Clinical Trial Details
— Status: Completed
Administrative data
| NCT number |
NCT05181917 |
| Other study ID # |
2021.004 |
| Secondary ID |
|
| Status |
Completed |
| Phase |
N/A
|
| First received |
|
| Last updated |
|
| Start date |
May 1, 2023 |
| Est. completion date |
November 30, 2023 |
Study information
| Verified date |
June 2024 |
| Source |
Hospital Pablo Tobón Uribe |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Interventional
|
Clinical Trial Summary
Insulin remains the only approved treatment for type 1 diabetes mellitus patients and is used
by many with type 2 diabetes. Carbohydrate counting is the most recommended way to prescribe
prandial insulin dose because it is safe and efficacious, and also it allows a more variate
diet to patients. Methods to improve carbohydrate counting include automatization of the
process, optimizing carbohydrate meal content estimation, and including other nutrients such
as fat into the equation. Being an iterative process that patients perfect by practicing and
repeating, we believe that using simulations can improve carbohydrate counting. Simulations
allow individuals to practice in a safe environment and help build confidence in one's
ability to perform a task. In this clinical trial, patients assigned to the intervention
group will have installed the STUDIA app, an automatic carbohydrate counter coupled to a
mathematical model that simulates glucose excursions at the individual level using the
patients' parameters in their smartphone. Time in range will be measured using a continuous
glucose monitor.
Description:
Study setting Patients with T1DM trained in carbohydrate counting whose follow-up is
considered feasible will be recruited from university hospitals and clinics dedicated to
treating people with DM in the city of Medellín, Colombia.
Cointerventions Since only patients with T1DM will be recruited, patients in both groups are
expected to be treated with insulin, either with a multiple injection scheme or an insulin
pump. There are no other approved interventions for the treatment of T1DM. Researchers will
be instructed to avoid the use of unapproved drugs for T1DM.
Protocol discontinuation
The protocol will be discontinued if any of the following conditions are met:
The patient does not wish to continue participating in the study. The patient's treating
physician decides that she should not continue in the study due to the need for additional
therapies that could affect the patient's ability to interact with the application or
substantially modify glucose behavior and require other interventions, such as treatment with
high doses of glucocorticoids.
The patient requires hospital treatment for more than 72 hours, and the use of the
application at the researcher's discretion may interfere with her care or put her health at
risk.
The patient develops a condition that contraindicates continuous glucose monitoring, such as
intractable allergies to adhesives, frequent radiological evaluations that could
contraindicate the implantation of devices, or any generalized skin condition that makes it
impossible to insert the device in any area of the body.
Sample size The sample size has been calculated based on the primary outcome, i.e., the
expected difference in the TIR.
Twenty-eight patients, 14 per group, will be required to analyze time in range using a
mixed-effects model assuming a standard deviation of 4%, the statistical power of 90%, a type
I error rate of 0.05%, and intra-subject variability of 5%, and inter-subject variability of
9%. The sample size was adjusted to 15 patients per group to admit a withdrawal rate of 10%
in the intervention group, assuming that no patients in the control group could access the
intervention. To maintain statistical power, sample size was adjusted by a factor that
represents the retirement ratio.
Recruitment Participants will be recruited from University Hospitals and clinics specializing
in the treatment of T1DM, to which patients are referred for treatment with an insulin pump
or CGM support in Medellín, Colombia. Participants will be invited to the study by their
treating physicians at each site.
Random sequence generation Participants will be randomly assigned in a 1: 1 ratio to the
intervention or control group by a sequence of computer-generated random numbers, using
permuted blocks of 4 to 6 individuals of variable size. The size of the blocks will not be
revealed to ensure the concealment of the sequence. The sequence will be generated using a
web application.
Allocation concealment mechanism Allocation concealment will be maintained using a web-based
central randomization system. This system will not deliver the allocation code to a
coordinator until after the last evaluation visit, preventing the researcher from knowing the
group in which the patient will be included.
Random sequence implementation During the study, randomization will be implemented by the
Pablo Tobón Uribe hospital research unit. The study coordinator will access the sequence list
and assign the generated code to each patient. Within the application, the codes and the
assignment of each code to one of the arms of the study will be prerecorded. Doing so will
ensure the independence of the researchers and those responsible for data analysis.
Blinding Due to the nature of the intervention, patients and investigators will not be
blinded. Instead, those responsible for data analysis will be blinded until the data analysis
is complete. This will be done by assigning codes to the patients with whom those in charge
of processing the data will identify each data set and build the databases. The assignment
groups will be named Group A and Group B, and the intervention of each group will be known
only to the coordinator in the research unit. The groups will be declassified for
interpretation once the study is finished and the analysis plan is complete.
Data collection methods
The data will be collected using the code assigned to each patient during the generation of
the randomization sequence, and the databases will be built weekly with the following
information:
Time in range and time out of range: The time in range and out of range will be calculated
from the data obtained from the continuous glucose monitor at each weekly visit by one of the
investigators. This person will be trained in retrieving the data from the CGM.
Grade 3 hypoglycemic episodes: At each visit, one of the investigators will inquire and
record the Grade 3 hypoglycemic episodes that the patient has presented.
Glycemic variability: The glycemic variability will be calculated based on the data obtained
from the CGM.
Degree of agreement of the phenomenological-based model: The data to estimate the degree of
agreement in the glucose levels of the phenomenological-based model will be obtained by
comparing the simulation given by the model and the data obtained by the CGM. The glucose
values estimated by the model will be stored on a server for the duration of the study and
will later be entered into a database for analysis.
Actions carried out by the participants: The decision entered by the patient is reported in
the same application that performs the carbohydrate count and stored on a server for later
analysis.
Usability of the application: The participants will fill out the usability scale during the
last visit. This data will be stored on a server and subsequently analyzed.
Figure 2 shows the patient's flowchart.
Retention Given the short follow-up time, no losses are expected. However, all reasonable
efforts will be made to ensure follow-ups. The next visit will be scheduled at each follow-up
visit, and the participants will be reimbursed for transportation costs if required to attend
the visits.
Participants may leave the study at any time and for any reason. In addition, researchers may
withdraw participants if they consider any danger to the participant continuing in the study.
Participants may be withdrawn from the study if the competent authority decides to do so.
Data management All data will be recorded electronically in forms stored on a virtual server
during the study. Access to these forms will be restricted and only authorized by the Pablo
Tobón Uribe Hospital research unit.
The data obtained from the CGM will be stored in its original format on a virtual server once
the patient has been de-identified and can be accessed for data quality verification reasons.
Pablo Tobón Uribe Hospital's research unit will store all data indefinitely, but any
information that could identify the patients will be deleted.
Statistical methods Table 1 shows the initial variables of the groups contained in the table
for the operative definition of variables (Supplementary Table1) and will be presented as
divided by the treatment assignment groups. The normality analysis will be performed using a
Q-Q (quantile-quantile) graph and the Shapiro-Wilk test. According to data distribution,
continuous variables will be presented as mean and standard deviation or medians and
interquartile ranges. Discrete variables will be presented as medians and interquartile
ranges, and qualitative variables will be presented as absolute frequencies and proportions.
Analysis population Complete analysis set: The full analysis set will consist of all
randomized individuals, including follow-up and protocol deviation losses.
Per-protocol analysis set: The per-protocol analysis set will be made up of individuals who
use the CHOC + Sim application at least 80% of the time. This proportion of the use of the
simulation is proposed based on previous results that show that CGM sensor wear time is
associated with greater reductions in glucose levels.
Objectives analysis plan A complete analysis set will be used for the primary endpoint and
intention-to-treat analysis. This will be compared with the analysis result as a sensitivity
analysis, taking into account only the set of per-protocol analyses. A mixed model analysis
will be used to evaluate the time in range difference. The TIR is included as the dependent
variable in the mixed-effects model analysis. The type of treatment and the time since
randomization are included as independent variables. The TIR before randomization, type of
insulin treatment, and pre-randomization HbA1c will be included as covariates.
The secondary counting outcomes will be analyzed in the per-protocol set. The number of
episodes of Grade 3 hypoglycemia and number of DKA or HSS will be compared in both groups
using a ratio of incidence rates, calculated using a Poisson regression if the variance is
less than the mean or by negative binomial regression otherwise (60, 61).
Agreement between the phenomenological-based model and CGM measurements will be analyzed
using limits of agreement (LoA) in the complete data set. However, the clinical
interpretation of LoA is not straightforward without a minimal clinically significant
difference. More importantly, it does not consider the clinical risk derived from an error in
the estimation. Therefore, the authors also decided to perform an error grid analysis.
