuous Glucose Monitoring Sensors: Bayesian Calibration Approach Applied to Next-Generation Dexcom Technology

Acciaroli, G., Vettoretti, M., Facchinetti, A., & Sparacino, G. (2017) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2017.0297 · Cited by 22 · see citing works

Minimal and Maximal Models to Quantitate Glucose Metabolism: Tools to Measure, to Simulate and to Run in Silico Clinical Trials

Cobelli, C., & Man, C. D. (2021) · Journal of Diabetes Science and Technology · doi:10.1177/19322968211015268 · Cited by 21 · see citing works

Yet Another Glucose Variability Index: Time for a Paradigm Change?

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In Silico Head-to-Head Comparison of Insulin Glargine 300 U/mL and Insulin Degludec 100 U/mL in Type 1 Diabetes

Schiavon, M., VISENTIN, R., Giegerich, C., Sieber, J., Man, C. D., Cobelli, C., & Klabunde, T. (2020) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2020.0027 · Cited by 19 · see citing works

In Silico Assessment of literature insulin bolus calculation methods accounting for glucose rate of change

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A Model of Self-Monitoring Blood Glucose Measurement Error

Vettoretti, M., Facchinetti, A., Sparacino, G., & Cobelli, C. (2017) · Journal of Diabetes Science and Technology · doi:10.1177/1932296817698498 · Cited by 18 · see citing works

Modeling Between-Subject Variability in Subcutaneous Absorption of a Fast-Acting Insulin Analogue by a Nonlinear Mixed Effects Approach

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Federated multimodal AI for precision-equitable diabetes care

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Calibration methods for flow rates down to 5 nL/min and validation methodology

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Anti-biofouling strategies for implantable biosensors of continuous glucose monitoring systems

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A New Index of Insulin Sensitivity from Glucose Sensor and Insulin Pump Data: In Silico and In Vivo Validation in Youths with Type 1 Diabetes

Schiavon, M., Galderisi, A., Basu, A., Kudva, Y. C., Cengiz, E., & Dalla Man, C. (2023) · Diabetes Technology &amp; Therapeutics · doi:10.1089/dia.2022.0397 · Cited by 8 · see citing works

Understanding of type 1 diabetes mellitus: what we know and where we go

Cheon, C. K. (2018) · Korean Journal of Pediatrics · doi:10.3345/kjp.2018.06870 · Cited by 8 · see citing works

Diabetes Mellitus

Sperling, M. A., Wolfsdorf, J. I., Menon, R. K., Tamborlane, W. V., Maahs, D. M., Battelino, T., & Phillip, M. (2021) · Elsevier eBooks · doi:10.1016/b978-0-323-62520-3.00021-x · Cited by 9 · see citing works

Tailoring the Padova Type 2 Diabetes Simulator for Treatment Guidance in Target Populations

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In Silico Cloning of Target Type 2 Diabetes Population for Treatments Development and Decision Support

VISENTIN, R., Schiavon, M., & Man, C. D. (2020) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc44109.2020.9175271 · Cited by 6 · see citing works

Methods for Insulin Bolus Adjustment Based on the Continuous Glucose Monitoring Trend Arrows in Type 1 Diabetes: Performance and Safety Assessment in an In Silico Clinical Trial

Noaro, G., Cappon, G., Sparacino, G., Boscari, F., Bruttomesso, D., & Facchinetti, A. (2021) · Journal of Diabetes Science and Technology · doi:10.1177/19322968211043162 · Cited by 6 · see citing works

A Model of Acetaminophen Pharmacokinetics and its Effect on Continuous Glucose Monitoring Sensor Measurements

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Implantable biosensors: Advancements and applications

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How Much Accuracy of Interstitial Glucose Measurement Is Enough? Is There a Need for New Evidence?

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Artificial intelligence-based decision support systems for diabetes

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Retrospective Analysis of Continuous Glucose Monitoring Data With the Surveillance Error Grid Supports Nonadjunctive Dosing Decisions

Welsh, J. B., Walker, T. C., & Price, D. A. (2017) · Journal of Diabetes Science and Technology · doi:10.1177/1932296817694180 · Cited by 3 · see citing works

Commentary Regarding Shapiro, “Nonadjunctive Use of Continuous Glucose Monitors for Insulin Dosing: Is It Safe?”

Price, D. A. (2017) · Journal of Diabetes Science and Technology · doi:10.1177/1932296817695341 · Cited by 3 · see citing works

Optimal Insulin Bolus Dosing in Type 1 Diabetes Management: Neural Network Approach Exploiting CGM Sensor Information

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Free-living use of artificial pancreas for children with type 1 diabetes: systematic review

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Modeling the SMBG measurement error

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Clinical Studies on Efficacy of Continuous Glucose Monitoring

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Commentary Regarding Shapiro, “Nonadjunctive Use of Continuous Glucose Monitors for Insulin Dosing: Is It Safe?”

Price, D. A. (2017) · Journal of Diabetes Science and Technology · doi:10.1177/1932296817695341 · Cited by 3 · see citing works

Nonadjunctive Use of Continuous Glucose Monitors for Insulin Dosing: Is It Safe?

Shapiro, A. R. (2017) · Journal of Diabetes Science and Technology · doi:10.1177/1932296816688303 · Cited by 22 · see citing works

FDA Approval of Nonadjunctive Use of Continuous Glucose Monitors for Insulin Dosing

Shapiro, A. R. (2017) · JAMA · doi:10.1001/jama.2017.12075 · Cited by 26 · see citing works

Dexcom G5 Mobile CGM System Approved by FDA

Staff, A. (2015) · The American Journal of Managed Care

Importance of FDA-integrated continuous glucose monitors to ensure accuracy of continuous glucose monitoring

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