Validation Study of a Peri-intervention Diabetes Management Algorithm (DIAPI) for Endoscopy Procedures

Summary

The peri-endoscopy management of diabetes mellitus creates significant challenges for both patients and healthcare professionals. These procedures require fasting and in certain situations, such as prior to a colonoscopy, the diet must be modified the day before the intervention and patients need to take a laxative. These factors put patients at high risk for hyperglycemia and hypoglycemia. Inadequate diabetes control or the continuation of certain medications during this period can be dangerous for the patient and lead to the cancellation of the procedure.

DIAPI is a web application designed to generate orders for optimal and personalized treatment based on each patient's antidiabetic treatment, their glycemic control, their risk of hypoglycemia, and the intervention-related variables. DIAPI's algorithm is established on current evidence-based data when available, and experts' opinions.

Information generated by DIAPI:

* For the patient: Clear instructions regarding their diabetes medication management for the days preceding and the day of the endoscopy.
* For the health care team:

* Clear instructions regarding patient's diabetes medication management for the days preceding and the day of the endoscopy;
* Individualized hyperglycemia protocol;
* Hypoglycemia protocol;
* Guidelines if SGLT2 inhibitors have not been discontinued pre-intervention;
* Suggestion on whether an Endocrinology consultation is needed.

DIAPI aims to simplify the complex task of peri-intervention diabetes management while ensuring patient safety. It is a cost-effective solution that can lead to a reduction of unnecessary Endocrinology consultations, a decrease in nurses' workload, a lessening of the risk of errors and a diminution of endoscopy cancellation.

The validation study is divided into two main phases.

* Phase 1 - Concordance. The investigators will assess the reproducibility of DIAPI orders when two different healthcare workers (an endocrinologist and a nurse) collect data for the same patient. The investigators hypothesize that DIAPI orders are concordant in 80%. Patients in this phase will be subjected to the mainstay management, which is using the treating-physician's recommendations instead of DIAPI's. This group will be the control arm for the non-inferiority study (Phase 2).
* Phase 2 - Non-inferiority study. The investigators hypothesize that DIAPI's orders are not inferior to the recommendations issued by the treating-physician in terms of efficacy and security. Patients in this phase will be subjected DIAPI's orders. This group will be the intervention arm for the non-inferiority study.

Conditions

• Diabetes Mellitus

Interventions

OTHER

Peri-intervention Diabetes Management Algorithm (DIAPI)

Patients and healthcare workers will apply DIAPI's orders.

OTHER

Usual Care

Patients and healthcare workers will apply the mainstay management, which is using the treating-physician's recommendations instead of DIAPI's.

Sponsors & Collaborators

• Centre hospitalier de l'Université de Montréal (CHUM)

  lead OTHER

Principal Investigators

• Jean-Marie Boutin, MD, PhD · Université de Montréal

Study Design

Allocation

NON_RANDOMIZED

Purpose

TREATMENT

Masking

NONE

Model

SEQUENTIAL

Eligibility

Min Age

18 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2023-09-01

Primary Completion

2024-12-01

Completion

2024-12-01

Countries

• Canada

Study Locations