CGM Precision and Glycaemic Variability

Diabetes Type 1 Clinical Trial

Official title:

Are Todays Continuous Glucose Monitoring Precise and Can They be Used to Reveal and Reduce Glycaemic Variability?

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT03842683
• Other study ID #: sdcn1802
• Status: Completed
• Start date: June 6, 2016
• Est. completion date: June 20, 2017

Study information

• Verified date: October 2019
• Source: Aalborg University Hospital
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

Use of devices for continuous monitoring of the blood sugar is valuable for people with diabetes to understand their disease and to help prevent low blood sugar. Furthermore, continuous monitoring should be used in drug development to evaluate efficacy and safety. However, the devices have been criticised for being too inaccurate. This investigation sought to reveal the inaccuracies of current devices and to assess the subsequent usability related to the mentioned use cases.

Description:

The following study is an exploratory investigation of continuous glucose monitoring based on data from a completed Novo Nordisk A/S clinical trial. Please refer to ClinicalTrials.gov Identifier: NCT02825251.

Continuous Glucose Monitoring (CGM) provides an interstitial glucose reading every 5 minutes and is thus a powerful and important tool to identify glycaemic variability in people with diabetes. CGM is valuable for people with diabetes to understand their glucose metabolism and it has the potential to be used for detection and prediction of glycaemic excursions, such as, the potentially fatal and inevitable events of hypoglycaemia, or even as a component in the holy grail of diabetes technology; the artificial pancreas.

However, CGM has been criticised for being inaccurate and unreliable, amongst others, due to the physiological and a device-related delay between plasma glucose (PG) and interstitial glucose (IG). Nevertheless, CGM keeps on being popular and in February 2017 an international consensus was established at the Advanced Technologies & Treatments for Diabetes (ATTD) congress that even considers CGM data as a valuable and meaningful end point to be used in clinical trials of new drugs and devices for diabetes treatment where accuracy is of high importance.

The above mentioned use cases entail that the CGM data are accurate. Therefore, the first part of this research proposal is to investigate whether the newest state-of-the-art CGM devices used in Novo Nordisk trials are in fact accurate. Based on these results, it is investigated to which degree glycaemic variability can be revealed.

To investigate the accuracy of CGM, mean absolute relative difference (MARD) will be calculated and presented and the impact of the delay assessed by time shifting CGM measurements. Furthermore, correlation analyses, between for example, PG and first derivative of IG, will be performed to try to understand when CGM devices tend to measure inaccurate. Lastly, machine learning and/or deep learning approaches will be utilised to reveal glycaemic patterns and to detect/predict outcomes, such as, hypoglycaemia.

Different glycaemic variability investigations will be undertaken:

• Test of PG vs IG and effect on clinical research. [analysis of differences]

• Correlation between PG values at bedtime and nocturnal hypoglycaemic events [correlation analyses]

• Effect of main evening meal and meal-time dose on nocturnal hypoglycaemic events [correlation analyses]

• Prediction of PG-confirmed hypoglycaemic events with CGM, dose and meal data as input [machine learning]

• The optimal dose and meal distribution and least CGM variability / eHbA1c [machine learning]

• Algorithm to suggest optimal dosing in relation to glycaemic variability [machine learning]

Requested data are demographic, CGM, meal, dose and hypoglycaemia data from the following trial. The analyses are independent of treatment and therefore the treatment arm can be blinded.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 472
• Est. completion date: June 20, 2017
• Est. primary completion date: June 20, 2017
• Gender: All
• Age group: 18 Years and older

Eligibility

As copied from the original clinical trial ClinicalTrials.gov Identifier: NCT02825251

Inclusion Criteria:

• Male or female, age at least 18 years at the time of signing the informed consent

• Diagnosed with T1DM (Type 1 Diabetes Mellitus) (based on clinical judgement and/or supported by laboratory analysis as per local guidelines) equal or above 1 year prior to the day of screening

• Using the same Medtronic pump (Minimed 530G (551/751), Paradigm Veo (554/754), Paradigm Revel (523/723), Paradigm (522/722)) for CSII in a basal-bolus regimen with a rapid acting insulin analogue for at least six months prior to screening and willing to stay on the same pump model throughout the trial (if the model is changed the change should not exceed 7 consecutive days.)

• HbA1c (glycosylated haemoglobin) 7.0-9.0% (53-75 mmol/mol) as assessed by central laboratory at screening

• Body mass index (BMI) below or equal to 35.0 kg/m^2 at screening

• Ability and willingness to take at least 3 daily meal-time insulin bolus infusions every day throughout the trial

Exclusion Criteria:

• Any of the following: myocardial infarction, stroke, hospitalization for unstable angina or transient ischaemic attack within the past 180 days prior to the day of screening

• Planned coronary, carotid or peripheral artery revascularisation known on the day of screening

• History of hospitalization for ketoacidosis below or equal to 180 days prior to the day of screening

• Treatment with any medication for the indication of diabetes or obesity other than stated in the inclusion criteria in a period of 90 days before screening

• Any condition which, in the opinion of the Investigator, might jeopardise a Subject's safety or compliance with the protocol

Related Conditions & MeSH terms

• Diabetes Mellitus, Type 1
• Diabetes Type 1

Intervention

Other:
• CGM
This study seeks to assess CGM accuracy and develop prediction models for hypoglycemia detection and no intervention is therefore applied.

Locations

Country	Name	City	State
n/a

Sponsors (1)

Lead Sponsor	Collaborator
Peter Vestergaard

References & Publications (7)

Danne T, Nimri R, Battelino T, Bergenstal RM, Close KL, DeVries JH, Garg S, Heinemann L, Hirsch I, Amiel SA, Beck R, Bosi E, Buckingham B, Cobelli C, Dassau E, Doyle FJ 3rd, Heller S, Hovorka R, Jia W, Jones T, Kordonouri O, Kovatchev B, Kowalski A, Laffel L, Maahs D, Murphy HR, Nørgaard K, Parkin CG, Renard E, Saboo B, Scharf M, Tamborlane WV, Weinzimer SA, Phillip M. International Consensus on Use of Continuous Glucose Monitoring. Diabetes Care. 2017 Dec;40(12):1631-1640. doi: 10.2337/dc17-1600. Review. — View Citation

El-Khatib FH, Balliro C, Hillard MA, Magyar KL, Ekhlaspour L, Sinha M, Mondesir D, Esmaeili A, Hartigan C, Thompson MJ, Malkani S, Lock JP, Harlan DM, Clinton P, Frank E, Wilson DM, DeSalvo D, Norlander L, Ly T, Buckingham BA, Diner J, Dezube M, Young LA, Goley A, Kirkman MS, Buse JB, Zheng H, Selagamsetty RR, Damiano ER, Russell SJ. Home use of a bihormonal bionic pancreas versus insulin pump therapy in adults with type 1 diabetes: a multicentre randomised crossover trial. Lancet. 2017 Jan 28;389(10067):369-380. doi: 10.1016/S0140-6736(16)32567-3. Epub 2016 Dec 20. Erratum in: Lancet. 2017 Jan 28;389(10067):368. Lancet. 2017 Feb 4;389(10068):e2. — View Citation

Jensen MH, Christensen TF, Tarnow L, Mahmoudi Z, Johansen MD, Hejlesen OK. Professional continuous glucose monitoring in subjects with type 1 diabetes: retrospective hypoglycemia detection. J Diabetes Sci Technol. 2013 Jan 1;7(1):135-43. — View Citation

Jensen MH, Christensen TF, Tarnow L, Seto E, Dencker Johansen M, Hejlesen OK. Real-time hypoglycemia detection from continuous glucose monitoring data of subjects with type 1 diabetes. Diabetes Technol Ther. 2013 Jul;15(7):538-43. doi: 10.1089/dia.2013.0069. Epub 2013 Apr 30. — View Citation

Kovatchev BP, Patek SD, Ortiz EA, Breton MD. Assessing sensor accuracy for non-adjunct use of continuous glucose monitoring. Diabetes Technol Ther. 2015 Mar;17(3):177-86. doi: 10.1089/dia.2014.0272. Epub 2014 Dec 1. — View Citation

Rebrin K, Sheppard NF Jr, Steil GM. Use of subcutaneous interstitial fluid glucose to estimate blood glucose: revisiting delay and sensor offset. J Diabetes Sci Technol. 2010 Sep 1;4(5):1087-98. — View Citation

Rodbard D. Continuous Glucose Monitoring: A Review of Recent Studies Demonstrating Improved Glycemic Outcomes. Diabetes Technol Ther. 2017 Jun;19(S3):S25-S37. doi: 10.1089/dia.2017.0035. Review. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Optimal Time Shift of Continuous Glucose Monitoring Measurements	Continuous glucose monitoring (CGM) measurements are delayed compared to blood glucose. The CGM signal is time-shifted -1 minute at a time and the mean absolute difference between CGM and blood glucose measurements are calculated at each step. The lowest mean absolute difference depicts the optimal time shift in minutes. The resultant mean absolute relative difference is provided as outcome.; Publication reference: https://doi.org/10.1177/1932296819848721	16 weeks
Secondary	Area Under the Receiver Operating Characteristics Curve of the Hypoglycemia Prediction	Area under the receiver operating characteristics curve (ROC-AUC) is a measure of the prediction capabilities of a prediction algorithm. Each point of the curve gives a sensitivity and a specificity of the prediction.; Publication reference: https://doi.org/10.1177/1932296819868727	16 weeks