Continuous glucose monitoring (CGM) sensors are portable devices that allow measuring and visualizing the glucose concentration in real time almost continuously for several days and are provided with hypo/hyperglycemic alerts and glucose trend information. CGM sensors have revolutionized Type 1 diabetes (T1D) management, improving glucose control when used adjunctively to self-monitoring blood glucose systems. Furthermore, CGM devices have stimulated the development of applications that were impossible to create without a continuous-time glucose signal, e.g., real-time predictive alerts of hypo/hyperglycemic episodes based on the prediction of future glucose concentration, automatic basal insulin attenuation methods for hypoglycemia prevention, and the artificial pancreas. However, CGM sensors’ lack of accuracy and reliability limited their usability in the clinical practice, calling upon the academic community for the development of suitable signal processing methods to improve CGM performance. The aim of this paper is to review the past and present algorithmic challenges of CGM sensors, to show how they have been tackled by our research group, and to identify the possible future ones.

https://www.mdpi.com/1424-8220/16/12/2093/pdf

Continuous Glucose Monitoring Sensors: Past, Present and Future Algorithmic Challenges.

Medical Devices, Smart Drug Delivery, Wearables and Technology for the treatment of Diabetes Mellitus.

Monitoring blood glucose levels for diabetic patients is critical to achieve tight glycaemic control. As none of the current antidiabetic treatments restore lost functional β-cell mass in diabetic patients, insulin injections and the use of insulin pumps are most widely used in the management of glycaemia. The use of advanced and intelligent chemical engineering, together with the incorporation of micro- and nanotechnological-based processes have lately revolutionized diabetic management. The start of this concept goes back to 1974 with the description of an electrode that repeatedly measures the level of blood glucose and triggers insulin release from an infusion pump to enter the blood stream from a small reservoir upon need. Next to the insulin pumps, other drug delivery routes, including nasal, transdermal and buccal, are currently investigated. These processes necessitate competences from chemists, engineers-alike and innovative views of pharmacologists and diabetologists. Engineered micro and nanostructures hold a unique potential when it comes to drug delivery applications required for the treatment of diabetic patients. As the technical aspects of chemistry, biology and informatics on medicine are expanding fast, time has come to step back and to evaluate the impact of technology-driven chemistry on diabetics and how the bridges from research laboratories to market products are established. In this review, the large variety of therapeutic approaches proposed in the last five years for diabetic patients are discussed in an applied context. A survey of the state of the art of closed-loop insulin delivery strategies in response to blood glucose level fluctuation is provided together with insights into the emerging key technologies for diagnosis and drug development. Chemical engineering strategies centered on preserving and regenerating functional pancreatic β-cell mass are evoked in addition as they represent a permanent solution for diabetic patients.

The impact of chemical engineering and technological advances on managing diabetes: present and future concepts

Nocturnal hypoglycemia is a common phenomenon among patients with diabetes and can lead to a broad range of adverse events and complications. Identifying factors associated with hypoglycemia can improve glucose control and patient care. We propose a repeated measures random forest (RMRF) algorithm that can handle nonlinear relationships and interactions and the correlated responses from patients evaluated over several nights. Simulation results show that our proposed algorithm captures the informative variable more often than naively assuming independence. RMRF also outperforms standard random forest and extremely randomized trees algorithms. We demonstrate scenarios where RMRF attains greater prediction accuracy than generalized linear models. We apply the RMRF algorithm to analyze a diabetes study with 2524 nights from 127 patients with type 1 diabetes. We find that nocturnal hypoglycemia is associated with HbA1c, bedtime blood glucose (BG), insulin on board, time system activated, exercise intensity, and daytime hypoglycemia. The RMRF can accurately classify nights at high risk of nocturnal hypoglycemia.

Repeated measures random forests (RMRF): Identifying factors associated with nocturnal hypoglycemia.

BACKGROUND
Although insulin pump infusion set failures are common, studies assessing the failure rate are limited.


METHODS
Data were analyzed from two clinical trials in which 263 participants aged 6 to 71 years used 22,741 infusion sets. The frequency of removal due to prolonged hyperglycemia (CGM glucose >300 mg/dL immediately prior to removal and >250 mg/dL continuously for at least 2 hours prior to removal with at least 90 minutes >300 mg/dL out of the prior 120 minutes) was determined. Differences in failure rates among age groups and infusion set types were evaluated.


RESULTS
Among 22,741 infusion sets, 748 (3.3%) were removed prior to 72 hours in association with prolonged hyperglycemia. The percentage replaced within 48 hours and within 24 hours with prolonged hyperglycemia were 1.8% and 1.0%, respectively. Mean duration of continuous time >250 mg/dL prior to removal was 5.1±3.7 hours. Using a less restrictive definition of failure related to hyperglycemia, 1688 (7.4%) sets were removed prior to 72 hours with a glucose level >300 mg/dL at the time of removal. The frequency of insulin set failure with prolonged hyperglycemia was lower in adults ≥18 years old (1.9%) than in those 14-17 years old (5.8%, P<0.001) or 6-13 years old (4.4%, P=0.002). The 90o Teflon sets had the highest frequency of prolonged hyperglycemia failure within 72 hours (4.0%) compared with the angled Teflon set frequency (1.3%, P=0.01) or the steel set frequency (1.9%, P=0.006).


CONCLUSIONS
Based on the data from these 22,741 infusion sets, infusion set changes associated with prolonged hyperglycemia occur on average about 4 times a year, with the frequency being higher in youth than adults. The frequency also appears to be higher with straight Teflon sets compared with angled Teflon sets and steel sets.

Insulin Pump Infusion Set Failures Associated with Prolonged Hyperglycemia: Frequency and Relationship to Age and Type of Infusion Set During 22,741 Infusion Set Wears.

Background:Hypoglycemia varies between patients with type 1 diabetes and is the main obstacle to therapy intensification. We investigated known and potential risk factors for hypoglycemia in subjec...

https://journals.sagepub.com/doi/pdf/10.1177/1932296815586014

Predictors of Hypoglycemia in the ASPIRE In-Home Study and Effects of Automatic Suspension of Insulin Delivery

Objective: Most insulin infusion sets (ISs) are labeled for 2- or 3-days use. The Medtronic EWIS is designed to increase wear time up to 7 days. This study reports on EWIS survival and performance during the pivotal trial (ClinicalTrials.gov: NCT04113694). Method: This multi-center (15), single-arm, non-randomized, non-inferiority trial in 259 participants (18-80yrs) with T1D using the MiniMed™ 670G system (with Humalog™ or Novolog™ insulin) involved wearing a 2- or 3-day IS for 2 weeks (run-in) followed by 12 consecutive EWIS wears (study phase). The latter was worn for ≥174 hours or until set failure. Safety and performance (i.e., a priori target of Result: The EWIS survival rate was 74.8% versus the 67.7% rate of 2- or 3-day ISs (p Conclusion: The 7-day EWIS was safe and its performance was superior at ≥174 hours compared to the performance observed for 2- or 3-day infusion sets. Disclosure B. A. Buckingham: Advisory Panel; Self; Medtronic, Tolerion, Inc., Research Support; Self; Beta Bionics, Inc., Insulet Corporation, Medtronic, Tandem Diabetes Care. B. Horowitz: Research Support; Self; Abbott Laboratories, BD, Celgene, Crinetics Pharmaceuticals, Gan & Lee Pharmaceuticals, Medtronic, Speaker’s Bureau; Self; AstraZeneca, Lilly Diabetes, Merck & Co., Inc., Novo Nordisk Inc. K. B. Kaiserman: Advisory Panel; Self; Medtronic, Consultant; Self; Medtronic, Employee; Self; MannKind Corporation, Research Support; Self; Medtronic, Speaker’s Bureau; Self; Medtronic, Stock/Shareholder; Self; MannKind Corporation. S. Huang: None. V. Chen: Employee; Self; Medtronic. G. Zhang: Employee; Self; Medtronic. S. Chattaraj: Employee; Self; Medtronic. J. Shin: Employee; Self; Medtronic. R. Vigersky: Employee; Self; Medtronic. R. L. Brazg: Research Support; Self; Abbott Diabetes, Ascensia Diabetes Care, Lexicon Pharmaceuticals, Inc., Lilly Diabetes, Medtronic, Pfizer Inc., Roche Diagnostic USA, Senseonics. T. S. Bailey: Consultant; Self; Abbott Diabetes, LifeScan, Novo Nordisk, Sanofi, Research Support; Self; Abbott, Abbott Diabetes, Biolinq, Capillary Biomedical, Inc., Dexcom, Inc., Eli Lilly and Company, Kowa Research Institute, Inc., Lexicon Pharmaceuticals, Inc., Livongo, Medtronic, Medtrum Technologies, Novo Nordisk, REMD Biotherapeutics, Sanofi, Sanvita, Viacyte, Inc., vTv Therapeutics, Zealand Pharma A/S, Speaker’s Bureau; Self; BD, Medtronic, Sanofi. Y. C. Kudva: Research Support; Self; Dexcom, Inc. S. K. Garg: Advisory Panel; Self; Eli Lilly and Company, Medtronic, Novo Nordisk Inc., Zealand Pharma A/S, Research Support; Self; Dexcom, Inc., Eli Lilly and Company, Medtronic. G. Grunberger: Research Support; Self; Medtronic, Speaker’s Bureau; Self; Abbott Diabetes, Eli Lilly and Company, Novo Nordisk. J. Thrasher: Advisory Panel; Self; Medtronic, Board Member; Self; Medtronic, Consultant; Self; Medtronic, Research Support; Self; Eli Lilly and Company, Lexicon Pharmaceuticals, Inc., Medtronic, Mylan N. V., Novo Nordisk, Sanofi, Speaker’s Bureau; Self; AstraZeneca, Janssen Pharmaceuticals, Inc., Medtronic, Sanofi. A. Cavale: Research Support; Self; Eli Lilly and Company, Medtronic. A. Bhargava: Research Support; Self; Abbott, AbbVie Inc., Amgen Inc., Boehringer Ingelheim Pharmaceuticals, Inc., Boston Therapeutics, Inc., Covance Inc., Dexcom, Inc., Eli Lilly and Company, Gan & Lee Pharmaceuticals, Insulet Corporation, Janssen Research & Development, LLC, Kowa Pharmaceuticals America, Inc., Madrigal Pharmaceuticals, Inc., Medtronic, Merck & Co., Inc., Mylan N. V., Novo Nordisk, Poxel SA, Quintiles, Sanofi, Senseonics, Tolerion, Inc., Viking Therapeutics.

100-LB: Infusion Set Survival and Performance during the Medtronic Extended-Wear Infusion Set (EWIS) Pivotal Trial

Background:SAR342434 (U100; SAR-Lis; insulin lispro) is a biosimilar/follow-on to insulin lispro (U100; Ly-Lis). Similar pharmacokinetics/pharmacodynamics between the two products has been demonstr...

https://journals.sagepub.com/doi/pdf/10.1177/1932296817753644

Safety of Insulin Lispro and a Biosimilar Insulin Lispro When Administered Through an Insulin Pump.

Background: The aim was to assess the safety and tolerability of the insulin aspart biosimilar/follow-on product SAR341402 (100 U/mL solution; SAR-Asp) and originator insulin aspart (100 U/mL; NN-A...

Safety and Tolerability of Insulin Aspart Biosimilar SAR341402 Versus Originator Insulin Aspart (NovoLog) When Used in Insulin Pumps in Adults with Type 1 Diabetes: A Randomized, Open-Label Clinical Trial.

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Predictors of Hypoglycemia in the ASPIRE In-Home Study and Effects of Automatic Suspension of Insulin Delivery

100-LB: Infusion Set Survival and Performance during the Medtronic Extended-Wear Infusion Set (EWIS) Pivotal Trial

Safety of Insulin Lispro and a Biosimilar Insulin Lispro When Administered Through an Insulin Pump.

Safety and Tolerability of Insulin Aspart Biosimilar SAR341402 Versus Originator Insulin Aspart (NovoLog) When Used in Insulin Pumps in Adults with Type 1 Diabetes: A Randomized, Open-Label Clinical Trial.