Prediction and Prevention of Nocturnal Hypoglycemia in Persons With Type 1 Diabetes Using Machine Learning Techniques

Prediction and Prevention of Nocturnal Hypoglycemia in Persons With Type 1 Diabetes With Multiple Doses of Insulin Using Machine Learning Techniques.

The objective is to develop a novel system to predict and prevent nocturnal hypoglycemia in type 1 diabetic (T1D) patients, focused in patients with multiple daily injections (MDI) therapy. The general idea is to make use of previous-day information in the moment when patients go to sleep, and then predict if in the next following hours any hypoglycemic event will occur. If the system will have predicted any hypoglycemic event in that moment, it is expected that it will be able to warn the patient to take some action: such as reduce basal insulin dose or to consume a snack before sleep.

10 patients with T1D for more than five years will be included.

It is a longitudinal, prospective, interventional study in which every patient will use intermittently scanned Continuous Glucose Monitoring (isCGM) and a physical activity tracker during 12 weeks. Moreover, during this period, patients will store in a mobile application (Freestyle LibreLink) or in a reader information regarding their diabetes management activities, such as insulin delivery doses and meal consumption.

Study Overview

• Status: Completed
• Conditions: Hypoglycemia; Type1diabetes
• Intervention / Treatment: Device: isCGM (intermittently scanned Continuous Glucose Monitoring); Device: Physical exercise tracker

• Study Type: Interventional
• Enrollment (Actual): 10
• Phase: Not Applicable

Contacts and Locations

Study Locations

• Spain
  • Catalonia
    • Barcelona, Catalonia, Spain, 08036
      • Hospital Clinic de Barcelona

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

Description

Inclusion Criteria:

• Patients > 18 years with Type 1 Diabetes:

  • > 4 hypoglycemia / week (< 70 mg/dl, including day and night), last 2 weeks and / or
  • One severe hypoglycemia during the last year and / or
  • Hypoglycemia unawareness (Clarke Test >3)
• Disease duration > 5 years
• On multiple doses of insulin (MDI) therapy using a rapid acting insulin analogue as prandial insulin (lispro, aspart or glulisine) and any basal analogue as basal insulin.
• A1c 6.5 - 9.5 %
• Able to use an intermittently scanned continuous glucose monitoring (isCGM) system.
• Performing >4 self-monitoring blood glucose (SMBG) per day
• Using carb-counting
• Providing an informed consent
• No CGM user previously (during the last 3 months).

Exclusion Criteria:

• Patients with a previous Diabetic Ketoacidosis (DKA) episode in the previous 6 months.
• Patients with a severe hypoglycemia in the previous 6 months.
• Severe diabetic complications or comorbidities: eye, renal, cardiovascular...from the clinicians point of view.
• Pregnancy and breastfeeding.
• History of drug or alcohol abuse.
• Scheduled surgery during the study period.
• Mental conditions that prevent the subject to understand the nature, purpose and possible consequences of the study.
• Subjects those are unlikely to meet the clinical study protocol, eg uncooperative attitude, inability to return for follow-up visits, or poor probability of completing the study.
• Using an experimental drug or device during the past 30 days.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Other
• Allocation: N/A
• Interventional Model: Single Group Assignment
• Masking: None (Open Label)

Number of Arms

1

Arms and Interventions

Participant Group / Arm

Intervention / Treatment

Experimental: isCGM and Physical exercise tracker; Participants will perform CGM during 12 weeks using an isCGM (intermittently scanned Continuous Glucose Monitoring), Freestyle Libre, (Abbott Diabetes Care, Witney, Oxon, UK). Insulin dose (rapid-acting and long acting), carbohydrates and Self-monitoring blood glucose (SMBG) per day will be recorded by the patient in the reader or in the App (LibreLink, Abbott Diabetes Care, Witney, Oxon, UK). Moreover, participants will be instructed to collect data about moderate or high intensity exercise, illness and other disturbances occurring during the study period at home. Patients will wear a physical exercise tracker (Fitbit Alta HR® wristband (Fitbit, Inc., San Francisco, California, USA)) to track physiological variables such as heart rate, steps, activity level and sleep quality.

Device: isCGM (intermittently scanned Continuous Glucose Monitoring); Data collection; Device: Physical exercise tracker; Data collection

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Sensitivity (SE) and specificity (SP) to predict Level 1 hypoglycemia	Primary outcome will be sensitivity (SE) and specificity (SP) to predict Level 1 hypoglycemia. Level 1: a hypoglycemia alert glucose value between 54-70 mg/dL (3.0-3.9 mmol/L) with or without symptoms. A hypoglycemic event should be defined as follows. Beginning of a CGM event: readings below the threshold for at least 15 min are considered an event. For example, at least 15min, <70 mg/dL (3.9 mmol/L) to define a significant hypoglycemic event. End of a CGM event: readings for 15 min at >70 mg/dL (3.9 mmol/L).	90 days

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Sensitivity (SE) and specificity (SP) to predict Level 2 hypoglycemia	Secondary outcome will be sensitivity (SE) and specificity (SP) to predict Level 2 hypoglycemia1. Level 2: a glucose level of <54mg/dL (3.0 mmol/L) with or without symptoms. A hypoglycemic event should be defined as follows. Beginning of a CGM event: readings below the threshold for at least 15 min are considered an event. For example, at least 15min, <54 mg/dL (3.0 mmol/L) to define a clinically significant hypoglycemic event. End of a CGM event: readings for 15 min at >70 mg/dL (3.9 mmol/L). A second hypoglycemic event outcome of prolonged hypoglycemia is considered when CGM levels are < 54 mg/dL (3.0 mmol/L) for consecutive 120 min or more.	90 days
Predicted HbA1c from the sensor data	Predicted HbA1c from the sensor data	90 days
standard deviation (SD)	Standard deviation (SD)	90 days
Mean glucose	Mean glucose	90 days
Level 3 hypoglycaemia	Number of Severe hypoglycemia Clinical diagnosis: event requiring assistance (level 3)	90 days
Percentage of time in hypoglycemic ranges	Percentage of time in hypoglycemic ranges, mg/dL (mmol/L), %: Clinically significant/very low/immediate action required <54 (<3.0) (level 2); Alert/low/monitor 70-54 (3.9-3.0) (level 1)	90 days
Percentage of time in target range	Percentage of time in target range, mg/dL (mmol/L), %: Default 70-180 (3.9-10.0); Secondary 70-140 (3.9-7.8)	90 days
Percentage of time in hyperglycemic range >180	Percentage of time in hyperglycemic ranges, mg/dL (mmol/L), % Alert/elevated/monitor > 180 (>10)	90 days
Percentage of time in hyperglycemic range >250	Percentage of time in hyperglycemic ranges, mg/dL (mmol/L), % Clinically significant/very elevated > 250 (>13.9)	90 days
Glucose variability LBGI	Low Blood Glucose Index (LBGI)	90 days
Glucose variability HBGI	High Blood Glucose Index (HBGI)	90 days
Number of Level 3: severe hypoglycemia	Number of Level 3: severe hypoglycemia. This denotes cognitive impairment requiring external assistance for recovery but is not defined by a specific glucose value.	90 days

Collaborators and Investigators

• Sponsor: Hospital Clinic of Barcelona
• Collaborators: Universitat de Girona
• Investigators: Principal Investigator: Ignacio Conget, MD, PhD, Hospital Clinic of Barcelona

Study record dates

Study Major Dates

• Study Start (Actual): October 10, 2018
• Primary Completion (Actual): March 1, 2019
• Study Completion (Actual): April 30, 2019

Study Registration Dates

• First Submitted: October 10, 2018
• First Submitted That Met QC Criteria: October 16, 2018
• First Posted (Actual): October 18, 2018

Study Record Updates

• Last Update Posted (Actual): August 7, 2019
• Last Update Submitted That Met QC Criteria: August 6, 2019
• Last Verified: August 1, 2019

More Information

Terms related to this study

• Keywords: machine learning; hypoglycemia; Type 1 diabetes; intermittent continuous glucose monitoring
• Additional Relevant MeSH Terms: Glucose Metabolism Disorders; Metabolic Diseases; Immune System Diseases; Autoimmune Diseases; Endocrine System Diseases; Diabetes Mellitus; Diabetes Mellitus, Type 1; Hypoglycemia
• Other Study ID Numbers: Nocturnal Hypoglycemia

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No